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Article

Genome-Wide Association Study for Fatty Acid Composition in American Angus Cattle

by
Muhammad Dawood
1,2,
Luke Matthew Kramer
3,
Muhammad Imran Shabbir
1 and
James Mark Reecy
3,*
1
Department of Biological Sciences, International Islamic University, Islamabad 44000, Pakistan
2
Department of Animal Breeding & Genetics, University of Veterinary and Animal Sciences, Lahore 54000, Pakistan
3
Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA 50011, USA
*
Author to whom correspondence should be addressed.
Animals 2021, 11(8), 2424; https://doi.org/10.3390/ani11082424
Submission received: 1 July 2021 / Revised: 4 August 2021 / Accepted: 12 August 2021 / Published: 18 August 2021
(This article belongs to the Section Animal Genetics and Genomics)
Versions Notes

Abstract

:

Simple Summary

Almost everybody depends on livestock for various reasons directly or indirectly. Consequently, improving livestock production means improving human life. Meat plays important role in human life, as it is good source of protein and energy. Meat composition depends on breed’s genetics and environmental factors. Fatty acids (FA) play important role in human diet and health. FA add flavor and taste to meat. Fatty acid composition of meat is a complex polygenic trait that is controlled by genetics and environmental factors. Therefore, the objective of the present study was to identify genomic regions associated with FA composition in American Angus. Thirty-six different genomic regions were identified associated with variation in at least one FA. The genomic regions associated with more than one FA and high genetic variance, harbor good candidate genes (e.g., FABP2, FASN, FADS2, FADS3 and SCD). The identified makers could be used to select for altered FA profile and help to increase the understanding of the genetic basis of FA composition. Furthermore, findings from the present study could help to devise effective breeding plans and selection strategies for the improvement of beef FA profile.

Abstract

Livestock is an important commodity playing a major role in the global economy. Red meat plays an important role in human life, as it is a good source of animal protein and energy. The fatty acid content of beef has been shown to impact the eating experience and nutritional value of beef. Therefore, this study aimed to identify genomic regions which can account for genetic variation in meat fatty acid content. Genotypes imputed to the Illumina BovineHD 770K BeadChip were used in this study. Thirty-six 1-Mb genomic regions with a posterior probability of inclusion (PPI) greater than 0.90 were identified to be associated with variation in the content of at least one fatty acid. The genomic regions (1Mb) which were associated with more than one fatty acid trait with high genetic variance and harbored good candidate genes were on Chromosome (Chr) 6 (fatty acid binding protein 2), Chr 19 (thyroid hormone receptor alpha, fatty acid synthase), Chr 26 (stearoyl-CoA desaturase), and Chr 29 (thyroid hormone responsive, fatty acid desaturase 2, and fatty acid desaturase 3). Further studies are required to identify the causal variants within the identified genomic regions. Findings from the present study will help to increase understanding of the variation in fatty acid content of beef and help to enhance selection for beef with improved fatty acid composition.

1. Introduction

Beef has a high nutritional value, and it is a rich source of minerals, vitamins, and protein. The consumer is becoming more concerned about their health and more conscious about the quality of the meat that they consume. Consumers have been told that beef consumption is associated with some serious health issues, such as heart diseases and obesity [1]. However, recent findings have shown that the long-standing belief that beef is associated with cardiovascular disease is incorrect [2,3,4,5,6,7]. Furthermore, fat is a very important constituent of the human daily diet; it provides energy and also contains essential fatty acids, and adds flavor to food [8]. The fatty acids present in animal tissues can be separated into phospholipid and triacylglyceride fractions [9]. Fatty acid composition and fat content of the beef are associated with the taste, flavor, and sensory properties of the meat [10]. It has been reported that fatty acid composition varies across different breeds and feeding regimes [10,11].
The mechanism that control fatty acid composition of meat is a complex process that is regulated by genetics and environmental factors. There have been several studies published that evaluated the extent to which genetics controlled variation in fatty acid composition in Santa Gertrudis, Brahman, Hereford, Nellore, and Black Angus cattle breeds [12,13,14]. Identification of genomic markers and regions associated with beef fatty acids could be used to select for an improved fatty acid profile and to alter the saturated to mono- and polyunsaturated fatty acid ratios. The objective of the present study was to identify genomic regions associated with fatty acid composition in American Angus cattle.

2. Materials and Methods

2.1. Animal Selection

The purebred American Angus cattle used in the current study were reared according to standard animal care procedures, approved by the Iowa State University Animal Care and Use Committee. All the research animals were raised on Iowa State University research demonstration farms.

2.2. Sampling and DNA Isolation

A total of 2177 American Black Angus calves sired by 134 sires were used in this study. Blood samples were collected from the jugular vein. DNA samples were collected as previously described by Garmyn et al. [15]. DNA was stored at -20 degrees Celsius until further processing.

2.3. Genotype Data

Animals were originally genotyped with either the BovineSNP50 BeadChip (Illumina, San Diego, CA, USA) or the BovineHD BeadChip (Illumina, San Diego, CA, USA) by Neogen GeneSeek Operations (Lincoln, NE, USA). Animals genotyped with the BovineSNP50 BeadChip were imputed to the BovineHD BeadChip SNP density using FImpute [16] and SNPipeline package (Hailin Su, https://github.com/cbkmephisto/SNPipeline (accessed on 27 February 2017)) by using 820 Angus individuals originally genotyped on the BovineHD BeadChip. These 820 individuals included animals from the ISU herd and external animals. A filter of 0.05 minor allele frequency was applied and all markers with missing information were excluded. After filtering, a total of 199,431 markers were excluded from analysis, leaving a total of 574,662 markers for data analyses. Genome coordinates are relative to the Bovine UMD 3.1 genome assembly.

2.4. Fatty Acid Profile

For fatty acid profile analysis, animals were slaughtered at commercial slaughtering facilities. All the slaughtering procedures were carried out by trained personnel. Carcass data collection, tissue sampling, and fatty acid profile analysis were carried out. Fatty acid composition was analyzed as previously reported [17].
For each fatty acid, phenotypic observations were recorded on a fat percentage basis to estimate the marker effect. In this study, 56 different fatty acid traits were included.

2.5. Statistical Analysis

Imputed genotype data were utilized to estimate the SNP effect associated with fatty acid composition. Statistical analysis was performed using the BayesB method for genomic prediction [18]. Data were analyzed via the following model:
y = Xb + Zu + e
where y is the observable value for fatty acid, and X and Z are fixed and random effects, respectively. In this model, b is the fixed effect (age, sex, and population mean), u is the random effect marker, and e is the residual effect [18,19]. Fixed effect and covariates included: contemporary group, sex and hot carcass weight, longissimus muscle area at 12th rib, subcutaneous fat thickness at 12th rib and chemically extracted fat. All the analyses were performed using GenSel software [20].
A chain of 50,000 iterations with the first 5000 as burn-in was used, and the parameter pi (π) was set at 0.99906 (99.9%; approximately 540 SNP markers with a non-zero effect), while genetic and residual variances for each trait were estimated using BayesC (initial variances set as half the total phenotypic variance) before being used in BayesB [21,22]. The posterior probability of inclusion and correlation between QTL and trait were calculated, as described by [20].

3. Results and Discussion

3.1. Fatty Acid Data Statistics

Summary statistics for the studied fatty acids traits are given in Table 1.

3.2. Posterior Residual and Genetic Variance, Heritability Estimation

The SNP-based heritability estimates for fatty acid traits in this study ranged from 0.005 to 0.610 (Table 2). The lower heritability estimates indicated that SNP markers would be poor predictors of fatty acid composition, whereas the higher heritability estimates demonstrate that SNP markers could provide reliable predictions for the content of some fatty acids. The highest heritability estimate was 0.610 for C13:0 and 0.478 for C18:1 trans-12. There were ten fatty acid traits that appeared to have moderate heritability values. These ten fatty acids and their respective h2 values were C16:0 (0.20), LCFA (0.231), MCFA (0.232), C14:1 (0.232), n3/n6 (0.237), C14:0 (0.262), C16:1/C16:0 (0.266) C16:0/C14:0 (0.286), C20:0 (0.298), and UFA/SFA (0.359). Heritability estimates for all fatty acid traits are reported in Table 2.
All other traits (excluding those described above) appeared to have low heritability values. For these traits, the amount of phenotypic variance explained by the markers was low. The lowest heritability estimated was on fatty acid ratio C18:0/C16:0 (0.005). Previously reported heritability values for fatty acid traits were lower than in the present study. In American Angus, using 50k SNP chip data, the highest heritability value (0.57) was reported for saturated fatty acid C14:0 [18], while in Nellore cattle, the highest reported value was 0.24 for C17:0 and C18:3-n6 [12]. Another study in Canada on beef cattle showed higher h2 values of 0.57 and 0.59 for a saturated fatty acid (C17:0) and monounsaturated fatty acids (C14:1 and C18:1) [23]. A previous study showed that analysis of fatty acid content on a fat percentage basis was able to explain a greater proportion of phenotypic variance by SNP markers, as compared to using fatty acid content on a beef basis [18].

3.3. Genome-Wide Association Study

A total of 56 fatty acid traits including saturated, monounsaturated, polyunsaturated, and fatty acid groups were used for genome-wide association studies. The identified genomic regions (1-Mb windows) that showed high genetic variance and posterior probability of inclusion (PPI) greater than 90% for having non-zero genetic variance or above are presented in Table 3.
The highest estimated genetic variance explained by a single SNP window was 88.09% for the fatty acid C22:1, while the window with the lowest estimated genetic variance, 1.72%, was for fatty acid C18:1t12. Regarding the SNP window which explained the highest level of genetic variance (30_72), there was a potential candidate gene, phosphatidylinositol specific phospholipase C X domain containing 1 (PLCXD1), on the pseudo-autosomal region (PAR). This gene is X-linked in ruminants. PLCXD gene products are phosphodiesterases involved in the regulation of cytosolic calcium and have protein kinase activity [24,25].
This study identified a total of 36 different 1-Mb SNP windows that were associated with fatty acid content of skeletal muscle. Three windows (19_51, 26_21, and 29_18) appeared to be associated with most beef fatty acid traits. These three windows were previously reported in the same Black Angus population using the Bovine SNP50 BeadChip [18]. Many of the 1-Mb SNP windows were associated with more than one fatty acid trait. A genomic region on chromosome 19 (at 51 Mb) was associated with 15 fatty acid traits (LCFA, MCFA, MUFA, MUFA/SFA, SFA, UFA/SFA, C14:0, C14:1, C16:0, C16:1, C18:1c9, C16:0/C14:0, C16:1/C16:0, C16:1-C18:1/C16:0-C18:0, and C18:0/C16:0). This region was also previously reported to be associated with important fatty acid traits [18,26]. This SNP window contains a good candidate gene—fatty acid synthase (FASN). This gene has been reported to be involved in beef fatty acid composition [27]. It has also been reported to be associated with adipose composition, milk fatty acid composition, and milk fat content in many different breeds of cattle. These reports indicated that this gene has a pivotal role and is an important candidate gene for fatty acid composition [26,28,29,30,31].
Similar to the window at 51 Mb on chromosome 19, there were additional SNP windows that were associated with more than ten fatty acid traits, including a window at 21 Mb on Chromosome 26, which was associated with 13 FA traits, and a window at 18 Mb on Chromosome 29, which was associated with 11 FA traits. These two SNP windows were also previously reported to be associated with various FA traits [18,26]. These two regions harbor good candidate genes for fatty acid composition, including stearoyl-CoA desaturase (SCD) and thyroid hormone responsive (THRSP). Previous studies have reported that SCD is associated with meat fat composition and milk fat composition [28,29,30,31]. Thyroid hormone responsive and stearoyl-CoA desaturase is involved in fatty acid synthesis [32]. Variants in the THRSP gene have been shown to be associated with the synthesis of beef fatty acids, which are expected to have a direct impact on beef quality [18,33]. It has also been reported that both SCD and THRSP genes are involved in lipid metabolism in cattle [34]. The 1-Mb SNP windows that harbor good candidate genes for fatty acid synthesis and fat regulation are shown in Table 4.
In the present GWAS, some new genomic windows were identified which were not previously reported by any study. These new SNP windows are associated with different fatty acid traits and harbor good candidate genes for fatty acid composition. On chromosome 16, a 1-Mb SNP window at 4 Mb was associated with SFA, MUFA, MUFA/SFA, UFA/SFA, and C16:1, C18:1/C16:0, C18:0. This window contains good candidate genes—6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 (PFKFB2) and peptidase M20 domain-containing 1 (PM20D1). The PFKFB2 gene has a role in degradation and synthesis of fructose-2,6-biphosphate [26]. It has been previously reported that a QTL spanning this region is related to fat thickness at the 12th rib in American Angus [35]. Peptidase M20 domain-containing 1 (PM20D1) is an enzyme that synthesizes N-acyl amino acids (NAAs). NAAs are bioactive lipids composed of fatty acyl chains. PM20D1 regulates the condensation and hydrolysis of N-acyl amino acids from free amino acids and fatty acids [36,37,38]. Fatty acid binding protein 2 (FABP2) may have a role in lipogenesis and adipose tissue weight variability [39]. This gene has not been identified in previous GWAS as having a significant association with fatty acid composition [40]. Bardet-Biedl syndrome 4 (BBS4) is involved in the secretion and expression of Follistatin-like 1 (FSTL1), which is associated with adipogenesis. BBS4 also plays a role in fatty acid profile, lipolysis, and fat accumulation [41,42]. Acetyl-CoA acyltransferase 2 (ACAA2) codes for an enzyme from the thiolase family. This enzyme is involved in elongation and degradation of fatty acids. It has been associated with milk yield and fat yield in dairy sheep [43]. Fatty acid desaturase 2 (FADS2) and fatty acid desaturase 3 (FADS3) belong to the fatty acid desaturase family. This family of genes creates a cis double bond in FA chains at specific sites and is associated with desaturation of fatty acids and blood phospholipids [44,45]. Oxysterol binding protein like 5 (OSBPL5) is a lipid transporter, chiefly linked with the exchange of phosphatidylserine with phosphatidylinositol 4-phosphate. It has a role in maintaining cholesterol balance [46,47].
These genes have not been previously identified by any GWAS as being related to fatty acid content and fat regulation. In the present GWAS, we did not identify any SNP windows which contain some genes (LXR, LXRA, SREBP1, PPARG, ACSL1, LEP, ACACA, FABP4, and SL1TRK6) previously shown to be associated with FA composition and variation in beef cattle [27,48,49,50,51,52,53]. This may indicate that genetic control of fatty acid content varies greatly from breed to breed.

3.4. Correlation within Genomic Regions

All of the identified regions of the genome had a genetic correlation with a fatty acid trait that ranged from 0.1 to 0.74. Both the highest genetic correlation of 0.74 and the lowest genetic correlation of 0.1 were observed for C13:0 (6_95 and 6_65). These genomic windows lack candidate genes associated with fatty acid composition or fat regulation. Some genomic regions (6_7, 10_19, 16_4, 19_42, 19_51, 24_49, 26_21, 29_18, 29_42, and 29_49) that had high correlations with fatty acid content do contain potential candidate genes (FABP2, BBS4, PFKFB2, THRA, FASN, ACAA2, SCD, THRSP, FADS2, FADS3, MOB2, OSBPL5). Three of the genomic regions (19_51, 26_21, 29_18) were previously reported by our group to have a high correlation with fatty acid content [18]. These genomic windows harbor good candidate genes (FASN, SCD, THRSP) that may be associated with fatty acid composition [27,28,49,54,55]. Besides these three genomic windows, there are other genomic regions (6_7, 10_19, 16_4, 19_42, 24_49, 29_42, and 29_49) that had a positive correlation with different saturated or monounsaturated fatty acid traits (C14:0, C14:1, C16:1, C17:0, MCFA, LCFA) and fatty acid groups (C16:0/C14:0 and C16:1/C16:0). These regions have not been previously reported to be associated with fatty acid content, but they contain possible candidate genes (FABP2, BBS4, THRA, ACAA2, FADS2, and FADS3) for fatty acid synthesis/composition and fat regulation. [39,40,44,45,56,57,58,59,60,61,62,63,64].

4. Conclusions

Genome-wide association studies can provide insight into understanding the mechanisms underlying fatty acid composition. Furthermore, genomic selection methodology can be used to select for, and to alter, fatty acid content. This study utilized imputed BovineHD BeadChip (770k) genotypes along with skeletal muscle fatty acid content phenotypic data to identify 36 1-Mb SNP windows that had a PPI > 0.90. Some of these SNP windows have been previously reported, including 19_51, 26_21, and 29_18. In addition, some new genomic regions that had not been previously reported to be associated with fatty acid content were identified: 6_7, 19_42, and 29_42. Fatty acid composition and deposition are complex polygenic traits having low to moderate heritability. The genomic regions identified in the present study and associated potential candidate genes for FA composition could help increase understanding of the genetic basis of FA composition in beef cattle (Black Angus). This study could also help to devise sensible breeding plans and selection strategies based on identified genomic regions for the improvement of beef fatty acid profile.

Author Contributions

Data curation, M.D.; Formal analysis, M.D. and L.M.K.; Funding acquisition, J.M.R.; Investigation, M.D.; Project administration, J.M.R.; Software, M.D. and L.M.K.; Supervision, J.M.R. and M.I.S.; Writing—original draft, M.D.; Writing—review & editing, J.M.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Higher Education Commission of Pakistan under “International Research Support Initiative Program”.

Institutional Review Board Statement

All the animal work and experiments were approved by the Iowa State University Animal Care and Use Committee (IACUC).

Data Availability Statement

Data will be available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Laborde, F.L.; Mandell, I.B.; Tosh, J.J.; Wilton, J.W.; Buchanan-Smith, J.G. Breed effects on growth performance, carcass characteristics, fatty acid composition, and palatability attributes in finishing steers. J. Anim. Sci. 2001, 79, 355–365. [Google Scholar] [CrossRef] [PubMed]
  2. Vernooij, R.W.M.; Zeraatkar, D.; Han, M.A.; El Dib, R.; Zworth, M.; Milio, K.; Sit, D.; Lee, Y.; Gomaa, H.; Valli, C.; et al. Patterns of red and processed meat consumption and risk for cardiometabolic and cancer outcomes a systematic review and meta-analysis of cohort studies. Ann. Intern. Med. 2019, 171, 732–741. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Valli, C.; Rabassa, M.; Johnston, B.C.; Kuijpers, R.; Prokop-Dorner, A.; Zajac, J.; Storman, D.; Storman, M.; Bala, M.M.; Solà, I.; et al. Health-related values and preferences regarding meat consumption a mixed-methods systematic review. Ann. Intern. Med. 2019, 171, 742–755. [Google Scholar] [CrossRef] [PubMed]
  4. Han, M.A.; Zeraatkar, D.; Guyatt, G.H.; Vernooij, R.W.M.; El Dib, R.; Zhang, Y.; Algarni, A.; Leung, G.; Storman, D.; Valli, C.; et al. Reduction of red and processed meat intake and cancer mortality and incidence a systematic review and meta-analysis of cohort studies. Ann. Intern. Med. 2019, 171, 711–720. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Zeraatkar, D.; Han, M.A.; Guyatt, G.H.; Vernooij, R.W.M.; El Dib, R.; Cheung, K.; Milio, K.; Zworth, M.; Bartoszko, J.J.; Valli, C.; et al. Red and processed meat consumption and risk for all-cause mortality and cardiometabolic outcomes a systematic review and meta-analysis of cohort studies. Ann. Intern. Med. 2019, 171, 703–710. [Google Scholar] [CrossRef] [Green Version]
  6. Zeraatkar, D.; Johnston, B.C.; Bartoszko, J.; Cheung, K.; Bala, M.M.; Valli, C.; Rabassa, M.; Sit, D.; Milio, K.; Sadeghirad, B.; et al. Effect of lower versus higher red meat intake on cardiometabolic and cancer outcomes a systematic review of randomized trials. Ann. Intern. Med. 2019, 171, 721–731. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Johnston, B.C.; Zeraatkar, D.; Han, M.A.; Vernooij, R.W.M.; Valli, C.; El Dib, R.; Marshall, C.; Stover, P.J.; Fairweather-Taitt, S.; Wójcik, G.; et al. Unprocessed red meat and processed meat consumption: Dietary guideline recommendations from the nutritional recommendations (NUTRIRECS) consortium. Ann. Intern. Med. 2019, 171, 756–764. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Da Martins, T.S.; de Lemos, M.V.A.; Mueller, L.F.; Baldi, F.; de Amorim, T.R.; Ferrinho, A.M.; Muñoz, J.A.; de Fuzikawa, I.H.S.; de Moura, G.V.; Gemelli, J.L.; et al. Fat Deposition, Fatty Acid Composition, and Its Relationship with Meat Quality and Human Health. In Meat Science and Nutrition; IntechOpen: London, UK, 2018; pp. 17–37. Available online: https://www.intechopen.com/chapters/61577 (accessed on 15 August 2021). [CrossRef] [Green Version]
  9. Yen, C.-L.E.; Stone, S.J.; Koliwad, S.; Harris, C.; Farese, R.V. Thematic Review Series: Glycerolipids. DGAT enzymes and triacylglycerol biosynthesis. J. Lipid Res. 2008, 49, 2283–2301. [Google Scholar] [CrossRef] [Green Version]
  10. Venkata Reddy, B.; Sivakumar, A.S.; Jeong, D.W.; Woo, Y.-B.; Park, S.-J.; Lee, S.-Y.; Byun, J.-Y.; Kim, C.-H.; Cho, S.-H.; Hwang, I. Beef quality traits of heifer in comparison with steer, bull and cow at various feeding environments. Anim. Sci. J. 2015, 86, 1–16. [Google Scholar] [CrossRef]
  11. Vahmani, P.; Mapiye, C.; Prieto, N.; Rolland, D.C.; McAllister, T.A.; Aalhus, J.L.; Dugan, M.E.R. The scope for manipulating the polyunsaturated fatty acid content of beef: A review. J. Anim. Sci. Biotechnol. 2015, 6, 29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Cesar, A.S.M.; Regitano, L.C.A.; Mourão, G.B.; Tullio, R.R.; Lanna, D.P.D.; Nassu, R.T.; Mudado, M.A.; Oliveira, P.S.N.; do Nascimento, M.L.; Chaves, A.S.; et al. Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle. BMC Genet. 2014, 15, 39. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Kelly, M.J.; Tume, R.K.; Fortes, M.; Thompson, J.M. Whole-genome association study of fatty acid composition in a diverse range of beef cattle breeds. J. Anim. Sci. 2014, 92, 1895–1901. [Google Scholar] [CrossRef]
  14. Wang, Z.; Zhu, B.; Niu, H.; Zhang, W.; Xu, L.; Xu, L.; Chen, Y.; Zhang, L.; Gao, X.; Gao, H.; et al. Genome wide association study identifies SNPs associated with fatty acid composition in Chinese Wagyu cattle. J. Anim. Sci. Biotechnol. 2019, 10, 27. [Google Scholar] [CrossRef] [Green Version]
  15. Garmyn, A.J.; Hilton, G.G.; Mateescu, R.G.; Morgan, J.B.; Reecy, J.M.; Tait, J.G.; Beitz, D.C.; Duan, Q.; Schoonmaker, J.P.; Mayes, M.S.; et al. Estimation of relationships between mineral concentration and fatty acid composition of longissimus muscle and beef palatability traits. J. Anim. Sci. 2011, 89, 2849–2858. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Sargolzaei, M.; Chesnais, J.P.; Schenkel, F.S. A new approach for efficient genotype imputation using information from relatives. BMC Genom. 2014, 15, 478. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Kramer, L.M.; Abdel Ghaffar, M.A.; Koltes, J.E.; Fritz-Waters, E.R.; Mayes, M.S.; Sewell, A.D.; Weeks, N.T.; Garrick, D.J.; Fernando, R.L.; Ma, L.; et al. Epistatic interactions associated with fatty acid concentrations of beef from angus sired beef cattle. BMC Genom. 2016, 17, 891. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Saatchi, M.; Garrick, D.J.; Tait, R.G.; Mayes, M.S.; Drewnoski, M.; Schoonmaker, J.; Diaz, C.; Beitz, D.C.; Reecy, J.M. Genome-wide association and prediction of direct genomic breeding values for composition of fatty acids in Angus beef cattlea. BMC Genom. 2013, 14, 730. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Meuwissen, T.H.E.; Hayes, B.J.; Goddard, M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001, 157, 1819–1829. [Google Scholar] [CrossRef] [PubMed]
  20. Garrick, D.J.; Fernando, R.L. Implementing a QTL detection study (GWAS) using genomic prediction methodology. Methods Mol. Biol. 2013, 1019, 275–298. [Google Scholar] [CrossRef]
  21. Habier, D.; Fernando, R.L.; Kizilkaya, K.; Garrick, D.J. Extension of the bayesian alphabet for genomic selection. BMC Bioinform. 2011, 12, 186. [Google Scholar] [CrossRef] [Green Version]
  22. Kizilkaya, K.; Fernando, R.L.; Garrick, D.J. Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. J. Anim. Sci. 2010, 88, 544–551. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Chen, L.; Ekine-Dzivenu, C.; Vinsky, M.; Basarab, J.; Aalhus, J.; Dugan, M.E.R.; Fitzsimmons, C.; Stothard, P.; Li, C. Genome-wide association and genomic prediction of breeding values for fatty acid composition in subcutaneous adipose and longissimus lumborum muscle of beef cattle. BMC Genet. 2015, 16, 135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Das, P.J.; Chowdhary, B.P.; Raudsepp, T. Characterization of the bovine pseudoautosomal region and comparison with sheep, goat, and other mammalian pseudoautosomal regions. Cytogenet. Genome Res. 2009, 126, 139–147. [Google Scholar] [CrossRef] [PubMed]
  25. Gellatly, S.A.; Kalujnaia, S.; Cramb, G. Cloning, tissue distribution and sub-cellular localisation of phospholipase C X-domain containing protein (PLCXD) isoforms. Biochem. Biophys. Res. Commun. 2012, 424, 651–656. [Google Scholar] [CrossRef]
  26. Buchanan, J.W.; Reecy, J.M.; Garrick, D.J.; Duan, Q.; Beitz, D.C.; Koltes, J.E.; Saatchi, M.; Koesterke, L.; Mateescu, R.G. Deriving Gene Networks from SNP Associated with Triacylglycerol and Phospholipid Fatty Acid Fractions from Ribeyes of Angus Cattle. Front. Genet. 2016, 7, 116. [Google Scholar] [CrossRef] [Green Version]
  27. Zhang, S.; Knight, T.J.; Reecy, J.M.; Beitz, D.C. DNA polymorphisms in bovine fatty acid synthase are associated with beef fatty acid composition. Anim. Genet. 2008, 39, 62–70. [Google Scholar] [CrossRef] [PubMed]
  28. Narukami, T.; Sasazaki, S.; Oyama, K.; Nogi, T.; Taniguchi, M.; Mannen, H. Effect of DNA polymorphisms related to fatty acid composition in adipose tissue of Holstein cattle. Anim. Sci. J. 2011, 82, 406–411. [Google Scholar] [CrossRef]
  29. Yokota, S.; Sugita, H.; Ardiyanti, A.; Shoji, N.; Nakajima, H.; Hosono, M.; Otomo, Y.; Suda, Y.; Katoh, K.; Suzuki, K. Contributions of FASN and SCD gene polymorphisms on fatty acid composition in muscle from Japanese Black cattle. Anim. Genet. 2012, 43, 790–792. [Google Scholar] [CrossRef]
  30. Maharani, D.; Jung, Y.; Jung, W.Y.; Jo, C.; Ryoo, S.H.; Lee, S.H.; Yeon, S.H.; Lee, J.H. Association of five candidate genes with fatty acid composition in Korean cattle. Mol. Biol. Rep. 2012, 39, 6113–6121. [Google Scholar] [CrossRef]
  31. Li, C.; Sun, D.; Zhang, S.; Wang, S.; Wu, X.; Zhang, Q.; Liu, L.; Li, Y.; Qiao, L. Genome wide association study identifies 20 novel promising genes associated with milk fatty acid traits in Chinese Holstein. PLoS ONE 2014, 9, e96186. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Donnelly, C.; Olsen, A.M.; Lewis, L.D.; Eisenberg, B.L.; Eastman, A.; Kinlaw, W.B. Conjugated linoleic acid (CLA) inhibits expression of the spot 14 (THRSP) and fatty acid synthase genes and impairs the growth of human breast cancer and liposarcoma cells. Nutr. Cancer 2009, 61, 114–122. [Google Scholar] [CrossRef] [Green Version]
  33. La, B.; Oh, D.; Lee, Y.; Shin, S.; Lee, C.; Chung, E.; Yeo, J. Association of bovine fatty acid composition with novel missense nucleotide polymorphism in the thyroid hormone-responsive (THRSP) gene. Anim. Genet. 2013, 44, 118. [Google Scholar] [CrossRef] [PubMed]
  34. Graugnard, D.E.; Berger, L.L.; Faulkner, D.B.; Loor, J.J. High-starch diets induce precocious adipogenic gene network up-regulation in longissimus lumborum of early-weaned Angus cattle. Br. J. Nutr. 2010, 103, 953–963. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. McClure, M.C.; Morsci, N.S.; Schnabel, R.D.; Kim, J.W.; Yao, P.; Rolf, M.M.; McKay, S.D.; Gregg, S.J.; Chapple, R.H.; Northcutt, S.L.; et al. A genome scan for quantitative trait loci influencing carcass, post-natal growth and reproductive traits in commercial Angus cattle. Anim. Genet. 2010, 41, 597–607. [Google Scholar] [CrossRef] [PubMed]
  36. Long, J.Z.; Roche, A.M.; Berdan, C.A.; Louie, S.M.; Roberts, A.J.; Svensson, K.J.; Dou, F.Y.; Bateman, L.A.; Mina, A.I.; Deng, Z.; et al. Ablation of PM20D1 reveals N-acyl amino acid control of metabolism and nociception. Proc. Natl. Acad. Sci. USA 2018, 115, E6937–E6945. [Google Scholar] [CrossRef] [Green Version]
  37. Long, J.Z.; Svensson, K.J.; Bateman, L.A.; Lin, H.; Kamenecka, T.; Lokurkar, I.A.; Lou, J.; Rao, R.R.; Chang, M.R.R.; Jedrychowski, M.P.; et al. The Secreted Enzyme PM20D1 Regulates Lipidated Amino Acid Uncouplers of Mitochondria. Cell 2016, 166, 424–435. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Gunawardhana, L.P.; Baines, K.J.; Mattes, J.; Murphy, V.E.; Simpson, J.L.; Gibson, P.G. Differential DNA methylation profiles of infants exposed to maternal asthma during pregnancy. Pediatr. Pulmonol. 2014, 49, 852–862. [Google Scholar] [CrossRef] [PubMed]
  39. Hu, G.; Wang, S.; Tian, J.; Chu, L.; Li, H. Epistatic effect between ACACA and FABP2 gene on abdominal fat traits in broilers. J. Genet. Genom. 2010, 37, 505–512. [Google Scholar] [CrossRef]
  40. Estellé, J.; Mercadé, A.; Pérez-Enciso, M.; Pena, R.N.; Silió, L.; Sánchez, A.; Folch, J.M. Evaluation of FABP2 as candidate gene for a fatty acid composition QTL in porcine chromosome 8. J. Anim. Breed. Genet. 2009, 126, 52–58. [Google Scholar] [CrossRef] [PubMed]
  41. Aksanov, O.; Green, P.; Birk, R.Z. BBS4 directly affects proliferation and differentiation of adipocytes. Cell. Mol. Life Sci. 2014, 71, 3381–3392. [Google Scholar] [CrossRef] [PubMed]
  42. Prieto-Echagüe, V.; Lodh, S.; Colman, L.; Bobba, N.; Santos, L.; Katsanis, N.; Escande, C.; Zaghloul, N.A.; Badano, J.L. BBS4 regulates the expression and secretion of FSTL1, a protein that participates in ciliogenesis and the differentiation of 3T3-L1. Sci. Rep. 2017, 7, 9765. [Google Scholar] [CrossRef] [PubMed]
  43. Miltiadou, D.; Hager-Theodorides, A.L.; Symeou, S.; Constantinou, C.; Psifidi, A.; Banos, G.; Tzamaloukas, O. Variants in the 3′ untranslated region of the ovine acetyl-coenzyme A acyltransferase 2 gene are associated with dairy traits and exhibit differential allelic expression. J. Dairy Sci. 2017, 100, 6285–6297. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Rzehak, P.; Heinrich, J.; Klopp, N.; Schaeffer, L.; Hoff, S.; Wolfram, G.; Illig, T.; Linseisen, J. Evidence for an association between genetic variants of the fatty acid desaturase 1 fatty acid desaturase 2 (FADS1 FADS2) gene cluster and the fatty acid composition of erythrocyte membranes. Br. J. Nutr. 2009, 101, 20–26. [Google Scholar] [CrossRef] [Green Version]
  45. Park, W.J.; Kothapalli, K.S.D.; Reardon, H.T.; Kim, L.Y.; Brenna, J.T. Novel fatty acid desaturase 3 (FADS3) transcripts generated by alternative splicing. Gene 2009, 446, 28–34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Chung, J.; Torta, F.; Masai, K.; Lucast, L.; Czapla, H.; Tanner, L.B.; Narayanaswamy, P.; Wenk, M.R.; Nakatsu, F.; De Camilli, P. PI4P/phosphatidylserine countertransport at ORP5- and ORP8-mediated ER—Plasma membrane contacts. Science 2015, 349, 428–432. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Pollock, R.A.; Zaman, L.; Chandran, V.; Gladman, D.D. Epigenome-wide analysis of sperm cells identifies IL22 as a possible germ line risk locus for psoriatic arthritis. PLoS ONE 2019, 14, e0212043. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Shi, H.; Kokoeva, M.V.; Inouye, K.; Tzameli, I.; Yin, H.; Flier, J.S. TLR4 links innate immunity and fatty acid-induced insulin resistance. J. Clin. Investig. 2006, 116, 3015–3025. [Google Scholar] [CrossRef] [PubMed]
  49. Matsuhashi, T.; Maruyama, S.; Uemoto, Y.; Kobayashi, N.; Mannen, H.; Abe, T.; Sakaguchi, S.; Kobayashi, E. Effects of bovine fatty acid synthase, stearoyl-coenzyme A desaturase, sterol regulatory element-binding protein 1, and growth hormone gene polymorphisms on fatty acid composition and carcass traits in Japanese Black cattle. J. Anim. Sci. 2011, 89, 12–22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Oh, D.; Lee, Y.; Lee, C.; Chung, E.; Yeo, J. Association of bovine fatty acid composition with missense nucleotide polymorphism in exon7 of peroxisome proliferator-activated receptor gamma gene. Anim. Genet. 2012, 43, 474. [Google Scholar] [CrossRef]
  51. Widmann, P.; Nuernberg, K.; Kuehn, C.; Weikard, R. Association of an ACSL1 gene variant with polyunsaturated fatty acids in bovine skeletal muscle. BMC Genet. 2011, 12, 96. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Orrù, L.; Cifuni, G.F.; Piasentier, E.; Corazzin, M.; Bovolenta, S.; Moioli, B. Association analyses of single nucleotide polymorphisms in the LEP and SCD1 genes on the fatty acid profile of muscle fat in Simmental bulls. Meat Sci. 2011, 87, 344–348. [Google Scholar] [CrossRef] [PubMed]
  53. Hoashi, S.; Hinenoya, T.; Tanaka, A.; Ohsaki, H.; Sasazaki, S.; Taniguchi, M.; Oyama, K.; Mukai, F.; Mannen, H. Association between fatty acid compositions and genotypes of FABP4 and LXR-alpha in Japanese Black cattle. BMC Genet. 2008, 9, 84. [Google Scholar] [CrossRef] [Green Version]
  54. Abe, T.; Saburi, J.; Hasebe, H.; Nakagawa, T.; Misumi, S.; Nade, T.; Nakajima, H.; Shoji, N.; Kobayashi, M.; Kobayashi, E. Novel mutations of the FASN gene and their effect on fatty acid composition in japanese black beef. Biochem. Genet. 2009, 47, 397–411. [Google Scholar] [CrossRef] [PubMed]
  55. Uemoto, Y.; Abe, T.; Tameoka, N.; Hasebe, H.; Inoue, K.; Nakajima, H.; Shoji, N.; Kobayashi, M.; Kobayashi, E. Whole-genome association study for fatty acid composition of oleic acid in Japanese Black cattle. Anim. Genet. 2011, 42, 141–148. [Google Scholar] [CrossRef] [PubMed]
  56. Mullur, R.; Liu, Y.Y.; Brent, G.A. Thyroid hormone regulation of metabolism. Physiol. Rev. 2014, 94, 355–382. [Google Scholar] [CrossRef] [Green Version]
  57. Yao, D.W.; Luo, J.; He, Q.Y.; Wu, M.; Shi, H.B.; Wang, H.; Wang, M.; Xu, H.F.; Loor, J.J. Thyroid hormone responsive (THRSP) promotes the synthesis of medium-chain fatty acids in goat mammary epithelial cells. J. Dairy Sci. 2016, 99, 3124–3133. [Google Scholar] [CrossRef] [Green Version]
  58. Damiano, F.; Rochira, A.; Gnoni, A.; Siculella, L. Action of thyroid hormones, T3 and T2, on hepatic fatty acids: Differences in metabolic effects and molecular mechanisms. Int. J. Mol. Sci. 2017, 18, 744. [Google Scholar] [CrossRef]
  59. Boschetti, E.; Bordoni, A.; Meluzzi, A.; Castellini, C.; Dal Bosco, A.; Sirri, F. Fatty acid composition of chicken breast meat is dependent on genotype-related variation of FADS1 and FADS2 gene expression and desaturating activity. Animal 2016, 10, 700–708. [Google Scholar] [CrossRef]
  60. Schaeffer, L.; Gohlke, H.; Müller, M.; Heid, I.M.; Palmer, L.J.; Kompauer, I.; Demmelmair, H.; Illig, T.; Koletzko, B.; Heinrich, J. Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids. Hum. Mol. Genet. 2006, 15, 1745–1756. [Google Scholar] [CrossRef]
  61. Jornayvaz, F.R.; Lee, H.Y.; Jurczak, M.J.; Alves, T.C.; Guebre-Egziabher, F.; Guigni, B.A.; Zhang, D.; Samuel, V.T.; Silva, J.E.; Shulman, G.I. Thyroid hormone receptor-α gene knockout mice are protected from diet-induced hepatic insulin resistance. Endocrinology 2012, 153, 583–591. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Yao, X.; Hou, S.; Zhang, D.; Xia, H.; Wang, Y.C.; Jiang, J.; Yin, H.; Ying, H. Regulation of fatty acid composition and lipid storage by thyroid hormone in mouse liver. Cell Biosci. 2014, 4, 38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Sinha, R.A.; Singh, B.K.; Yen, P.M. Thyroid hormone regulation of hepatic lipid and carbohydrate metabolism. Trends Endocrinol. Metab. 2014, 25, 538–545. [Google Scholar] [CrossRef] [PubMed]
  64. Rudolph, M.C.; Monks, J.; Burns, V.; Phistry, M.; Marians, R.; Foote, M.R.; Bauman, D.E.; Anderson, S.M.; Neville, M.C. Sterol regulatory element binding protein and dietary lipid regulation of fatty acid synthesis in the mammary epithelium. Am. J. Physiol. Endocrinol. Metab. 2010, 299, 918–927. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table
Table 1. Fatty acids and fatty acids groups statistics summary (mean, standard deviation, and coefficient of variance) for all the studied traits.
Table 1. Fatty acids and fatty acids groups statistics summary (mean, standard deviation, and coefficient of variance) for all the studied traits.
TraitMeanSDCV%
C10:00.040.07196.81
C12:00.060.0690.09
C13:00.010.071105.43
C14:02.710.5821.21
C14:10.570.2034.78
C15:00.590.3355.61
C16:026.571.806.79
C16:13.490.7120.38
C17:01.340.3929.14
C17:11.070.3734.65
C18:013.621.9114.01
C18:1 c938.552.797.24
C18:1 c110.100.10106.32
C18:1 c120.250.1663.85
C18:1 c130.100.10105.96
C18:1 t6/90.130.23178.88
C18:1 t10/113.581.3938.84
C18:1 t120.070.24355.96
C18:1 t151.030.5148.92
C18:23.941.3133.33
CLA c9t110.130.13104.68
CLA t10c120.050.09174.15
C18:3 n30.170.1692.47
C18:3 n60.020.03222.57
C20:00.020.04178.69
C20:10.090.11116.14
C20:20.040.05131.67
C20:3 n30.020.09378.10
C20:3 n60.120.17139.19
C20:40.770.3848.84
C20:50.130.29215.61
C22:00.110.15135.98
C22:10.010.061073.49
C22:40.060.14215.40
C22:50.130.17127.20
C22:60.080.16194.83
C23:00.070.18256.19
C24:00.140.37258.53
SFA45.292.395.27
MUFA49.042.795.70
PUFA5.671.8532.66
MCFA3.980.8020.02
LCFA96.020.800.83
n30.540.55101.90
n65.131.6432.04
n3/n60.110.13117.97
AI0.690.0912.91
PUFA/SFA0.130.0434.10
UFA/SFA1.210.129.72
MUFA/SFA1.090.1110.35
C14:1/C14:00.210.0525.99
C16:0/C14:010.192.0520.07
C16:1/C16:00.130.0218.66
C17:1/C17:00.881.50170.85
C18:0/C16:00.520.0916.80
C16:1-C18:1/C16:0-C18:01.180.1210.28
Table
Table 2. Posterior residual variance (σ2e, g × 10−10) estimate, genetic variance (σ2g, g × 10−10) estimate and the estimated heritability (h2) for fatty acids (Fat% basis).
Table 2. Posterior residual variance (σ2e, g × 10−10) estimate, genetic variance (σ2g, g × 10−10) estimate and the estimated heritability (h2) for fatty acids (Fat% basis).
Traitσ2e, g × 10−10σ2g, g × 10−10h2
SFA3.9951.0290.200
C10:00.0050.0000.072
C12:00.0030.0000.021
C13:00.0020.0030.610
C14:00.2260.0800.262
C15:00.0710.0020.023
C16:02.2670.5680.200
C17:00.0660.0050.077
C18:02.1330.4260.166
C20:00.0010.0000.298
C22:00.0120.0000.023
C23:00.0310.0010.019
C24:00.0990.0120.111
MUFA4.9320.8790.151
C14:010.0230.0070.232
C16:10.4200.0830.165
C17:10.0580.0030.045
C18:1 cis-95.3901.0890.168
C18:1 cis-110.0110.0000.016
C18:1 cis-120.0180.0020.104
C18:1 cis-130.0120.0000.015
C18:1 trans-6/90.0440.0060.121
C18:1 trans-10/111.3970.1030.068
C18:1 trans-120.0320.0290.478
C18:1 trans-150.2170.0060.029
C20:10.0040.0000.014
C22:10.0040.0000.102
PUFA2.4040.0670.027
C18:021.2280.0430.034
C18:3 n-30.0140.0000.019
C18:3 n-60.0010.0000.016
C20:20.0020.0000.016
C20:3 n-30.0090.0000.014
C20:3 n-60.0250.0020.062
C20:40.1090.0030.025
C20:50.0700.0080.105
C22:40.0150.0010.045
C22:50.0170.0000.018
C22:60.0190.0040.191
CLA c9t110.0150.0000.025
CLA t10c120.0070.0000.042
n-30.1900.0270.124
n-61.9420.0570.029
n-3/n-651.52715.9850.237
AI11.0391.0140.084
MCFA0.4440.1340.232
LCFA0.4450.1330.231
MUFA/SFA0.0120.0020.134
PUFA/SFA3.8560.8870.187
UFA/SFA0.0070.0040.359
C14:1/C14:00.0010.0010.026
C16:0/C14:02.2900.9160.286
C16:1/C16:00.0000.0000.266
C16:1,C18:1/C16:0,C18:00.0130.0020.131
C17:1/C17:00.0580.0030.045
C18:0/C16:00.0040.0020.005
Table
Table 3. 1-Mb SNP windows with PPI 90% or above for fatty acids on fat percent basis.
Table 3. 1-Mb SNP windows with PPI 90% or above for fatty acids on fat percent basis.
TraitPPI 1BTA_MbStart SNP–End SNPSNPVar %Map Position
C10:00.94410_99rs381994440–rs111659820725816.7899005624–99999474
C13:00.98721_10rs445662296–rs7221332702497.8810001943–10995552
0.9896_65rs723554673–rs37795480015941.6365014180–65999168
0.9896_66rs461561099–rs72129560723733.6966005071–66997033
0.9426_95rs525139052–rs7143965202503.2495004561–95991513
C14:0110_19rs457389817–rs7219998342319.2919000816–19989351
119_51rs475360660–rs38305885020454.8551028723–51996481
126_21rs1118223446–rs4754757241867.3921002029–21996318
129_18rs136831403–rs4380264488223.0818005978–18986358
C14:1110_19rs457389817–rs72199983423114.6619000816–19989351
119_51rs475360660–rs38305885020434.5251028723–51996481
126_21rs1118223446–rs47547572418620.1921002029–21996318
129_18rs136831403–rs4380264488218.1418005978–18986358
C16:0119_51rs475360660–rs38305885020449.9351028723–51996481
129_18rs136831403–rs4380264488221.7818005978–18986358
C16:10.96610_19rs457389817–rs7219998342317.7819000816–19989351
119_51rs475360660–rs38305885020426.2351028723–51996481
126_21rs1118223446–rs47547572418613.8521002029–21996318
0.99829_18rs136831403–rs4380264488216.1218005978–18986358
C17:00.98419_42rs137786121–rs46624030025122.2842004863–42996553
124_49rs381050710–rs47712392115533.2349001908–49998425
C17:10.98424_49rs381050710–rs47712392115537.8549001908–49998425
C18:00.9226_21rs1118223446–rs4754757241867.9821002029–21996318
129_18rs136831403–rs4380264488217.118005978–18986358
C18:1 c9119_51rs475360660–rs38305885020450.151028723–51996481
129_18rs136831403–rs4380264488212.2318005978–18986358
C18:1 c12126_21rs1118223446–rs47547572418649.3621002029–21996318
C18:1 t 6/90.94427_39rs451168763–rs3816893132608.3239005538–39990529
C18:1 t10/110.99620_4rs721326040–rs46061756425523.474010324–4999564
C18:1 t120.92210_96rs479600948–rs1093352922562.4996019445–96996211
116_46rs478465218–rs79759903222411.4646008112–46996022
116_48rs135228863–rs4749071193179.6348017181–48984950
0.9817_65rs378071414–rs5253330532857.5165009493–65999327
121_59rs472316688–rs4518062253892.8159000097–59996736
15_111rs468287514–rs4446673952765.12111007549–111997116
0.9025_9rs516462777–rs111681723415211.559015235–9989218
C20:3 n60.9243_86rs718706801–rs73073370426455.386003522–86991729
C20:5129_49rs472519303–rs5261646149322.5449009465–49997333
0.9913_86rs718706801–rs73073370426455.4986003522–86991729
C22:10.99321_10rs445662296–rs72213327024914.3310001943–10995552
0.98730_71rs458478290–rs4810596596536.6571227458–71976081
0.98730_72rs524807927–rs1356093512988.0972004959–72982639
C22:40.92425_11rs467215611–rs45631468438727.9511001504–11999813
C22:60.9625_11rs467215611–rs4563146843876.2411001504–11999813
13_86rs718706801–rs73073370426476.0486003522–86991729
C24:0116_65rs468954509–rs111799671630550.0665001270–65996849
0.9463_86rs718706801–rs73073370426439.5986003522–86991729
SFA0.9491_115rs436612027–rs7152050982246.43115000891–115995052
0.99816_4rs450830345–rs3798115693136.694004764–4992166
119_51rs475360660–rs38305885020431.5351028723–51996481
126_21rs1118223446–rs47547572418612.2321002029–21996318
0.9517_93rs443092875–rs3780899891436.4693002992–93993941
MUFA0.92616_4rs450830345–rs3798115693135.634004764–4992166
119_51rs475360660–rs38305885020435.9451028723–51996481
0.91326_21rs1118223446–rs4754757241869.5421002029–21996318
MCFA110_19rs457389817–rs72199983423111.2219000816–19989351
119_51rs475360660–rs38305885020457.451028723–51996481
129_18rs136831403–rs4380264488223.1918005978–18986358
LCFA110_19rs457389817–rs72199983423111.3519000816–19989351
119_51rs475360660–rs38305885020457.351028723–51996481
129_18rs136831403–rs4380264488223.0218005978–18986358
n3129_49rs472519303–rs5261646149311.7349009465–49997333
13_86rs718706801–rs73073370426476.5186003522–86991729
UFA/SFA0.9821_115rs436612027–rs7152050982243.92115000891–115995052
0.99116_4rs450830345–rs3798115693133.244004764–4992166
119_51rs475360660–rs38305885020415.3551028723–51996481
0.96222_9rs446574361–rs1344224562471.879004252–9998969
0.98926_21rs1118223446–rs4754757241865.3421002029–21996318
0.93126_32rs136160709–rs3828892712712.0632010478–32984426
0.9937_93rs443092875–rs3780899891433.8693002992–93993941
MUFA/SFA0.98916_4rs450830345–rs3798115693135.274004764–4992166
119_51rs475360660–rs38305885020435.9951028723–51996481
0.90426_21rs1118223446–rs4754757241867.4421002029–21996318
C14:1/C14:0126_21rs1118223446–rs4754757241865221002029–21996318
C16:0/C14:0119_51rs475360660–rs38305885020434.5551028723–51996481
126_21rs1118223446–rs4754757241866.9921002029–21996318
129_18rs136831403–rs4380264488215.5218005978–18986358
0.94729_42rs379690091–rs4635882851555.3842001720–42991376
C16:1/C16:00.98410_19rs457389817–rs7219998342315.719000816–19989351
0.98919_51rs475360660–rs3830588502046.1451028723–51996481
126_21rs1118223446–rs4754757241868.421002029–21996318
0.9626_29rs380753352–rs429127342403.4129000086–29997851
0.96429_18rs136831403–rs438026448824.2618005978–18986358
0.9356_7rs43449965–rs7974243122183.367050818–7999410
C18:0/C16:00.82415_75rs474144146–rs7998628082882.8775000095–75998710
119_51rs475360660–rs38305885020413.3851028723–51996481
0.921_22rs449389929–rs4377529202582.4322011632–22997995
129_18rs136831403–rs4380264488215.2618005978–18986358
C16:1,C18:1/
C16:0,C18:0		0.99516_4rs450830345–rs3798115693135.624004764–4992166
119_51rs475360660–rs38305885020431.5151028723–51996481
1 PPI = Posterior Probability of Inclusion.
Table
Table 4. 1Mb chromosome windows having candidate gene associated with fatty acid traits.
Table 4. 1Mb chromosome windows having candidate gene associated with fatty acid traits.
Sr.Region (BTA_Mb)GeneTraits
16_7FABP2C16:1/C16:0
210_19BBS4C14:0, C14:1, C16:1, MCFA, LCFA, C16:1/C16:0
316_4PFKFB2, IL10, RAB7B, PM20D1SFA, MUFA, MUFA/SFA, UFA/SFA, C16:1,C18:1/C16:0,C18:0
419_42THRAC17:0
519_51FASNC14:0, C14:1, C16:0, C16:0/C14:0, C16:1, C16:1/C16:0, C18:0/C16:0, C18:1 c9, LCFA, MCFA, MUFA, MUFA/SFA, SFA, UFA/SFA, C16:1,C18:1/C16:0,C18:0
624_49ACAA2C17:0, C17:1
726_21SCDC14:0, C14:1, C14:1/C14:0, C16:0/C14:0, C16:1, C16:1/C16:0, C18:0, C18:1 c12, MUFA, MUFA/SFA, SFA, UFA/SFA
829_18THRSPC14:0, C14:1, C16:0, C16:0/C14:0, C16:1, C16:1/C16:0, C18:0, C18:0/C16:0, C18:1 c9, LCFA, MCFA
929_42FADS2, FADS3C16:0/C14:0
1029_49MOB2, INS, OSBPL5C20:5, n3
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Dawood, M.; Kramer, L.M.; Shabbir, M.I.; Reecy, J.M. Genome-Wide Association Study for Fatty Acid Composition in American Angus Cattle. Animals 2021, 11, 2424. https://doi.org/10.3390/ani11082424

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Dawood M, Kramer LM, Shabbir MI, Reecy JM. Genome-Wide Association Study for Fatty Acid Composition in American Angus Cattle. Animals. 2021; 11(8):2424. https://doi.org/10.3390/ani11082424

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Dawood, Muhammad, Luke Matthew Kramer, Muhammad Imran Shabbir, and James Mark Reecy. 2021. "Genome-Wide Association Study for Fatty Acid Composition in American Angus Cattle" Animals 11, no. 8: 2424. https://doi.org/10.3390/ani11082424

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