Sources of bias in genetic association studies of cattle: a review

José L. Zepeda Batista; María I. Carrillo Díaz; Luis A. Saavedra Jiménez

doi:10.17533/udea.rccp.v31n4a02

Authors

José L. Zepeda Batista Chapingo Autonomous University
María I. Carrillo Díaz University of Colima
Luis A. Saavedra Jiménez Chapingo Autonomous University

DOI:

https://doi.org/10.17533/udea.rccp.v31n4a02

Keywords:

association estimates, genetic bias, genetic improvement, sampling-related bias, statistical bias

Abstract

Background: Genetic association studies have been increasingly used in cattle breeding programs. However, inconsistent results -such as positive, negative, or absence of association- across studies restrain reproducibility and proper implementation, propitiating the occurrence of bias. Objective: To identify and classify potential sources of bias and determine possible strategies to avoid it in genetic association studies in cattle. Source of bias in genetic association studies: Genetic and genomic sources of bias include effects associated with the gene loci governing expression. Sampling-related and statistical biases are related with factors such as stratification and database size. Strategies to correct bias in genetic association studies: Correction strategies differ in nature. Genetic and genomic strategies are based on determining the appropriate approach to obtain and report the genetic information. Sampling-related and statistical strategies are based on grouping individuals with certain traits that lead to a reduction in heterogeneity. Conclusion: It is necessary to consider the methodology used in previous studies to establish a hierarchy of sources of bias and facilitate decisions on the use of tools to reduce inconsistencies in the results of future studies.

|Abstract

= 287 veces | PDF

= 145 veces|

Downloads

Download data is not yet available.

Author Biographies

José L. Zepeda Batista, Chapingo Autonomous University

VMZ, MSc., Postgraduate in Animal Production, Chapingo Autonomous University, Chapingo, State of Mexico, Mexico.

María I. Carrillo Díaz, University of Colima

IAZ, PhD., Faculty of Veterinary Medicine and Zootechnics, University of Colima, Tecomán, Colima, Mexico.

Luis A. Saavedra Jiménez, Chapingo Autonomous University

IAZ, MSc., Postgraduate in Animal Production, Universidad Autónoma Chapingo, Chapingo, State of Mexico, México.

References

Abdallah JM, Mangin B, Goffinet B, Cierco-Ayrolles C, Perez-Enciso M. A comparison between methods for linkage disequilibrium fine mapping of quantitative trait loci. Genet Res 2004; 83:41-47.

Andrés AM, Clark AG, Shimmin L, Boerwinkle E, Sing CF, Hixson JE. Understanding the accuracy of statistical haplotype inference with sequence data of known phase. Genet Epidemiol 2007; 31:659-671.

Bennewitz J, Edel C, Fries R, Meuwissen THE, Wellmann R. Application of a Bayesian dominance model improves power in quantitative trait genome-wide association analysis. Genet Sel Evol 2017; 49:7.

Benyamin B, Visscher PM, McRae A. Family-based genome-wide association studies. Pharmacogenomics 2009; 10(2):181-190.

Boleckova J, Christensen OF, Sørensen P, Sahana G. Strategies for haplotype-based association mapping in a complex pedigreed population. Czech J Anim Sci 2012; 57:1-9.

Brito LF, Silva FG, Melo ALP, Caetano GC, Torres RA, Rodrigues MT, Menezes GRO. Genetic and environmental factors that influence production and quality of milk of Alpine and Saanen goats. Genet Mol Res 2011; 10:3794-3802.

Burgueño J, De los Campos G, Weigel K, Crossa J. Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Science 2012; 52:707–719.

Bush WS, Moore JH. Chapter 11: Genome-Wide Association Studies. PLoS Comput Biol 2012; 8(12): e1002822.

Day FR, Loh PR, Scott RA, Ong KK, Perry JRB. A robust example of collider bias in a genetic association study. Am J Hum Genet 2016; 98:392–393.

De R, Bush WS, Moore JH. Bioinformatics challenges in genome-wide association studies (GWAS). In: Ronald Trent editor. Clinical Bioinformatics, Methods in Molecular Biology. New York: Springer Science+Bussiness Media; 2014. p.63-81.

De los Campos, G., Naya H., Gianola D., Crossa J., Legarra A., Manfredi E., Weigel K., Cotes J. M. 2009. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182:375-385.

Deb R, Singh U, Kumar S, Singh R, Sengar G, Sharma A. Genetic polymorphism and association of kappa-casein gene with milk production traits among Frieswal (HF x Sahiwal) cross breed of Indian origin. Iran J Vet Res 2014; 15(4):406-408.

Dias RAP, Petrini J, Sterman JB, Pereira J, Santos R, Lopes AL, Barreto G. Multicollinearity in genetic effects for weaning weight in a beef cattle composite population. Livest Sci 2011; 142:188–194.

Dickerson GE. Evaluation of breeds and crosses of domestic animals. Rome (IT): Animal Production and Health Paper FAO; 1993.

Dohoo IR, DesCoteaux L, Leslie K, Fredeen A, Shewfelt W, Preston A, Dowling P. A meta-analysis review of the effects of recombinant bovine somatotropin 2. Effects on animal health, reproductive performance, and culling. Can J Vet Res 2003; 67:252-264.

Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, García JR, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 2013; 36:027–046.

Duifhuis-Rivera T, Lemus-Flores C, Ayala-Valdovinos MÁ, Sánchez-Chiprés DR, Galindo-García J, Mejía-Martínez K, González-Covarrubias E. Polymorphisms in beta and kappa casein are not associated with milk production in two highly technified populations of Holstein cattle in México. J Anim Plant Sci 2014; 24:1316-1321.

Eynard SE, Windig JJ, Leroy G, Van Binsbergen R, Calus MPL. The effect of rare alleles on estimated genomic relationships from whole genome sequence data. BMC Genetics 2015; 16(24):1-12.

Foulkes AS. Applied statistical genetics with R for population-based association studies. Cham (Swiss): Springer International Publishing AG; 2009.

Goode EL, Jarvik GP. Assessment and implications of linkage disequilibrium in genome-wide single-nucleotide polymorphism and microsatellite panels. Genet Epidemiol 2005; 29(Suppl.1): S72-S76.

Groenwold RHH, Moons KGM, Peelen LM, Knol MJ, Hoes AW. Reporting of treatment effects from randomized trials: A plea for multivariable risk ratios. Contemp Clin Trials 2011; 32:399-402.

Gustavsson F, Buitenhuis AJ, Johansson M, Bertelsen HP, Glantz M, Poulsen NA, Lindmark-Månsson H, Stålhammar H, Larsen LB, Bendixen C, Paulsson M, Andrén A. Effects of breed and casein genetic variants on protein profile in milk from Swedish Red, Danish Holstein, and Danish Jersey cows. J Dairy Sci 2014; 97:3866-3877.

Han M, Hu YQ, Lin S. Joint detection of association, imprinting and maternal effects using all children and their parents. Eur J Hum Genet 2013; 21:1449–1456.

Ioannidis JP. Why most published research findings are false. Plos Med 2005; 2(8):0696-0701 e124.

Jaiswal V, Gahlaut V, Meher PK, Mir RR, Jaiswal JP, Rao AR, Bayan HS, Gupta PK. Genome wide single locus single trait, multi-locus and multi-trait association mapping for some important agronomic traits in common wheat (T. aestivum L.). Plos One 2016; 11(7):1-25 e0159343.

Jahuey-Martínez FJ, Parra-Bracamonte GM, Sifuentes-Rincón AM, Martínez-González JC, Gondro C, García-Pérez CA, López-Bustamantes LA. Genomewide association analysis of growth traits in Charolais beef cattle. J Anim Sci 2016; 94:4570-4582.

Kent JW, Dyer TD, Göring HH, Blangero J. Type I error rates in association versus joint linkage/association tests in related individuals. Genet Epidemiol 2007; 31:173-177.

Kinghorn BP, Bastiaansen JWM, Ciobanu DC, Van Der Steer HAM. Quantitative genotyping to estimate genetic contributions to pooled samples and genetic merit of the contributing entities. Acta Agric Scand A 2010; 60:3-12.

Kučerová J, Matějiček A, Jandurová OM, Sørensen P, Němcová E, Štípková M, Kott V, Bouška J, Frelich J. Milk protein genes CSN1S1, CSN2, CSN3, LGB and their relation to genetic values of milk production parameters in Czech Fleckvieh. Czech J Anim Sci 2006; 51:241-247.

Lee YH. Meta-analysis of genetic association studies. Ann Lab Med 2015; 35:283-287.

Lenstra JA, Ajmone-Marsan P, Beja-Pereira A, Bollongino R, Bradley DG, Colli L, De Gaetano A, Edwards CJ, Felius M, Ferretti L, Ginja C, Hristov P, Kantanen J, Lewis CM, Knight J. Introduction to genetic association studies. Cold Spring Harbor Protoc 2012; 3:297-306.

Lirón JP, Magee DA, Negrini R, Radoslavov GA. Meta-analysis of mitochondrial DNA reveals several population bottlenecks during worldwide migrations of cattle. Diversity 2014; 6:179-187.

Ma P, Lund MS, Nielsen US, Aamand GP, Su G. Single-stepgenomic model improved reliability and reduced the bias of genomicpredictions in Danish Jersey. J Dairy Sci 2015; 98:9026-9034.

Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos ME, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CE, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TFC, McCarroll SA, Visscher PM. Finding the missing heritability of complex diseases.Nature 2009; 461(7265):747–753.

Miciński J, Klupczyński J, Mordas W, Zablotna R. Yield and composition of milk from Jersey cows as dependent on the genetic variants of milk proteins. Pol J Food Nutr Sci 2007; 57:95-99.

Pärna E, Kaart T, Kiiman H, Bulitko T, Viinalass H. In: Chaiyabutr N, editor. Milk Production-Advanced Genetic Traits, Cellular Mechanism, Animal Management and Health. Rijeka: InTech; 2012. p.155-172.

Pereira AGT, Utsunomiya YT, Milanesi M, Torrecilha RBP, Carmo AS, Neves HHR, Carvalheiro R, Ajmone-Marsan P, Sonstegard TS, Sölkner J, Contreras-Castillo CJ, Garcia JF. Pleiotropic genes affecting carcass traits in Bos indicus (Nellore) cattle are modulators of growth. Plos One 2016; 11(7):1-13e0158165.

Poulsen NA, Buitenhuis AJ, Larsen LB. Phenotypic and genetic associations of milk traits with milk coagulation properties. J Dairy Sci 2015; 98:1-9.

Pyo HE, Wan PJ. Sample size and statistical power calculation in genetic association studies. Genomics Inform 2012; 10(2):117-122.

Ramírez-Valverde R, Núñez-Domínguez R, Ruíz-FloresA, García-Muñiz JG, Magaña-Valencia F. Comparison of contemporary group definitions for genetic evaluation of Braunvieh cattle. Téc Pec Mex 2008; 46(4):359-370.

Rosenberg NA, Huang L, Jewett EM, Szpiech ZA, JankovicI, Boehnke M. Genome-wide association studies in diverse populations. Nat Rev Genet 2010; 11(5):356-366.

Sagoo GS, Little J, Higgins JPT. Systematic reviews of genetic association studies. PLoS Med 2009; 6(3):e1000028.

Sahana G, Guldbrandtsen B, Janss L, Lund MS. Comparison of association mapping methods in a complex pedigreed population. Genet Epidemiol 2010; 34:455–462.

Satkoski JA, Malhi RS, Kanthaswamy S, Johnson J, Garnica WT, Malladi VS, Smith DG. The effect of SNP discovery method and sample size on estimation of population genetic data for Chinese and Indian rhesus macaques (Macaca mulatta). Primates 2011; 52:129-138.

Schwarzenbacher H, Burgstaller J, Seefried FR, Wurmser C, Hilbe M, Jung S, Fuerst C, Dinhopl N, Weissenböck H, Fuerst-Waltl B, Dolezal M, Winkler R, Grueter O, Bleu U, Wittek T, Fries R, Pausch H. A missense mutation in TUBD1 is associated with high juvenile mortality in Braunvieh and Fleckvieh cattle. BMC Genomics 2016; 17: 400.

Shringarpure S, Xing EP. Effects of sample selection bias on the accuracy of population structure and ancestry inference. G3-GenesGenom Genet 2014; 4:901-911.

Sifuentes-Rincón AM, Parra-Bracamonte GM, De la Rosa RXF, Sánchez VA, Serrano MF, Rosales AJ. Importancia de las pruebas de paternidad basadas en microsatélites para la evaluación genética de ganado de carne en empadre múltiple. Tec Pec Mex 2006; 44(3):389-398.

Su YS, Lee WC. False appearance of gene–environment interactions in genetic association studies. Medicine 2016; 95(9):1-8.

Su G, Christensen OF, Ostersen T, Henryon M, Lund MS. Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. Plos One, 2012, 7(9):e45293.

Tenesa A, Knott SA, Ward D, Smith D, Williams JL, Visscher PM. Estimation of linkage disequilibrium in a sample of the United Kingdom dairy cattle population using unphased genotypes. J Anim Sci 2003; 81:617-623.

Trail JCM, Gregory KE, Durkin J, Sandford J. Crossbreeding cattle in beef production programmes in Kenya. II. Comparison of purebred Boran and Boran crossed with the Red Poll and Santa Gertrudis breeds. Trop Anim Hlth Prod 1984; 16:191-200.

Visscher PM, Woolliams JA, Smith D, Williams JL. Estimation of pedigree errors in the UK dairy population using microsatellite markers and the impact on selection. J Dairy Sci 2002; 85:2368–2375.

Wu C, Li S, Cui Y. Genetic Association Studies: An Information Content Perspective. Curr Genomics 2012; 13(7):566-573.

Yoo W, Mayberry R, Bae S, Singh K, He Q, Lillard J. A study of effects of multicollinearity in the multivariable analysis. Int J Appl Sci Technol 2014; 4(5):9–19.

Zaitlen N, Kraft P. Heritability in the genome-wide association era. Hum Genet 2012; 131(10):1655-1664.

Zhang W, Li J, Guo Y, Zhang L, Xu L, Gao X, Zhu B, Gao H, Ni H, Chen Y. Multistrategy genome-wide association studies identify the DCAF16-NCAPG region as a susceptibility locus for average daily gain in cattle. Sci Rep 2016; 6:38073.

Zhao H, Rebbeck TR, Mitra N. Analyzing genetic association studies with an extended propensity score approach. Stat Appl Genet Mol Biol 2012; 11(5):1-17.

Zhou L, Ding X, Zhang Q, Wang Y, Lund MS, Su G. Consistency of linkage disequilibrium between Chinese and Nordic Holsteins and genomic prediction for Chinese Holsteins using a joint reference population. Genet Sel Evol 2013; 45:1-7.

Zoche-Golob V, Heuwieser W, Krömker V. Investigation of the association between the test day milk fat-protein ratio and clinical mastitis using a Poisson regression approach for analysis of time-to-event data. Prev Vet Med 2015; 121:64-73.