research studies on total quality management

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Total Quality Management: Practices to Leverage Its Principles in Distance Higher Education

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• Márcia Helena Borges Notarjacomo 12 ,
• Bruna Strapazzon Do Couto 12 ,
• Fernanda Bica de Almeida 12 ,
• Miriam Borchart 12 &
• Giancarlo Medeiros Pereira 12  

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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• International Scientific-Technical Conference MANUFACTURING

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The growth of distance learning in higher education has been gaining market share. The COVID-19 pandemic has reinforced this trend. Enrollments are increasing sharply while fierce competition is a threat to distance learning institutions. To conquer and retain distance learning students, it is necessary to offer quality services and products, seeking innovation and continuous improvement to meet their needs. However, studies that analyze the application of the principles and practices of TQM (Total Quality Management) in distance higher education are still scarce. From this perspective, this study seeks to identify practices that increase the presence of TQM principles in distance higher education institutions. The qualitative research was carried out with managers from 64 teaching centers of one of the largest distance learning institutions in Brazil. The pillars of TQM considered in this study are leadership, staff, students, technological resources and continuous improvement. This study contributed to the literature by identifying practices to expand the presence of TQM principles in distance higher education adopted by the studied institution and its teaching centers. Management contributions are also presented.

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Total Quality Management Fundamentals and Evolving Outcomes in Higher Education Institutions

Total Quality Management in Educational Institutions

Quality in Distance Learning Courses: A Longitudinal Survey of Teacher Training in Federal Programs

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Borges Notarjacomo, M.H., Strapazzon Do Couto, B., Bica de Almeida, F., Borchart, M., Medeiros Pereira, G. (2022). Total Quality Management: Practices to Leverage Its Principles in Distance Higher Education. In: Hamrol, A., Grabowska, M., Maletič, D. (eds) Advances in Manufacturing III. MANUFACTURING 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-00218-2_6

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Please note you do not have access to teaching notes, the key factors of total quality management in the service sector: a cross-cultural study.

Benchmarking: An International Journal

ISSN : 1463-5771

Article publication date: 23 January 2019

Issue publication date: 28 March 2019

Literature refers to the key factors of total quality management (TQM) based on studies carried out in individual countries. However, few studies focus on studying the TQM factors in service companies based on multinational data. The purpose of this paper is to empirically identify the key TQM factors and their impact on internal and external customer performance measures across different countries.

Design/methodology/approach

The research questions regarding the TQM factors and their effects were examined using a sample of service organisations from three countries (131 from Greece, 70 from Mexico and 151 from Spain). TQM factors and their impact on employee and customer satisfaction were analysed separately for each country. Exploratory factor analyses, coupled with multiple linear regression analyses, were conducted.

The key TQM factors identified are common among the three participating countries and can be summarised as follows: quality practices of top management, process management, employee quality management, customer focus, and employee knowledge and education. The adoption level of these five key factors of TQM varies across service organisations in different countries. The results also confirmed that some of the TQM elements are antecedents of customer- and employee-focused performance.

Practical implications

Multinational service organisations may use such an instrument to evaluate TQM implementation among worldwide operations and then benchmark their performance. In addition, an understanding of similarities and differences among countries would help managers around the world to address difficulties of TQM implementation related to the country culture.

Originality/value

Previous studies have compared key TQM factors across different countries in manufacturing, but overall, there has been a little attempt in the literature to analyse the adoption of TQM factors among service firms, as well the relationships between quality improvement and performance across different geographical regions.

• Total quality management
• Service operations

Bouranta, N. , Psomas, E. , Suárez-Barraza, M.F. and Jaca, C. (2019), "The key factors of total quality management in the service sector: a cross-cultural study", Benchmarking: An International Journal , Vol. 26 No. 3, pp. 893-921. https://doi.org/10.1108/BIJ-09-2017-0240

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Exploring the impact of total quality management initiatives on construction industry projects in Pakistan

Nimra afzal.

1 School of Management, Air University, Islamabad, Pakistan

Aamer Hanif

Muhammad rafique.

2 Islamabad Business School, Islamabad, Pakistan

Associated Data

All relevant data are within the paper and its Supporting information files.

The impact of total quality management on organizational performance has been studied extensively, however, the impact of total quality management initiatives on project performance is an area of ongoing research. The key objective of this research is to explore the impact of total quality management initiatives on project performance in the construction industry of Pakistan. Data was collected from 326 personnel working at different management levels across some of the leading construction firms operating in Pakistan. Analysis revealed that operational focus, management commitment, and employee involvement were deemed as dominant total quality management factors affecting project performance in the construction industry. Mediation analysis revealed a significant relationship between employee involvement and project performance mediated by management commitment. Research limitations and directions for future research have also been identified.

1. Introduction

In recent decades, using "total quality management" (TQM) initiatives has been a key stratey for achieving consumer satisfaction and for enhancing organizational performance. Total quality management is considered an approach for managing and continuously improving the entire organization to enhance efficiency of a business through active participation of every organizational member. The fundamental concept of total quality management was introduced in 1940 by three gurus; Deming, Juran and Feigenbaum in Japan who used the term "total quality" during the first international conference on quality control held in Tokyo [ 1 ]. In the 1980s and 1990s, the new quality control and management phases finally started which got widely known as total quality management.

The first implementation of TQM concept was in Japan’s general manufacturing and automobiles industry. Therefore, most of the literature addresses the industry’s misleading impression that the total quality management concept cannot be implemented in any industry other than the manufacturing industry. One key element of TQM approach is to accomplish customer satisfaction which is an essential goal for any organization including construction firms also where research studies have been conducted in the past [ 2 ]. Some of the past researches include TQM implementation in the Palestinian construction industry [ 3 ], TQM implementation in the Oman construction industry [ 2 , 4 ], and a study of the association between TQM and project performance in the Malaysian construction industry [ 5 ]. However, the studies mentioned above are limited to country characteristics different from Pakistan which is currently facing economic challenges even though construction related practices are quite similar.

1.1 Research gap

Many research studies have been conducted in Pakistan to analyze the influence of TQM initiatives across different performance dimensions, including firm supply performance [ 6 ], export performance [ 7 ], innovation performance [ 8 ], corporate green performance [ 9 ], organizational performance [ 10 ], business performance [ 11 ] and also in public sector universities [ 12 ]. While TQM has been given limited consideration in local construction industry, the research focus was on specifics like structural failure, on construction firms having ISO certifications already and on using ICT for modeling [ 13 – 15 ]. Hence, there is a research gap in existing TQM literature in the context of project performance in the Pakistani construction industry. With current focus on improvement of national economy by engaging the construction sector in Pakistan, TQM implementation has the potential to improve project performance and deliver quality outcomes which contribute to the uplift of this sector.

1.2 Research objectives

This paper aims to examine the association between TQM and project performance in the context of the construction industry of Pakistan. More specifically, the objectives of this research are as follows:

• To identify and examine the effect of TQM principles on project performance.
• To determine which total quality management principles have significant impact on project performance.
• To examine the mediating role of management commitment between employee involvement and project performance.

The significant contribution of this research is identification of TQM initiatives relevant to the Pakistani construction industry which enable organizational performance and project success.

2. Literature review

TQM is a management approach that emphasizes customer satisfaction and continuous improvement in the organization [ 16 ]. This is enabled by each employee within the firm who must consider the requirements of the person who uses their output. The objectives of total quality management are to develop quality enhancement as a dominant priority of an enterprise and organizational effectiveness improvements [ 17 ]. Arditi et al. [ 18 ] defined quality as meeting the expectations of owners, regulatory agencies, designers and builders in the construction industry. TQM is an efficient system that incorporates quality improvement, quality maintenance and quality development to enable service at an economic level for achieving complete satisfaction of customers and clients [ 19 ].

PMI clearly explains a project as "a temporary endeavor undertaken to create a unique product or service". The projects are distinctive, have specific goals and objectives, and have a clear starting and ending date. The unpredictable and complex nature of the project causes sensitive and serious challenges to project-based firms. In the context of construction projects, success might be measured differently by construction firms depending on their objectives and goals [ 20 ]. Alzahrani & Emsley [ 21 ] implied that what is considered a successful measure on one project might well be considered unsuccessful or failure on another. Therefore, project’s success may be determined differently according to the objectives or criteria set by the construction firms or organizations [ 20 ]. Hence there is no specific framework for project performance measurement in this industry [ 22 ]. It is impossible to establish a specific criteria or standard checklist for measuring project success because of varying characteristics and objectives of projects in terms of location, complexity, uniqueness and size. Project performance indicators include those related to cost, time and quality, also known as the iron triangle; these indicators are commonly accepted to measure construction project success as well [ 23 ].

The synthesis of principles and philosophies of TQM researchers in construction-related studies has yielded seven elements of TQM. These elements are continuous improvement, commitment, customer focus, strategic planning, operation focus, employee involvement, measurement, analysis & knowledge management. Few research studies have been conducted on total quality management in the construction industry, for example, Mir & Pinnington, Jong et al. and Leong et al. [ 5 , 24 , 25 ]. These studies found a positive association between total quality management and project performance. Jong et al. [ 5 ] explored the link between TQM and project performance in Malaysia. It was observed that total quality management (TQM) significantly affects the performance. Moreover, workforce involvement and continuous operational focus were essential elements for the project’s performance. Leong et al. [ 25 ] reported "ISO 9000" certification effectiveness in firms in Malaysia using indicators of project performance. They found that time variance and satisfaction of customers were positively significant with "ISO 9000 certification". No fixed elements of ISO 9000 certification in this research were inspected as indicators of project performance. Mir & Pinnington [ 24 ] examined the link between TQM and project success in UAE. The framework project management perfromance assessment (PMPA) of TQM was implemented and examined against the project’s success and it was found that the variables of PMPA had a positive impact on the success of the project.

2.1 Commitment and project performance

The effectiveness of TQM system mostly relies on top management commitment and their dedication to organization’s goals and objectives [ 26 , 27 ]. Top management or executives act as key drivers of the total quality management (TQM) program because they establish goals, systems and values to achieve customer satisfaction [ 28 ]. Commitment is essential not only for discussing or achieving business goals, strategies and objectives but also for providing motivation and direction to the workforce of an organization [ 29 ]. The successful completion of any work targeted at changing the organizational operations philosophy is robustly connected with upper-level management commitment. Othman et al. [ 30 ] argued that the consistent involvement of top management in quality-related activities would facilitate the changing attitudes of employees toward quality in an organization. According to the evidence mentioned above, we can say that commitment positively influences project performance. Therefore, we propose the following hypothesis.

• H1: Commitment will have a significant positive effect on project performance.

2.2 Employee involvement and project performance

Involvement of employees is about active participation of organizational members in various levels of the decision-making process. Involvement also refers to the sense of commitment and responsibility [ 31 ]. Employees at all levels are a vital asset in an organization without which it would not achieve its goals and objectives [ 32 ]. Amah & Ahiauzu [ 33 ] studied employee involvement and organization’s effectiveness. They found that employee involvement positively influenced the effectiveness of an organization. Bakotić & Rogošić [ 34 ] researched employee involvement as the key element of quality practices. Results showed that employee involvement positively affected the implementation of the system management method, process method, continual improvement, and decision-making method. Hence, we propose the following hypothesis.

• H2: Employee involvement will have a significant effect on project performance.

2.3 Client or customer focus and project performance

TQM is targeted towards a customer-oriented approach. Knowing and understanding the customers and client’s necessities, being responsive to the demands of the client, and additionally, ensuring satisfaction of the customer have led to growth in revenue, profitability, cash flow and market share [ 35 ]. Pambreni et al. [ 36 ] argued that focus on customers was an essential principle for the success of an organization because it was a starting point in any quality initiative. They studied TQM implementation in food companies and found that customer focus had a significant positive effect on organizational performance in the service sector of Spain. This study also suggests that focusing on clients/customers leads to a better understanding of clients’/customers’ requirements, client/customer satisfaction and improved organizational performance. Zou et al. [ 37 ] found that management strategy for customer relationships led to better project performance. Based upon these findings regarding impact of customer satisfaction on project performance, the following hypothesis is suggested.

• H3: Client and/or Customer focus will have a significant effect on project performance.

2.4 Continuous improvement and project performance

TQM is being termed “a journey, not a destination” [ 38 ]. It is about adopting an improvement-centered culture, understanding the customer requirements, and improving the processes to satisfy customers [ 36 ]. Continuous improvement’s fundamental idea is to prevent mistakes and defects from recurring [ 26 ]. Lizarelli et al. [ 39 ] analyzed the association between innovation performance and continuous improvement in the manufacturing industry of Brazil. They found that continuous improvement (CI) had a positive connection with innovation performance. Since CI aims to prevent defects, reduce waste and enhance performance, we propose the following hypothesis.

• H4: Continuous improvement will have a significant effect on project performance.

2.5 Strategic planning and project performance

Planning is a significant element of the success of the project. In any project, better planning increases the chances of uneventful project execution and enables completion in time [ 40 , 41 ]. High-quality planning reduces cost and schedule overruns in engineering and construction organizations [ 42 ]. Some other factors have also been identified in the literature on project management regarding the significance of planning [ 43 ]. These relate to pitfalls of the traditional approach of planning which contains exorbitant control restrictions and reduced opportunities for innovation or creativity eventually leading to project failure. The basic idea behind strategic planning is to try to reduce ambiguity and enhance the chance of success in a project. Although “strategic planning does not guarantee successful project completion; lack of planning most likely leads to project failure” according to PMBOK. Based on the evidence mentioned above, we propose the following hypothesis.

• H5: Strategic planning will have a significant effect on project performance.

2.6 Operation focus and project performance

Operations management uses methods in which all organizational resources are used in a productive and well-organized way to accomplish goals and desired performance [ 44 ]. The focus is on operational activities including proactive and preventive approaches to managing quality [ 45 ]. Activities include a stable schedule of production, reducing variation and distribution of work to enhance product quality during the production stage [ 46 ]. Different researchers, Irfan & Kee, Mehralian et al., Valmohammadi & Roshanzamir [ 47 – 49 ] conducted studies examining the association between operations, process management and performance. These studies found a significant and positive relationship among these factors. Hence, we propose the following hypothesis.

• H6: Operation focus will have a significant effect on project performance.

2.7 Measurement, analysis, knowledge management and project performance

The availability of consistent, high-quality, adequate and timely information ensures performance improvement [ 50 ]. Bouranta et al. [ 46 ] researched the validity and reliability of data and information using measurement and analysis tools to support decisions on quality related issues to improve organizational performance. The collection of data, application of quality tools, analysis procedures and dissemination of useful knowledge enhances the performances of firms [ 47 , 49 ]. Research conducted by Zeng et al. [ 51 ] on impact of soft and hard quality initiatives on innovation performance detected that quality-related data and analysis directly affects project performance. Hence, the following hypothesis is proposed.

• H7: Measurement, analysis, and knowledge management will have a significant effect on project performance.

Based on literature review presented above and ensuing creation of the research hypotheses, the proposed research model is shown in Fig 1 . In this model, clients of the organizations have been generally addressed as customers.

2.8 Mediating role of management commitment

The impace of management commitment on project performance has been discussed previously. Additionally, this research explores the mediating role of top-level management commitment, support and their involvement in quality-related activities to enhance project performance. Lee et al. [ 52 ] researched employees’ attitudes in response to HR development efforts with the moderating role of top management support and found that it moderated the relationship between employees’ attitudes and HRD efforts. Hence, management support for employees’ professional development is an essential factor for the growth and development of an organization because the top-level management members identify and guide the strategic business directions [ 53 ]. Another research conducted by Gözükara et al. [ 54 ] on the linkage between total quality management and culture development in Istanbul with mediating effect of employee empowerment and management commitment found that commitment mediated the relationship but employee empowerment did not. Commitment is observed as starting point in the direction of implementation and performance of total quality management within an organization [ 55 ]. It is impossible to adopt total quality management and achieve desired project performance without the strong support from top management. Moreover, the involvement of employees significantly influences the commitment of employees to an organization [ 56 ]. Employee involvement affects the performance of an organization in two ways. Firstly, it increases employees’ productivity, and secondly, it increases the firm’s capacity to react fast to agile decisions and plans made by top management. Hence, employee involvement is genuinely affected by the top-level management support which is a vital aspect of project’s success [ 57 ]. The corresponding mediation model based upon this discussion is shown in Fig 2 , and subsequently the following hypothesis is proposed.

• H8: Commitment will mediate the relationship between employee involvement and project performance.

3. Methodology

3.1 data collection and sample characteristics.

This study’s participants were employees working in construction firms across major cities of Pakistan. The questionnaire used to collect data is discussed in the next section. Convenience sampling technique was used for data collection. 600 forms were distributed out of which 330 were received back with a response rate of 54.34%. However, 4 were discarded as unusable, leaving 326 responses comprising the final sample size of this study.

With regards to gender distribution, out of the 326 respondents, 92% were males while 8% were female. This was expected as construction industry employs fewer females in Pakistan. Regarding employee job roles and management positions, 32% of respondents were in a lower-level position, 43% respondents were in a middle-level position and 25% of respondents were in a top-level position. About 70% of respondents had a total job experience of up to 5 years while the remaining had experience over 5 years.

3.2 Ethical considerations of research

The researchers gave the autonomy of individual respondents for this research due consideration, and all participants in the survey were voluntary. Confidentiality of participants, informed consent, and voluntary participation was specifically ensured. All participants were informed that their identity and individual response were to be treated as anonymous and would be utilized only for this research.

3.3 Research instrument and measures

The research instrument had statements requiring responses on five-point Likert scale to measure the constructs (1: strongly disagree, 2: disagree, 3: neutral, 4: agree, 5: strongly disagree). A brief operational definition of research variables and corresponding number of scale items for measurement is given in Table 1 . Commitment was measured by using 8 item scale [ 28 , 58 , 59 ]. One sample item of this scale is “Organization’s top management has objectives for quality performance”. Employee involvement was measured by using a 10-item scale [ 28 , 60 ]. One sample item of this scale is “Employees are recognized for superior quality improvement”. C ustomer focus was measured by using 7 item scale [ 58 , 59 ]. One sample item of this scale is “The organization frequently is in close contact with its customers”. Continuous improvement was measured by using 9 item scale [ 58 ]. One sample item of this scale is “The organization has a quality improvement program”. Strategic planning was measured by using 5 item scale [ 61 ]. One sample item of this scale is "Our organization has a comprehensive, structured planning process which regularly sets and reviews short and long-term goals". Operation focus was measured using 7 item scale [ 59 , 61 ]. One sample item of this scale is "Our organization practices daily operation work processes report system". Measurement , analysis & knowledge management was measured using 7 item scale [ 5 , 61 ]. One sample item of this scale is “Our organization implements organizational performance measurement system”. Project performance was measured by using 4 item scale [ 62 ]. One sample item of this scale is “The project was successful in terms of timeliness of project completion”.

Table 2 shows Cronbach Alpha valules for scale reliability, skewness and kurtosis values for checking data normality and VIF values for determining multicollinearity issues in the data.

Table 3 shows the mean and SD values of each variable. It also shows the values of Pearson’s correlation coefficient between each variable of the study.

Note: p< .05*, p< .01**.

The next section is the results where we will report data analysis outcomes and findings.

SPSS software was used to analyze the collected data. Correlation analysis suggested that associations between variables were significant and in anticipated directions thereby providing introductory support for the research hypotheses. Data normality assessment is a prerequisite for regression analysis and numerous other statistical tests because normal data is a basic assumption in parametric testing. Normality test was conducted, and values of skewness and kurtosis (acceptable not more than +3 & not less than -3) are presented in Table 2 . These values show that the normality assumption is met so that we can proceed with multiple regression analysis. Moreover, the VIF values (acceptable VIF < 10) also suggest that there are no issues with multicollinearity in the collected data.

4.1 Multiple regression analysis

Multiple regresson analysis using the stepwise method was performed to determine the impact of independent variables on project performance. The results are presented in Tables ​ Tables4 4 and ​ and5. 5 . The stepwise method adds independent variables one by one in the model to determine impact on the outcome. Table 4 shows that R 2 of model 1 was .242, which means 24.2% variation in project performance was due to independent variable employee involvement. In model 2, R 2 was .270, which means a 27% variation in project performance was due to employee involvement and operation focus. Model 3 shows that the value of R 2 was .280, meaning that 28% variance occurred in project performance due to three independent variables: employee involvement, operation focus, and commitment. After comparison, Model 3 came out to be a better model based upon the higher R 2 value.

Note: Dependent Variable is project performance for all 3 models presented in table ​ table2 2 above. Model 3 is best fit out of 3 models.

a Predictors: (Constant), Commitment.

b Predictors: (Constant), Commitment, Operation focus.

c Predictors: (Constant), Commitment, Operation focus, Employee involvement.

Multiple regression model coefficients are presented in Table 5 . R-square for model 3 was 28%, with an adj R-square was 27.3%. The multiple regression model revealed that employee involvement (β = .159, p< .05), operation focus (β = .142, p < .05) and commitment (β = .300, p< .05) all had a significant positive impact on project performance. Consequently, H1, H2, and H6 were accepted. The results also revealed that H3, H4, H5, and H7 were not supported.

4.2 Mediation analysis

Mediation analysis was performed to assess the mediating role of commitment on the association between employee involvement and project performance. The results provided in Table 6 revealed that the total effect of employee involvement on project performance was significant (β = .686, p < .05). The direct impact of employee involvement on project performance was also significant (β = .345, p < .05). The indirect effect of employee involvement on project performance through commitment was found significant (β = .341, p < .05). This shows that the association amongst employee involvement and project performance is mediated by management commitment. Hence, hypothesis H8 was supported.

5. Discussion

This study finds that commitment, which is one of the total quality management principles has demonstrated a significant positive effect on the performance of a construction project. This shows that the top management commitment has strong potential to affect the performance of a project in the construction industry of Pakistan. This research outcome is consistent with a few studies where it was found that senior management and leaders guiding the quality system and assessing the financial and non-financial activities resulted in better organizational performance, quality performance and safety programs [ 63 , 64 ]. The firm’s success in applying the best quality management methods depends on how seriously top management takes the deployment of a quality environment. If senior management is not committed, it is impossible to apply quality management standards across the firm [ 65 ]. The top leadership uses quality systems to establish organizational standards and enhances staff engagement to achieve better quality goals and success for the firm [ 66 ]. Our study also illustrates the importance of employee involvement as it is significantly associated with project performance in the Pakistan construction industry. Involved and committed employees understand customer needs and make efforts to address them. This finding is consistent with earlier research where employee involvement, encouragement and participation were found to be essential workforce practices for continuous improvement [ 67 , 68 ]. Another fundamental principle of workforce involvement relates to employee performance management, which significantly impacts the project’s performance [ 69 , 70 ]. Employee training, participation, and involvement were found to be essential elements of operational performance [ 71 ]. The success of ongoing improvement is directly correlated with employee engagement and dedication [ 72 , 73 ]. On the other hand, the construction industry relies heavily on its employees during the construction process, and employee involvement is positively correlated to project performance. This demonstrates that employee involvement in the construction industry of Pakistan is essential to influence project performance.

Clear goals and objectives fixed by companies with clearly defined methods will eventually lead teams to improved performance. The current study has verified this assertion, where the key performance index (KPI) was found to be an essential process management element for the project’s success [ 20 ]. Another important element of process management relates to operations which significantly impact the project’s performance [ 5 , 51 ]. Many other studies have confirmed that process and operation management strongly impact overall performance [ 74 ]. Sadikoglu & Olcay [ 71 ] discovered that customer service, inventory and innovation performance all contributed to organizational performance in Turkey’s industrial and service industry. Zeng et al. [ 51 ] explored the impact of management systems on manufacturing performance in 8 different countries and observed that operation/process management was found to impact performance significantly. Regardless of region, process and operation have shown their significance in enhancing performance. The outcomes of these studies highlight the significance of implementing a well-defined process over the project’s entire life cycle. Another important finding of this study is that the linkage between employee involvement and project performance is mediated by management commitment. The current study illustrates the importance of employee involvement as it is significantly associated with project performance in the Pakistan construction industry through the mediating role of management commitment. Employee participation and involvement in organizational functions were found to have a significant effect on senior management commitment. Interestingly, commitment is concerned with employees’ involvement and participation for better organizational performance [ 56 ]. Another study has shown a strong relationship between employee involvement and management commitment via organizational support [ 75 ]. A greater level of employee participation and personal commitment to organizational success is established with the support and dedication of top management [ 76 ].

Other results of this research show that customer focus, measurement, analysis and knowledge management, continuous improvement, and strategic planning have an insignificant impact on project performance in the local construction industry. Client/Customer focus has an insignificant link with project performance. This specifies that fulfilling customer satisfaction and understanding the needs of the clients/customers do not take priority in the construction sector of Pakistan. Similar surprising findings appeared in previous researchs where consumer satisfaction and retention including focus on the client/customer were not considered essential TQM practices for organizational performance [ 77 , 78 ]. Our study also finds that client/customer focus is insignificant in enhancing project performance and it is not surprising because it shows Pakistan’s construction sector dominated by mostly personal and family owned businesses still does not consider the significance of putting customer needs first.

This study also finds that strategic planning has an insignificant effect on the performance of a project in construction firms. This finding is consistent with earlier research where planning was not found to be an essential TQM principle for better quality performance [ 77 ]. Zwikael et al. [ 41 ] examined the association between planning and success with moderating effect of risk and discovered an insignificant relationship between planning, project effectiveness and efficiency. They also discovered the moderating role of risk; high risk increased the planning requirements and low risk increased the effectiveness of the project. Planning is considered a significant factor for project success in project management and strategic management literature. In the construction sector, some practices such as reliance on owners, designers, and contractors, consistently fluctuating project objectives and goals, and engagement of numerous experts make strategic planning more difficult [ 5 ]. While in the context of construction management, planning a project may be difficult, especially for construction projects. This is due to the fact that construction and building projects are notoriously intricate, dynamic, unusual, risky and unpredictable [ 79 – 81 ]. It is hard to forecast the results of construction projects due to these characteristics. During the early phases of a project, construction managers often pay little attention to detailed or strategic planning [ 82 ]. Client-related problems such as uncertainty in client requirements, frequent fluctuations in project scope and a lack of solid work breakdown structures (WBSs) to support clients’ frequent change orders all contribute to this [ 83 , 84 ]. Subsequently, project planning is not found significant to affect the performance of a project in Pakistan’s construction industry.

Measurement, analysis, and knowledge management are very important elements of TQM. This research shows measurement, analysis & knowledge management have an insignificant impact on project performance. This finding is unexpected and inconsistent with few studies. Mehralian et al. [ 47 ] argued availability of quality-related data and usage was considered one of the total quality management elements that significantly influence the performance of Iran’s pharmaceutical industry. Ooi et al. [ 44 ] discovered a negative relationship between information analysis and innovation performance. Similarly, Teh et al. [ 85 ] discovered a negative relationship between information analysis and the automotive industry of ASEAN. Our observation of lack of support for measurement, analysis & knowledge management in enhancing project performance is not surprising because it shows Pakistan’s construction sector does not consider the significance of using data and information in decision-making and does not allocate resources for this essential activity.

It is also found that continuous improvement does not significantly affect the performance of construction projects which is also inconsistent with other studies. Arief et al. [ 86 ] argued a significant impact of continuous improvement on the financial performance of the manufacturing sector. Jørgensen et al. [ 87 ] conducted a study to examine the relationship between CI, HRM and the effect of CI on performance. The authors discovered a significant effect of CI on performance. Generally, these studies showed that increased continuous improvement activities improve the overall organizational performance. Unfortunately, in the current study, CI is insignificant in enhancing project performance because it shows Pakistan’s construction sector still does not consider the significance of continual improvement in products and services.

Commitment, operation focus, and employee involvement were recognized as the most important pillars of TQM in construction projects. Unfortunately, some key principles of TQM, i.e., customer focus, continuous improvement, strategic planning, measurement, analysis and knowledge management were not recognized as the most important pillars in the construction industry of Pakistan. As per the report [ 88 ], the highly sensitive problems and risks in the construction industry of Pakistan are uncertainty in financial management, fluctuation in material prices, natural disaster, delays, and poor designs. All of these risks do not have any connection with the principles of total quality management in the construction industry of Pakistan. Moreover, comparing the findings of this research with other developing countries specifically on project-based organizations [ 5 , 89 ], it became evident that while each country’s culture, characteristics, and other aspects are distinct, these developing nations also emphasized on employee involvement, operations-related activities, and management support as in Pakistan. However, they do not adhere to the culture of focusing on consumer feedback, project planning, measurement, analysis, and ongoing improvement. They have simply not taken these crucial factors into account like developed and industrialized nations.

6. Conclusion

The importance and implementation of TQM specifically in construction industry has emerged as a key factor towards delivering successful projects besides contributing to improved organizational performance and project success. The construction sector is viewed as a significant contributor to the Pakistan economy. This study revealed that operation focus, employee involvement, and management commitment significantly affect project performance in the Pakistan construction industry. Employee involvement also has a significant positive impact on the project’s performance through the mediating role of management commitment. The current TQM principles show that employee involvement is important in the construction industry of Pakistan. Employee involvement is the main component that retains the operations of the construction project, and every project phase depends on employee involvement. Furthermore, operations and process-related activities are significant in this unpredictable, dynamic, and unique industry. Well-defined operations and processes enhance productivity. The participation of top-level managers, supervisors, and owners in quality-related activities and functions enhances the performance of a project. Other TQM principles such as customer focus, strategic planning, continuous improvement and measurement, analysis & knowledge management had insignificant impact on construction project performance and needs further research.

6.1 Practical implication

This study demonstrated the partial effect of TQM on project performance. The outcomes revealed that only three out of seven total quality management elements significantly correlated with construction project performance. Therefore, it is understandable for owners, managers and supervisors that the implementation of total quality management elements (commitment, employee involvement, and operation focus) enhanced project performance. The implication is that construction firms should focus on those TQM practices, including commitment, employee involvement, and operation focus, to improve the performance of construction projects. Construction companies need to invest in resources in order to collect, measure and analyze data related to customer satisfaction and project performance in order to ensure continuous improvement in their services. This study also guides the policy decision makers to encourage construction firms to embark on a total quality management system, enhance their organizational and project performance, and improve the economy.

6.2 Limitations and future directions

The study had few limitations because of time constraints and resources. The first constraint was that the research only concentrated on companies in Pakistan. It is recommended that research should be expanded to different developed and developing countries. Secondly, the research focuses only on the construction industry and in future may target different industries including software, manufacturing and service industry. Another future research direction is to perform qualitative research on TQM factors which were found insignificant in the local industry and determine their role in achieving project success. Moreover, top management commitment could also be tested as a moderator between TQM initiatives and project success.

Supporting information

S1 appendix, funding statement.

The author(s) received no specific funding for this work.

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Total quality management: three case studies from around the world

With organisations to run and big orders to fill, it’s easy to see how some ceos inadvertently sacrifice quality for quantity. by integrating a system of total quality management it’s possible to have both.

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There are few boardrooms in the world whose inhabitants don’t salivate at the thought of engaging in a little aggressive expansion. After all, there’s little room in a contemporary, fast-paced business environment for any firm whose leaders don’t subscribe to ambitions of bigger factories, healthier accounts and stronger turnarounds. Yet too often such tales of excess go hand-in-hand with complaints of a severe drop in quality.

Food and entertainment markets are riddled with cautionary tales, but service sectors such as health and education aren’t immune to the disappointing by-products of unsustainable growth either. As always, the first steps in avoiding a catastrophic forsaking of quality begins with good management.

There are plenty of methods and models geared at managing the quality of a particular company’s goods or services. Yet very few of those models take into consideration the widely held belief that any company is only as strong as its weakest link. With that in mind, management consultant William Deming developed an entirely new set of methods with which to address quality.

Deming, whose managerial work revolutionised the titanic Japanese manufacturing industry, perceived quality management to be more of a philosophy than anything else. Top-to-bottom improvement, he reckoned, required uninterrupted participation of all key employees and stakeholders. Thus, the total quality management (TQM) approach was born.

All in Similar to the Six Sigma improvement process, TQM ensures long-term success by enforcing all-encompassing internal guidelines and process standards to reduce errors. By way of serious, in-depth auditing – as well as some well-orchestrated soul-searching – TQM ensures firms meet stakeholder needs and expectations efficiently and effectively, without forsaking ethical values.

By opting to reframe the way employees think about the company’s goals and processes, TQM allows CEOs to make sure certain things are done right from day one. According to Teresa Whitacre, of international consulting firm ASQ , proper quality management also boosts a company’s profitability.

“Total quality management allows the company to look at their management system as a whole entity — not just an output of the quality department,” she says. “Total quality means the organisation looks at all inputs, human resources, engineering, production, service, distribution, sales, finance, all functions, and their impact on the quality of all products or services of the organisation. TQM can improve a company’s processes and bottom line.”

Embracing the entire process sees companies strive to improve in several core areas, including: customer focus, total employee involvement, process-centred thinking, systematic approaches, good communication and leadership and integrated systems. Yet Whitacre is quick to point out that companies stand to gain very little from TQM unless they’re willing to go all-in.

“Companies need to consider the inputs of each department and determine which inputs relate to its governance system. Then, the company needs to look at the same inputs and determine if those inputs are yielding the desired results,” she says. “For example, ISO 9001 requires management reviews occur at least annually. Aside from minimum standard requirements, the company is free to review what they feel is best for them. While implementing TQM, they can add to their management review the most critical metrics for their business, such as customer complaints, returns, cost of products, and more.”

The customer knows best: AtlantiCare TQM isn’t an easy management strategy to introduce into a business; in fact, many attempts tend to fall flat. More often than not, it’s because firms maintain natural barriers to full involvement. Middle managers, for example, tend to complain their authority is being challenged when boots on the ground are encouraged to speak up in the early stages of TQM. Yet in a culture of constant quality enhancement, the views of any given workforce are invaluable.

AtlantiCare in numbers

5,000 Employees

$280m Profits before quality improvement strategy was implemented

$650m Profits after quality improvement strategy

One firm that’s proven the merit of TQM is New Jersey-based healthcare provider AtlantiCare . Managing 5,000 employees at 25 locations, AtlantiCare is a serious business that’s boasted a respectable turnaround for nearly two decades. Yet in order to increase that margin further still, managers wanted to implement improvements across the board. Because patient satisfaction is the single-most important aspect of the healthcare industry, engaging in a renewed campaign of TQM proved a natural fit. The firm chose to adopt a ‘plan-do-check-act’ cycle, revealing gaps in staff communication – which subsequently meant longer patient waiting times and more complaints. To tackle this, managers explored a sideways method of internal communications. Instead of information trickling down from top-to-bottom, all of the company’s employees were given freedom to provide vital feedback at each and every level.

AtlantiCare decided to ensure all new employees understood this quality culture from the onset. At orientation, staff now receive a crash course in the company’s performance excellence framework – a management system that organises the firm’s processes into five key areas: quality, customer service, people and workplace, growth and financial performance. As employees rise through the ranks, this emphasis on improvement follows, so managers can operate within the company’s tight-loose-tight process management style.

After creating benchmark goals for employees to achieve at all levels – including better engagement at the point of delivery, increasing clinical communication and identifying and prioritising service opportunities – AtlantiCare was able to thrive. The number of repeat customers at the firm tripled, and its market share hit a six-year high. Profits unsurprisingly followed. The firm’s revenues shot up from $280m to $650m after implementing the quality improvement strategies, and the number of patients being serviced dwarfed state numbers.

Hitting the right notes: Santa Cruz Guitar Co For companies further removed from the long-term satisfaction of customers, it’s easier to let quality control slide. Yet there are plenty of ways in which growing manufacturers can pursue both quality and sales volumes simultaneously. Artisan instrument makers the Santa Cruz Guitar Co (SCGC) prove a salient example. Although the California-based company is still a small-scale manufacturing operation, SCGC has grown in recent years from a basement operation to a serious business.

SCGC in numbers

14 Craftsmen employed by SCGC

800 Custom guitars produced each year

Owner Dan Roberts now employs 14 expert craftsmen, who create over 800 custom guitars each year. In order to ensure the continued quality of his instruments, Roberts has created an environment that improves with each sale. To keep things efficient (as TQM must), the shop floor is divided into six workstations in which guitars are partially assembled and then moved to the next station. Each bench is manned by a senior craftsman, and no guitar leaves that builder’s station until he is 100 percent happy with its quality. This product quality is akin to a traditional assembly line; however, unlike a traditional, top-to-bottom factory, Roberts is intimately involved in all phases of instrument construction.

Utilising this doting method of quality management, it’s difficult to see how customers wouldn’t be satisfied with the artists’ work. Yet even if there were issues, Roberts and other senior management also spend much of their days personally answering web queries about the instruments. According to the managers, customers tend to be pleasantly surprised to find the company’s senior leaders are the ones answering their technical questions and concerns. While Roberts has no intentions of taking his manufacturing company to industrial heights, the quality of his instruments and high levels of customer satisfaction speak for themselves; the company currently boasts one lengthy backlog of orders.

A quality education: Ramaiah Institute of Management Studies Although it may appear easier to find success with TQM at a boutique-sized endeavour, the philosophy’s principles hold true in virtually every sector. Educational institutions, for example, have utilised quality management in much the same way – albeit to tackle decidedly different problems.

The global financial crisis hit higher education harder than many might have expected, and nowhere have the odds stacked higher than in India. The nation plays home to one of the world’s fastest-growing markets for business education. Yet over recent years, the relevance of business education in India has come into question. A report by one recruiter recently asserted just one in four Indian MBAs were adequately prepared for the business world.

RIMS in numbers

9% Increase in test scores post total quality management strategy

22% Increase in number of recruiters hiring from the school

20,000 Increase in the salary offered to graduates

50,000 Rise in placement revenue

At the Ramaiah Institute of Management Studies (RIMS) in Bangalore, recruiters and accreditation bodies specifically called into question the quality of students’ educations. Although the relatively small school has always struggled to compete with India’s renowned Xavier Labour Research Institute, the faculty finally began to notice clear hindrances in the success of graduates. The RIMS board decided it was time for a serious reassessment of quality management.

The school nominated Chief Academic Advisor Dr Krishnamurthy to head a volunteer team that would audit, analyse and implement process changes that would improve quality throughout (all in a particularly academic fashion). The team was tasked with looking at three key dimensions: assurance of learning, research and productivity, and quality of placements. Each member underwent extensive training to learn about action plans, quality auditing skills and continuous improvement tools – such as the ‘plan-do-study-act’ cycle.

Once faculty members were trained, the team’s first task was to identify the school’s key stakeholders, processes and their importance at the institute. Unsurprisingly, the most vital processes were identified as student intake, research, knowledge dissemination, outcomes evaluation and recruiter acceptance. From there, Krishnamurthy’s team used a fishbone diagram to help identify potential root causes of the issues plaguing these vital processes. To illustrate just how bad things were at the school, the team selected control groups and administered domain-based knowledge tests.

The deficits were disappointing. A RIMS students’ knowledge base was rated at just 36 percent, while students at Harvard rated 95 percent. Likewise, students’ critical thinking abilities rated nine percent, versus 93 percent at MIT. Worse yet, the mean salaries of graduating students averaged $36,000, versus $150,000 for students from Kellogg. Krishnamurthy’s team had their work cut out.

To tackle these issues, Krishnamurthy created an employability team, developed strategic architecture and designed pilot studies to improve the school’s curriculum and make it more competitive. In order to do so, he needed absolutely every employee and student on board – and there was some resistance at the onset. Yet the educator asserted it didn’t actually take long to convince the school’s stakeholders the changes were extremely beneficial.

“Once students started seeing the results, buy-in became complete and unconditional,” he says. Acceptance was also achieved by maintaining clearer levels of communication with stakeholders. The school actually started to provide shareholders with detailed plans and projections. Then, it proceeded with a variety of new methods, such as incorporating case studies into the curriculum, which increased general test scores by almost 10 percent. Administrators also introduced a mandate saying students must be certified in English by the British Council – increasing scores from 42 percent to 51 percent.

By improving those test scores, the perceived quality of RIMS skyrocketed. The number of top 100 businesses recruiting from the school shot up by 22 percent, while the average salary offers graduates were receiving increased by $20,000. Placement revenue rose by an impressive $50,000, and RIMS has since skyrocketed up domestic and international education tables.

No matter the business, total quality management can and will work. Yet this philosophical take on quality control will only impact firms that are in it for the long haul. Every employee must be in tune with the company’s ideologies and desires to improve, and customer satisfaction must reign supreme.

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Erica McNamee

Satellites continuously peer down from orbit to take measurements of Earth, and this week a group of scientists set sail to verify some of those data points.

On June 2, the SCOAPE (Satellite Coastal and Oceanic Atmospheric Pollution Experiment) research team, in partnership with the U.S. Interior Department’s Bureau of Ocean Energy Management, took to the seas in the Gulf of Mexico for its second campaign to make surface-based measurements of air pollutants.

The primary pollutant scientists are measuring is nitrogen dioxide (NO2), the compound that reacts with sunlight to make ground-level ozone, said Anne Thompson, senior scientist emeritus for atmospheric chemistry at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and senior researcher at the University of Maryland, Baltimore County.

The Gulf of Mexico is highly concentrated with oil and natural gas drilling platforms, which are sources of NO2. By taking measurements of these emissions from the sea surface nearby, scientists can help validate measurements taken from a much different vantage point. The research vessel the scientists are using, Point Sur, is owned by the University of Southern Mississippi and operated by the Louisiana Universities Marine Consortium.

“We’re the eyes on the surface to understand how well the eyes in the sky are working,” said Ryan Stauffer, research scientist for the atmospheric chemistry and dynamics laboratory at Goddard. Stauffer is also the principal investigator for the SCOAPE II project.

For the first iteration of the project in 2019, ship-based measurements were compared to data gathered by the Ozone Monitoring Instrument aboard NASA’s Aura satellite and the Tropospheric Monitoring Instrument aboard ESA’s (European Space Agency) Sentinel-5 Precursor satellite. Both instruments fly on polar orbiting satellites, which pass over every part of the globe once per day. They capture snapshots at the same time each day, but cannot capture the short-lived NO2 emissions that come and go at different times.

In 2024, the research team is working to validate the measurements taken by TEMPO (the Tropospheric Emissions: Monitoring of Pollution instrument), which was launched on a commercial satellite in April 2023. The TEMPO instrument provides a different perspective to the NO2 measurements due to its geostationary orbit — it focuses solely on North America and has a constant view of the Gulf of Mexico region. This allows scientists to better quantify emissions and make comparisons across all daylight hours.

From space, satellites collect measurements of the “total column” of air, which means they measure the concentrations of NO2 from the land or ocean surface all the way up to the top of the atmosphere. With SCOAPE, scientists are taking measurements from the ship, about 33 feet above sea level, to focus measurements on the air that people breathe.

Learning more about how those surface measurements compare to what satellites see in the total column can help scientists figure out how to use satellite data most effectively. Measuring NO2 from space over the past two decades has helped scientists understand how the compound affects air quality, and has helped to inform policies to reduce emissions of the pollutant.

During SCOAPE’s 2019 campaign, researchers detected concentrations of methane – a significant greenhouse gas – near the Gulf Coast. This time around, the scientists are  looking to accurately measure these concentrations from the surface as well. They will mount the NASA Airborne Visible and InfraRed Imaging Spectrometer–3 imaging spectrometer instrument on a Dynamic Aviation B-200 plane to collect methane measurements above the Gulf, which will add an extra layer to understanding emissions of this potent greenhouse gas from Gulf of Mexico oil and gas operations.

It has historically been difficult to measure methane from space , but scientists are working to build those capabilities. As with NO2, taking surface measurements helps scientists better understand the measurements taken from space.

By Erica McNamee

NASA’s Goddard Space Flight Center, Greenbelt, Md.

Related Terms

• Goddard Space Flight Center
• Tropospheric Emissions: Monitoring of Pollution (TEMPO)

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May 5-8, 2024

• Past Conferences

A Systematic Review and Meta-Analysis of Economic Burden of Breast Cancer Among Adult Females: Based on the Perspective of Social Development Level and Progression of Disease

Han L 1 , Wang J 1 , Han X 2 , Maitland E 3 , Nicholas S 4 , Xu Z 2 , Wang D 2 , Zeng K 2 1 Wuhan University, Wuhan, Hubei, China, 2 Wuhan University, Wuhan, China, 3 University of Liverpool, Liverpool, ACT, Australia, 4 University of Newcastle, Newcastle, NSW, Australia

OBJECTIVES: Breast cancer is the most common cancer among women, surpassing the incidence of lung cancer globally and imposing a heavy economic burden on patients and health systems. We systematically reviewed and quantified the proportion of medical expenditures in the total economic costs of breast cancer by social development level and progression of disease; provided policy advice to health system policy-makers.

METHODS: We conducted a literature search on PubMed, EMbase, The Cochrane Library, China National Knowledge Infrastructure and Wanfang Data Knowledge Service Platform from 2016 to 2023. The data indicators were extracted and evaluated independently for quality by two reviewers and disagreement was resolved by a third reviewer. Grouped by the social development level and progression of disease , the proportion of direct medical costs as a proportion of the total cost burden among breast cancer patients was calculated along with the 95% confidence interval.

RESULTS: A total of 15 studies on the economic burden of breast cancer identified, with one article eliminated due to significant risk bias, leaving 14 studies at a low risk level. The pooled proportion of direct medical cost burden of breast cancer in developed countries was 69.1% (95% CI, 52.5%-85.8%), in developing countries was 75.5% (95% CI, 61.2%-89.7%). The pooled medical costs proportion in total costs of metastatic breast cancer patients was 83.7% (95% CI, 62.7%-104.8%), and that of early breast cancer patients was only 49.6% (95% CI, 33.3%-66%).

Economic Evaluation, Real World Data & Information Systems, Study Approaches

Topic Subcategory

Health & Insurance Records Systems, Meta-Analysis & Indirect Comparisons, Work & Home Productivity - Indirect Costs

• Open access
• Published: 20 June 2024

Genome-wide association study considering genotype-by-environment interaction for productive and reproductive traits using whole-genome sequencing in Nellore cattle

• Ivan Carvalho Filho 1   na1 ,
• Leonardo M. Arikawa 1   na1 ,
• Lucio F. M. Mota 1   na1 ,
• Gabriel S. Campos 1 ,
• Larissa F. S. Fonseca 1 ,
• Gerardo A. Fernandes Júnior 1 ,
• Flavio S. Schenkel 2 ,
• Daniela Lourenco 3 ,
• Delvan A. Silva 1 ,
• Caio S. Teixeira 1 ,
• Thales L. Silva 1 ,
• Lucia G. Albuquerque 1 , 4 &
• Roberto Carvalheiro 1  

BMC Genomics volume  25 , Article number:  623 ( 2024 ) Cite this article

178 Accesses

Metrics details

The genotype-by-environment interaction (GxE) in beef cattle can be investigated using reaction norm models to assess environmental sensitivity and, combined with genome-wide association studies (GWAS), to map genomic regions related to animal adaptation. Including genetic markers from whole-genome sequencing in reaction norm (RN) models allows us to identify high-resolution candidate genes across environmental gradients through GWAS. Hence, we performed a GWAS via the RN approach using whole-genome sequencing data, focusing on mapping candidate genes associated with the expression of reproductive and growth traits in Nellore cattle. For this purpose, we used phenotypic data for age at first calving (AFC), scrotal circumference (SC), post-weaning weight gain (PWG), and yearling weight (YW). A total of 20,000 males and 7,159 females genotyped with 770k were imputed to the whole sequence (29 M). After quality control and linkage disequilibrium (LD) pruning, there remained  ∼  2.41 M SNPs for SC, PWG, and YW and ∼  5.06 M SNPs for AFC.

Significant SNPs were identified on Bos taurus autosomes (BTA) 10, 11, 14, 18, 19, 20, 21, 24, 25 and 27 for AFC and on BTA 4, 5 and 8 for SC. For growth traits, significant SNP markers were identified on BTA 3, 5 and 20 for YW and PWG. A total of 56 positional candidate genes were identified for AFC, 9 for SC, 3 for PWG, and 24 for YW. The significant SNPs detected for the reaction norm coefficients in Nellore cattle were found to be associated with growth, adaptative, and reproductive traits. These candidate genes are involved in biological mechanisms related to lipid metabolism, immune response, mitogen-activated protein kinase (MAPK) signaling pathway, and energy and phosphate metabolism.

Conclusions

GWAS results highlighted differences in the physiological processes linked to lipid metabolism, immune response, MAPK signaling pathway, and energy and phosphate metabolism, providing insights into how different environmental conditions interact with specific genes affecting animal adaptation, productivity, and reproductive performance. The shared genomic regions between the intercept and slope are directly implicated in the regulation of growth and reproductive traits in Nellore cattle raised under different environmental conditions.

Peer Review reports

Nellore cattle are raised under different production systems that are predominantly characterized by extensive pastures, with animals being influenced by a wide range of climatic conditions. These environmental variations introduce disparities in forage availability and quality, as well as challenges related to heat stress, among other factors. In this context, differences between the production systems of the selected herds and commercial herds can result in differences in productive performance, which has significant economic implications for the livestock industry [ 1 , 2 ]. These heterogeneous environmental conditions can decrease the accuracy of breeding values when genotype-by-environment (GxE) interactions are not accounted for during genetic evaluation [ 3 , 4 ].

GxE interactions may affect the reranking of animals across different environments [ 5 ] and have been identified as an important source of variation in the productive and reproductive performance of beef cattle [ 3 , 4 , 6 , 7 ]. The evaluation of GxE interactions in beef cattle is routinely performed using reaction norm models [ 8 , 9 ] to predict breeding values under different environmental conditions and to assess environmental sensitivity [ 1 ]. Traditionally, environmental gradients (EG) used to evaluate GxE interactions have been derived from contemporary group (CG) solutions based on phenotypic information [ 3 , 7 , 10 ]. This is because the CG encompasses the differences in nutritional and climatic factors, as well as the management in which the animals were raised over a determined period, representing a key factor in phenotypic variability [ 11 ]. Integrating genetic markers into reaction norm models allows the identification of candidate genes along environmental gradients through genome-wide association studies (GWAS) [ 12 ]. Moreover, the advent of whole-genome sequencing (WGS) technology has made it possible to refine the identification of genomic regions that affect the traits of interest by providing greater chances of identifying causal mutations when compared to marker panels with medium or high density [ 13 ]. Therefore, the combination of GWAS with WGS enables the unraveling of important regions of the genome, as well as candidate genes, thereby enabling the development of more informative marker panels and conducting more accurate genomic evaluations [ 13 ].

Implementing the reaction norm model with GWAS analysis could lead to a greater understanding of the genetic and physiological mechanisms regulating economically important traits. This approach also facilitates the identification of candidate genes associated with these traits across diverse environmental conditions. Thus, the overarching aim of this study was to perform GWAS utilizing sequencing data, focusing on mapping candidate genes associated with the expression of reproductive and growth traits in Nellore cattle, employing the reaction norm approach.

Materials and methods

Phenotypic data.

Phenotypic information was obtained from 138,706 females and 942,577 Nellore males born between 1984 and 2019 and belonging to three commercial breeding programs (DeltaGen, Cia do Melhoramento, and Paint – CRV Lagoa) in Brazil and Paraguay that integrate the Nellore Alliance dataset ( www.gensys.com.br ).

The traits used in the present study were age at first calving (AFC), scrotal circumference (SC), post-weaning weight gain (PWG), and yearling weight (YW). In reproductive management, some herds exposed heifer to reproduction in two breeding seasons: (1) heifers aged 16 months are exposed to reproduction for 60 days in an anticipated breeding season from February to April to identify sexually precocious heifers and (2) heifers that were not pregnant during the anticipated breeding season were given another opportunity during the regular breeding season (October and January), usually at approximately 24 months. During the mating season, the heifers were either artificially inseminated or naturally mated ( ∼  50%). When a fixed-time AI protocol was used, the entire contemporary group received the same protocol, and pregnancy was diagnosed approximately 60 days after the end of the breeding season. Non-conceiving females are discarded from the herd. The AFC was computed in days, which is the difference between the first calving date and the heifers’ birth date. SC was measured in centimeters (cm) at yearling, and PWG was calculated in kilograms (kg) to determine the difference between the YW and weaning weight.

For the analysis, only animals with known sires and dams and from contemporary groups (CG) with a minimum of 20 animals were considered. The CG for the evaluated traits considered animals from the same year and season of birth, herd (at birth, weaning, and yearling), and management group (at birth, weaning, and yearling). The management group includes information about nutritional and sanitary treatment at each growth stage. For YW and PWG, sex was also added to the CG. Descriptive statistics of the dataset used for each trait after data editing are shown in Table  1 .

The pedigree dataset considered genealogical information of 1,578,503 individuals form 9946 sires and 628,231 dams encompassing 95,606 populations. The pedigree data set had an average inbreeding of 0.16% in the whole population, and the proportion of inbreeding animals was 2.66% (42,026 animals) over the total inbreeding population, showing an inbreeding average of 2.56% (0.01 – 27.10%).

Genomic data

A total of 51,485 animals were genotyped with the Illumina BovineHD (HD) chip ( ∼  778 K SNPs; 4,559 samples) or with a lower and medium density assay (from ∼  26 K to ∼  74 K SNPs; 46,926 samples). Animals genotyped at lower and medium densities were imputed to HD panels using the software FImpute v.3 [ 14 ] considering the ARS-UCD1.2 map. Additionally, 151 influential Nellore sires were whole genome sequenced (WGS) using the Illumina HiSeq X™ Ten ( n  = 51) and Illumina NovaSeq 6000 ( n  = 100) platforms at an average sequence coverage of 14.5x (from 7.8 to 26.3x). Quality control, alignment, and variant calling were carried out following the guidelines provided by the 1000 Bull Genomes Project and described by Fernandes Júnior et al. [ 15 ]. A total of 30,394,484 autosomal SNP markers remained after quality control. Animals genotyped with 700k were imputed for WGS using the software FImpute v.3 [ 14 ], considering as a reference population 151 sires with the highest number of genotyped animals. The imputation accuracy of 0.94 was previously evaluated; for more details see Fernandes Júnior et al. [ 15 ].

Due to computational limitations, we selected 20,000 genotypes for SC, PWG, and YW and 7,159 genotypes for AFC with GEBV accuracy higher than 0.70. The GEBVs accuracy was calculated based on prediction error variance (PEV) and the genetic variance for each trait ( \({{\sigma }}_{\text{a}}^{2}\) ) using the following equation [ 16 ]: \(\text{A}\text{c}\text{c}=1-\sqrt{\text{P}\text{E}\text{V}/{{\sigma }}_{\text{a}}^{2}}\) . The GEBV was estimated using the following animal model:

where \(\mathbf{y}\) is the vector of observations; \(\mathbf{b}\) is the vector of fixed effects of CG and age of the animal at the measurement as linear and quadratic covariates for YW and PWG; \(\mathbf{a}\) is the vector of genetic additive effects, and \(\mathbf{e}\) is the vector of random residual effects. The \(\mathbf{X}\) and \(\mathbf{Z}\) are the incidence matrices related to fixed ( b ) and random effects ( a ), respectively. The model was fitted considering the random effects of animals and residuals as normally distributed: \(\mathbf{a}\sim\text{N}(0,\mathbf{A}{{\sigma }}_{\text{a}}^{2}\) and \(\mathbf{e}\sim\text{N}(0, \mathbf{I}{{\sigma }}_{\text{e}}^{2}\) ), where A is the numerator relationship matrix between animals, I is the identity matrix; \({{\sigma }}_{\text{a}}^{2}\) is the additive genetic variance and \({{\sigma }}_{\text{e}}^{2}\) is the residual variance. The parameters were estimated using the restricted maximum likelihood method considering the average information algorithm implemented in blupf90+ software [ 17 ].

Considering the number of animals genotyped for each trait and a large number of markers (30,394,484), markers with linkage disequilibrium values (r 2 ) greater than 0.75 for SC, PWG, and YW and greater than 0.95 for AFC were pruned using PLINK 2.0 [ 18 ]. This strategy was used to adjust the number of genotyped animals and genetic markers to the computational capacity. Additionally, quality control (QC) of the genomic information was performed by removing autosomal markers with a minor allele frequency (MAF) lower than 0.05, Hardy–Weinberg equilibrium ( P  ≤ 10 − 5 ), and a call rate of markers and samples lower than 0.90. After quality control and removing markers for LD, a total of ∼  2.41 M SNPs for SC, PWG, and YW and ∼  5.06 M SNPs for AFC remained for the GWAS analyses via reaction norm models.

Genotype by environment interaction (GxE)

Environmental gradient descriptor.

The dataset used to evaluate the sensitivity of sexual precocity indicators (AFC and SC) and growth traits (YW and PWG) was assessed through the reaction norm model in two steps [ 3 , 4 ]. In the first step, the environmental gradients (EG) for AFC, SC, and YW were based on the best linear unbiased estimates (BLUE) solutions of CG for YW. We focused on YW because differences in production environments affecting YBW have a significant impact on heifers’ early sexual maturity [ 3 , 12 , 19 ]. The EG for PWG was based on its CG solutions. The EG was obtained with an animal model as follows:

where \(\mathbf{y}\) is the vector of observations for YW or PWG; \(\mathbf{b}\) is the vector of fixed effects of CG and age of the animal at the measurement as linear and quadratic covariates; \(\mathbf{a}\) is the vector of genetic additive effects assumed to follow a normal distribution given by \(\text{N}(0,\mathbf{A}{{\sigma }}_{a}^{2}\) ) and \(\mathbf{e}\) is the vector of random residual effects considered normally distributed as \(\text{N}(0,\mathbf{I}{{\sigma }}_{\text{e}}^{2}\) ),. The \(\mathbf{X}\) and \(\mathbf{Z}\) are the incidence matrices related to fixed ( b ) and random effects ( a ), respectively. The model was performed using the blupf90 + software [ 17 ].

The EG descriptors obtained by CG solutions were standardized to a mean value of 0 and standard deviation (SD) equal to 1, with values ranging from − 3 to + 3 SD, to keep the environmental gradients on the same scale. The CG solutions of YW for AFC ranged from 228.98 (low EG; -3 SD) to 342.09 (high EG, 3 SD). The CG solutions of YW for young bulls with SC information varied from 244.17 (low EG; -3 SD) to 388.23 (high EG, 3 SD), and for animals with YW varied from 227.46 (low EG; -3 SD) to 390.22 (high EG, 3 SD). The CG solutions of PWG for PWG ranged from 55.55 (low EG; -3 SD) to 177.43 (high EG, 3 SD).

Reaction norm (RN) model

In the second step, a single-step genomic reaction norm (ssGRN) model was used to assess GxE. The model assumed a heterogeneous residual variance across EG, using linear regression on \({EG}_{i}\) , with the intercept and slope coefficients being modeled using the log-residual function [ 20 ].

where: \({\mathbf{y}}_{\mathbf{i}\mathbf{j}}\) is the phenotypic information (AFC, SC, YW, and PWG) of animal j on the environment i; \(\mathbf{b}\) is the vector of fixed effects of CG for all traits and age at measurement as linear and quadratic covariates for SC, YW, and PWG; \(\phi\) is the fixed regression coefficient of \({\mathbf{y}}_{\mathbf{i}\mathbf{j}}\) on \({\text{E}\text{G}}_{\text{i}}\) ; \({{\mathbf{a}}_{0}}_{\mathbf{j}}\) is the additive genetic effect for the intercept of animal j, \({{\mathbf{a}}_{1}}_{\mathbf{j}}\) is the additive effect of the slope of the animal j and \({\mathbf{e}}_{\mathbf{i}\mathbf{j}}\) is residual effects. The X , \({\varvec{Z}}_{0}\) and \({\varvec{Z}}_{1}\) are the incidence matrix relating the fixed effects ( b ), intercept ( \({\mathbf{a}}_{0}\) ) and slope ( \({\mathbf{a}}_{1}\) ) to y . The ssGRNM model was fitted considering the following assumptions:

where H is a combined pedigree-genomic relationship matrix, \({\sigma }_{{a}_{0}}^{2}\) and \({\sigma }_{{a}_{1}}^{2}\) are the genetic variances for intercept and slope, respectively, \({\sigma }_{{a}_{0}{a}_{1}}\) is the genetic covariance between the reaction norm parameters (intercept and slope), ⊗ is the Kronecker product; I is an identity matrix, and \(\mathbf{R}\) is the residual variance matrix considering heterogeneous classes. In the ssGRN methodology, the inverse of the H matrix ( \({\mathbf{H}}^{-1}\) ) is given as follows:

where A − 1 is the inverse of the pedigree-based relationship matrix for all animals, \({\varvec{A}}_{22}^{-1}\) is the inverse of the pedigree-based relationship matrix for the genotyped animals, and G − 1 is the inverse of the genomic relationship matrix ( G ), obtained according to VanRaden [ 21 ]:

where \(\mathbf{W}\) is the genotype matrix with codes 0, 1, and 2 for AA, AB, and BB, adjusted for allele frequency expressed as \(2{\text{p}}_{\text{i}}\) , and \({\text{p}}_{\text{i}}\) is the frequency of the second allele. These analyses were performed using the software blupf90 + from the BLUPF90 [ 17 ].

The p-values associated with the SNP effects were obtained from the postGSf90 program within the BLUPF90 software suite [ 17 ]. The p-values for the SNP effects were obtained by Aguilar et al. [ 22 ]:

where \({{\upalpha }}_{\text{i}}\) is the allele substitution effect of the ith marker, \(\text{S}\text{D}\left({{\upalpha }}_{\text{i}}\right)\) is the standard deviation of the ith SNP marker ( \({{\upalpha }}_{\text{i}}\) ) and \({\upvarphi }\) is cumulative function of the normal distribution.

Multiple testing correction and significance testing

The Bonferroni correction test was performed considering a significance threshold for the marker of 0.05 divided by the number of independent BTA segments (Me). The Me considered the effective population size (Ne) and the BTA length [L, in centimorgans (cM)] and was calculated as proposed by Goddard et al. [ 23 ]: \(\text{M}\text{e}= 2\text{N}\text{e}\text{L}/(\text{l}\text{o}\text{g}(\text{N}\text{e}\text{L}\left)\right)\) , where Ne was equal to 100 [ 24 ], and L equal to 2,750 cM for the autosomal chromosome of Nellore cattle ( https://ncbi.nlm.nih.gov/datasets/genome/GCF_000247795.1/ ). As a result, SNP were deemed statistically significant if their \({-\text{l}\text{o}\text{g}}_{10}\left(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e}\right)\) was greater than 5.45. The inflation/deflation factor ( \({\uplambda }\) ) were calculated as \({\uplambda }=\text{m}\text{e}\text{d}\text{i}\text{a}\text{n}\left({-\text{l}\text{o}\text{g}}_{10}\left(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e}\right)\right)/0.456\) , and λ values varied from 0.95 to 1.18 were considered acceptable in GWAS [ 25 ].

Functional analysis

After GWAS analyses, all SNPs were ranked based on their p-values. The average distance in bases pair between SNPs in each BTA was closer to 1 kb (see additional File 1 Table S1 ). Due to the short distance between genetic markers, a region of ± 5 kb around each significant SNP marker was used to map the genes using the Ensembl Variant Effect Predictor (VEP) [ 26 ] considering the ARS-UCD1.2 assembly as the reference genome (GCA_002263795.2).

A “training list” containing the top 100 genes associated with relevant keywords for each trait (see Additional file 1 Table S2 ) and for GxE (resilience, resistance, robustness, fitness, plasticity, and adaptability) was created using Guildify [ 27 ]. The gene list from VEP and training list from Guildify were used as a test list in the ToppGene Suite [ 28 ]. The prioritized significant genes were selected based on a multiple correction false discovery rate (FDR) of 5% (p-value ≤ 10 − 3), indicating that the test genes have the same functional profile as the genes on the “trained” list [ 28 ]. The R packages ClusterProfiler [ 29 ] and enrichplot [ 30 ] were used for enrichment analysis and functional clustering of GO terms for the list of “test” genes. Genes and GO terms were considered enriched when the FDR was lower than 5%.

Significant markers

Significant SNPs associated with both the AFC intercept and slope on EG coefficients were identified on practically all BTAs except for BTA12 (Fig.  1 ). Significant SNPs were found on BTAs 2, 3, 6, 10, 14, 16, 21, and 23 for both SC coefficients (Fig.  2 ). For PWG, significant SNPs were identified on BTA 6, 25, and 29 for intercept and on BTA 6, 13, 25, and 29 for the slope coefficient (Fig.  3 ). For YW, significant markers were found on BTA 6, 10, 14, and 29 for the intercept coefficient and on BTA 6, 10, 14, 23, 25, and 29 for the slope (Fig.  4 ). Considering a region of ± 5 kb of the significant SNPs, a total of 56, 9, and 24 positional candidate genes were identified for intercept coefficient affecting AFC (see Additional file 1 Table S3 ), SC (see Additional File 1 Table S4 ) and YW (see Additional file 1 Table S6 ), respectively, while for PWG (see Additional file 1 Table S5 ) no gene was found for the intercept. For the slope coefficient, a total of 50, 10, 3, and 29 genes were identified as affecting the AFC, SC, PWG, and YW, respectively (see Additional File 1 Table S3 – S6 ).

Manhattan plots of \({-\text{l}\text{o}\text{g}}_{10}(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e})\) for the intercept ( a ) and slope ( b ) coefficients of the reaction norm model for age at first calving (AFC). The horizontal line represents the significance threshold \({-\text{l}\text{o}\text{g}}_{10}\left(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e}\text{d}\right)>5.45\) used to identify the significant SNPs

Manhattan plots of \({-\text{l}\text{o}\text{g}}_{10}(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e})\) for the intercept ( a ) and slope ( b ) coefficients of the reaction norm model for scrotal circumference (SC). The horizontal line represents the significance threshold \({-\text{l}\text{o}\text{g}}_{10}\left(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e}\text{d}\right)>5.45\) used to identify the significant SNPs

Manhattan plots of \({-\text{l}\text{o}\text{g}}_{10}(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e})\) for the intercept ( a ) and slope ( b ) coefficients of the reaction norm model for post-weaning weight gain (PWG). The horizontal line represents the significance threshold \({-\text{l}\text{o}\text{g}}_{10}\left(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e}\text{d}\right)>5.45\) used to identify the significant SNPs

Manhattan plots of \({-\text{l}\text{o}\text{g}}_{10}(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e})\) for the intercept ( a ) and slope ( b ) coefficients of the reaction norm model for yearling weight (YW). The horizontal line represents the significance threshold \({-\text{l}\text{o}\text{g}}_{10}\left(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e}\text{d}\right)>5.45\) used to identify the significant SNPs

The significant SNP markers (− log10 (p-value) > 5.45) for productive and reproductive traits in Nellore cattle were environmentally dependent, with reranking of their effects across EG levels (Fig.  5 ). The SNP makers effects in the low EG (-3.0) were different from those in the high EG (3.0, Fig.  5 ). This strong effect of SNPxE interaction indicates that genomic regions have a striking effect on the Nellore sexual precocity indicator (Fig.  5 a and b) and weight traits (Fig.  5 c and d) at a determined EG level, with changes not only in magnitude but also in direction. A greater dispersion of SNP marker effects was observed for SC (Fig.  5 b) and YW (Fig.  5 d) when the EG level became less restrictive.

Single nucleotide polymorphism (SNP) effect estimates significantly associated ( \({-\text{l}\text{o}\text{g}}_{10}\left(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e}\text{d}\right)>5.45\) ) with age at first calving (AFC, a ), scrotal circumference (SC, b ), post-weaning weight gain (PWG, c ) and for yearling weight (YW, d ) across environmental conditions. Different colors represent the chromosome where the SNP marker was identified

After gene prioritization by ToppGene, 32, 6, and 2 positional candidate genes were retained for AFC, SC, and YW intercept coefficient, respectively. For the slope, there were 31, 6, 1, and 3 genes for AFC (Table  2 ), SC, PWG, and YW (Table  3 ), respectively. In the functional analysis, enriched clusters representing the relationships between prioritized genes and GO terms for intercept and slope common genes were found for the studied traits, and the complete table with all enrichment analysis results can be found in the supplementary material (Tables S7 to S9 ).

We performed a GWAS via ssGRN to detect candidate genomic regions associated with sexual precocity indicators (AFC and SC) and growth traits (YW and PWG) (Figs.  1 , 2 , 3 and 4 ). Some identified genomic regions are common between slopes and intercepts and between traits. The SNP markers detected (− log 10 (p-value) > 5.45) showed reranking across EG levels, in which the effects on the Low EG levels were different from those on the High EG levels (Fig.  5 ). The SNP effects changed in magnitude and direction according to the EG level. Several studies of reproductive traits in dairy cattle [ 31 ] and beef cattle [ 3 , 12 ] and reproduction, body composition, and growth traits in pigs [ 32 ] have shown that different environmental conditions can cause substantial changes in SNP effect estimates.

Genomic regions for RN coefficients affecting AFC

The GWAS analysis for AFC has identified 33 and 32 significant SNP markers associated with the intercept and slope, respectively. These markers map 29 genes that are shared between them (Table  2 ), which explains the high correlation between the coefficients of the reaction norm, which was r g = 0.93 [ 4 ]. Candidate genes with significant effect ( \({-\text{l}\text{o}\text{g}}_{10}\left(\text{p}-\text{v}\text{a}\text{l}\text{u}\text{e}\right)>5.45\) ) on the AFC intercept and slope were related to lipid metabolism. The PLCB1 on BTA13 encodes a phospholipase and is related to the hydrolysis of phospholipids into fatty acids [ 33 ] and to the energy metabolism process [ 34 ]. In addition, it was associated with carcass fat deposition in cattle [ 35 ]. This gene is essential for fertilization in mammals since it is widely distributed on the oocyte plasma membrane and, independently, is involved in sperm–oocyte fusion as an extracellular component in mouse oocytes [ 36 ]. Additionally, it is expressed in bovine oocytes during early growth and meiotic maturation and appears to be required for successful sperm–oocyte interactions during fertilization [ 37 , 38 ]. The PLCB1 gene has previously been associated with heat stress in sheep and goats [ 39 ], cattle [ 40 , 41 ], and catfish [ 34 ], suggesting that it can be an indicator of the GxE interaction response. The CTSH (BTA21) is a gene belonging to the cathepsin family and is involved in adipocyte differentiation [ 42 ]. The age at first calving, a trait related to female sexual precocity, can be affected by the level of subcutaneous fat in cattle [ 43 ]. These findings indicate that both genes ( PLCB1 and CTSH) have pleiotropic properties, supporting the occurrence of a favorable effect on subcutaneous fat deposition and precocity/fertility traits in bovine females [ 44 , 45 , 46 ].

The FUT8 gene on BTA10 encodes an enzyme that transfers fucose from GDP-fucose to glycoconjugates such as glycoproteins [ 47 ]. This gene was also associated with AFC [ 48 ] and sire conception rate [ 49 ]. Deletion of this gene in mice induced severe growth retardation and death during postnatal development [ 50 ]. Furthermore, FUT8 is an essential gene for maintaining normal physiological homeostasis [ 47 , 50 , 51 ], suggesting its role in adapting to environmental variations. The PPP1R12A gene (BTA5) is involved in insulin signaling regulation [ 52 ] and is associated with Nellore female sexual precocity [ 12 ]. This gene is promising since metabolic homeostasis mediated by insulin and glucose has an important role in the nervous system and ovary [ 53 ]. FGF10 (BTA20) is a member of the fibroblast growth factor family and is of particular interest for livestock reproduction because it is expressed in theca cells, luteal cells, and oocytes [ 54 , 55 ] in addition to playing an important role in oocyte maturation in bovines [ 56 , 57 , 58 ].

The functional enrichment analysis identified the major biological processes related to the positive regulation of cell communication (GO:0010647), neuropeptide catabolic process (GO:0010813), positive regulation of signaling (GO:0023056), MAPK cascade (GO:0000165), myoblast fusion involved in skeletal muscle regeneration (GO:0014905) and molecular function in lipid binding (GO:0008289, Table  4 ). These biological processes affect AFC by improving signaling pathways that involve hormones like estrogen and testosterone (GO:0010647 and GO:0023056), but also by hormones that affect cellular processes, such as growth, differentiation, and hormonal activities (GO:0000165) and early muscle development (GO:0014905, Table  4 ) in response to hormonal changes associated with early puberty [ 46 , 59 ].

The MAPK signaling pathway interacts with different intracellular signaling pathways, such as steroid receptors that influence uterine cell proliferation [ 60 ], and plays a key role in embryonic and yolk sac angiogenesis during fetal-placental development [ 61 ]. Furthermore, evidence shows that MAPK cascades are involved in several male reproductive processes such as spermatogenesis, sperm maturation, sperm capacitation, and acrosome reaction before oocyte fertilization [ 62 ]. In livestock species, Gonçalves et al. [ 63 ] found differentially expressed genes involved in the MAPK pathway in the cervix at different stages of the estrous cycle in sheep and cattle. The enriched genes were also involved in several immune system processes (see additional Table S7), such as the regulation of adaptive immune memory response (GO:0090716; GO:1,905,674; GO:1,905,676), processes associated with T cells (GO:0002456; GO:0035783; GO:2,001,188; GO:2,001,190; GO:0035739; GO:2,000,561; GO:2,000,563), regulation of B cell receptor (GO:0050855; GO:0050861), and interleukins (GO:0035722; GO:0070498; GO:0071349). The immune and reproductive systems closely interact due to the sharing of certain cytokines and their receptors, which can affect neuroendocrine events, ovarian function, placenta, and embryo development and may play a role in immunological reproductive failure [ 64 ]. In Holstein cattle, Thompson-Crispi et al. [ 65 ] reported favorable genetic associations between the adaptive immune response and reproductive traits, suggesting that selection for overall immune responsiveness may lead to a positive response in reproductive traits in cattle.

Genomic regions for RN coefficients affecting SC

Multiple prioritized genes ( GRB14 , CYP19A1 , LYN , and PAPPA2 ) were associated with both SC reaction norm coefficients (Table  3 ). The GRB14 gene, on BTA2, encodes a growth factor receptor-binding protein, and mRNA molecules of this gene have been found to be expressed at high levels in the mammalian ovary, liver, kidney, and skeletal muscle [ 66 , 67 ]. In addition, Bohrer et al. [ 68 ] showed that GRB14 mRNA is expressed in granulosa and theca cells during different stages of follicular development, suggesting that this gene may play a regulatory role during follicular divergence in cattle. The PAPPA2 gene, located on BTA16, affects reproduction and fertility and has important roles in pregnancy and postnatal growth [ 69 ]. SNP markers within the PAPPA2 gene have been associated with calving ease and productive life in Holstein cattle, playing an important role in the breeding of first-calf heifers and affecting essential reproductive aspects such as calving interval, days to calving, and pregnancy rate [ 70 ]. These results suggest a pleiotropic effect of genes that influence both SC and female sexual performance traits, corroborating studies reporting favorable genetic correlation estimates between these traits [ 44 , 71 , 72 , 73 ].

The RORA gene on BTA10, associated only with the SC intercept coefficient, encodes a nuclear receptor that is essential for the activation of myogenic-specific markers and regulates several genes involved in lipid metabolism [ 74 , 75 ]. Moreover, it is related to steroid hormone receptor activity and, when combined with this hormone, produces the signal within the cell to initiate a change in cell activity or function [ 76 ]. Additionally, associated only with SC intercept, the WNT2B gene encodes a member of the Wnt family of secreted and highly conserved signaling factors that function in a variety of developmental processes, including the regulation of cell growth and differentiation [ 77 , 78 ]. Using RNA-seq technology, Zhang et al. [ 79 ] identified a cluster of transcripts, including WNT2B mRNA, that may have direct or indirect functions in the initiation of puberty in sheep, which may provide new insights into the mechanisms that trigger puberty in ruminant species. In cattle, Liu et al. [ 80 ] reported that the WNT2B gene was enriched in male gonad development, supporting the influence of this gene on scrotal circumference. The MYO1E gene (BTA10) was associated with slope and is a structural myofibrillar protein related to the response of plants to recovery growth. Myogenic factors are associated with endocrine factors, which play important roles in the regulation of muscle mass, fiber size, nutrient partitioning, and reproduction [ 81 ]. This gene is also associated with the rapid differentiation of neonatal epithelial cells into mature intestinal epithelial cells (Benesh et al., 2010) and with feed efficiency in chickens [ 82 ].

The GO terms for SC (see additional Table S8) indicated that the CYP19A1 gene was associated with oxidoreductase activity (GO:0016712 and GO:0016705), aromatase activity (GO:0070330) and iron ion binding (GO:0005506). The CYP19A1 gene, enriched for aromatase activity, is mainly expressed in Leydig and testicular germ cells [ 83 , 84 ] and encodes a member of the cytochrome P450 superfamily of enzymes. Cytochrome P450 aromatase is an enzyme that catalyzes the conversion of androgens, such as testosterone, to estrogens, which act as sex steroid hormones but also function during growth and differentiation [ 85 ]. These enzymes are highly expressed in both the gonads and the brain in humans [ 86 ]. Variation in the CYP19A1 gene was associated with growth and reproduction in mice and humans [ 87 ]. Using RNA-seq to profile the testicular transcriptome in premature and mature sheep, Yang et al. [ 88 ] observed that CYP19A1 expression levels significantly increased with animals’ age, indicating that this gene may play an important role in ruminants’ testicular development.

Genomic regions for RN coefficients affecting PWG

For PWG, only the gene PTPRT gene on BTA13 (FDR-corrected p-value < 0.05) was detected in the prioritization analysis for the slope coefficient (Table  3 ). The PTPRT gene on BTA13 encodes a protein from the tyrosine phosphatase (PTP) family, related to a variety of physiological processes, including cell growth, differentiation, metabolism, cell cycle regulation, and cytoskeletal function [ 89 ]. In production animals, a relationship between the PTPRT gene polymorphisms and resistance to some bacterial and parasitic infections was observed, such as resistance to brucellosis in goats [ 90 ] and tuberculosis in cattle [ 91 ]. In this sense, the fact that this gene is associated with resistance to different infections lays the groundwork for potential GxE interaction. Furthermore, in a genomic association study, the PTPRT gene was shown to be associated with birth weight in ovine [ 92 ], elucidating the importance of this gene in growth traits.

Genomic regions for RN coefficients affecting YW

For YW, BTA14 had a major influence on this trait, and two prioritized genes ( LYN and PRKDC ) are associated with both reaction norm coefficients. The LYN gene encodes a Src family kinase that is involved in cell proliferation, survival, differentiation, migration, adhesion, and apoptosis [ 41 , 93 ]. In beef cattle, this gene has been associated with sexual precocity in heifers [ 12 , 46 ], growth [ 94 ], feed intake [ 95 ], carcass [ 96 ], and meat quality traits [ 96 ]. In addition, this gene was also associated with SC in this study. It is important to mention that the LYN gene is located within a promising QTL on BTA14 that harbors a variety of genes influencing a wide range of traits of economic interest in livestock [ 97 , 98 ]. PRKDC , also known as XRCC7 , is related to embryonic development, interferon tau expression, and the trophoblast development rate in cattle [ 58 ]. In other farm species, this gene has been associated with body size in sheep [ 99 ] and feed conversion efficiency in pigs [ 100 ], suggesting that this gene plays an important role in growth and development. Although Guildify did not identify the PLAG1 gene on BTA14 during gene prioritization, this gene has a striking effect on biological mechanisms that might help explain the variability in body weight and adaptability to environmental conditions. The SNP markers identified in the BTA14 region were 20.58–25.11 Mb ( LYN, TMEM68, PLAG1, CHCHD7 , and MOS ), affecting the MAPK signaling pathway and affecting cell proliferation and growth by mediating IGF-1 and − 2, which control the energy metabolism linked to tissue development [ 101 ]. In multiple breeds, Utsunomiya et al. [ 102 ] studied Nellore cattle, and Bouwman et al. [ 103 ] reported that specific haplotypes associated with the PLAG1 mutation have positive effects on weight and conformation traits.

The PPARD on BTA23, associated with the YW slope, encodes the peroxisome proliferator-activated receptor delta, a transcription factor predominantly expressed in skeletal muscle [ 104 ] involved in the development, lipid metabolism, energy expenditure, tissue repair and regeneration, and inflammation [ 105 ]. PPARD acts as a key regulator of energy metabolism in skeletal muscle, using lipids as the main energy substrate [ 106 ], thus allowing glucose to become more available for other physiological processes [ 105 ]. In dairy cows, this gene was implicated in muscle fatty acid transport and oxidation during early lactation [ 107 ] and influences factors such as lactation onset and lipid supply [ 108 , 109 ]. The enrichment analysis for YW (see additional Table S9) identified potential candidate genes ( BPNT2 and NAP1L5 ) involved in processes related to phosphorylated carbohydrate dephosphorylation (GO:0046838), inositol phosphate (GO:0046855, GO:0071545 and GO:0043647), phosphatidylinositol (GO:0046854, GO:0006661 and GO:0046488), nucleosome (GO:0006334 and GO:0034728), phospholipid (GO:0008654), glycerolipid (GO:0045017), nucleotidase activity (GO:0008252), phosphatase activity (GO:0016791), phosphoric ester and hydrolase activity (GO:0042578). Most of the enriched GO terms are involved in phosphate metabolism. Phosphate plays essential roles in diverse cellular actions, such as energy metabolism, differentiation, proliferation, and specific functions of differentiated cells [ 110 ], all of which are crucial for the growth and development of organisms. In addition, inositol phosphates are related to energy homeostasis, antioxidant and anti-inflammatory activities, and play a role as neurotransmitters [ 111 ]. There is evidence that inositol mimics the insulin signaling pathway [ 112 ]. In this sense, Lee & Bedford [ 113 ] suggested that possibly inositol induces glucose uptake, leading to an increased energy supply in skeletal muscle to support growth, providing insights into potential inositol mechanisms in promoting the animal growth response.

GWAS via reaction norm detected candidate genes affecting both the intercept and slope on EG for sexual precocity indicator (AFC and SC) and growth (YW and PWG) traits related to several biological mechanisms by which beef cattle respond to environmental changes. The genes found have been previously associated with growth, adaptative and reproductive traits in cattle and other livestock species. In general, the potential candidate genes identified were involved in several biological mechanisms related to lipid metabolism, immune response, MAPK signaling pathway, and energy and phosphate metabolism. The results of the GWAS analysis provide a better understanding of the underlying biological processes associated with growth and reproductive traits in Nellore cattle raised under different environmental conditions.

Data availability

The phenotypic and genotypic information are available for academic use from the authors upon reasonable request (contacting the researcher Lucia Galvão de Albuqueruqe to e-mail: [email protected]) and with permission of Alliance Nellore breeding program (https://gensys.com.br).

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Acknowledgements

The authors are thankful to FAPESP (#2017/10630-2, #2018/20026-8, #2019/06361-1, #2020/14846-2 and #2022/11852-7), CNPq and CAPES for financial support. We also thank the commercial breeding programs contributing to the Alliance Nellore dataset (https://gensys.com.br) for providing the data dataset used in this work.

The research was part of the projects supported by São Paulo Research Foundation (FAPESP) grants #2017/10630-2, #2018/20026-8, #2019/06361-1, #2020/14846-2 and #2022/11852-7.

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Ivan Carvalho Filho, Leonardo M. Arikawa, Lucio F. M. Mota authors have contributed equally to this work.

Authors and Affiliations

Department of Animal Science, School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil

Ivan Carvalho Filho, Leonardo M. Arikawa, Lucio F. M. Mota, Gabriel S. Campos, Larissa F. S. Fonseca, Gerardo A. Fernandes Júnior, Delvan A. Silva, Caio S. Teixeira, Thales L. Silva, Lucia G. Albuquerque & Roberto Carvalheiro

Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G2W1, Canada

Flavio S. Schenkel

Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA

Daniela Lourenco

National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil

Lucia G. Albuquerque

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Contributions

LGA and RC conceived and coordinated the study. LFMM, ICF, TLS, CST and RC performed the study design. LFMM, ICF, DAS, GSC, LMA, FSS and DL contributed to the statistical analysis and manuscript preparation. LMA and LFMM contributed to interpreting results and critically revising the manuscript. GAFJ and LFSF participated in sequence alignment and file preparation. All authors read, revised, and approved the final manuscript.

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Correspondence to Lucio F. M. Mota .

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The animal data collection procedures in this study were approved by Animal Care of São Paulo State University (UNESP) School of Agricultural and Veterinary Science Ethical Committee (protocol number 18.340/16). Furthermore, there is not direct animal involvement in this study.

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Carvalho Filho, I., Arikawa, L.M., Mota, L.F.M. et al. Genome-wide association study considering genotype-by-environment interaction for productive and reproductive traits using whole-genome sequencing in Nellore cattle. BMC Genomics 25 , 623 (2024). https://doi.org/10.1186/s12864-024-10520-x

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Accepted : 13 June 2024

Published : 20 June 2024

DOI : https://doi.org/10.1186/s12864-024-10520-x

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