Genome-wide association studies for additive and dominance effects for body composition traits in commercial crossbred Piétrain pigs.

Fat depth (FD) and muscle depth (MD) are economically important traits and used to estimate carcass lean content (LMP), which is one of the main breeding objectives in pig breeding programmes. We assessed the genetic architectures of body composition traits for additive and dominance effects in commercial crossbred Piétrain pigs using both 50 K array and sequence genotypes. We first performed a genome-wide association study (GWAS) using single-marker association analysis with a false discovery rate of 0.1. Then, we estimated the additive and dominance effects of the most significant variant in the quantitative trait loci (QTL) regions. It was investigated whether the use of whole-genome sequence (WGS) will improve the QTL detection (both additive and dominance) with a higher power compared with lower density SNP arrays. Our results showed that more QTL regions were detected by WGS compared with 50 K array (n = 54 vs. n = 17). Of the novel associated regions associated with FD and LMP and detected by WGS, the most pronounced peak was on SSC13, situated at ~116-118, 121-127 and 129-134 Mbp. Additionally, we found that only additive effects contributed to the genetic architecture of the analysed traits and no significant dominance effects were found for the tested SNPs at QTL regions, regardless of panel density. The associated SNPs are located in or near several relevant candidate genes. Of these genes, GABRR2, GALR1, RNGTT, CDH20 and MC4R have been previously reported as being associated with fat deposition traits. However, the genes on SSC1 (ZNF292, ORC3, CNR1, SRSF12, MDN1, TSHZ1, RELCH and RNF152) and SSC18 (TTC26 and KIAA1549) have not been reported previously to our best knowledge. Our current findings provide insights into the genomic regions influencing composition traits in Piétrain pigs.

[1]  M. Bink,et al.  Imputation to whole-genome sequence and its use in genome-wide association studies for pork colour traits in crossbred and purebred pigs , 2022, Frontiers in Genetics.

[2]  L. Zhou,et al.  A genome-wide association study reveals additive and dominance effects on growth and fatness traits in large white pigs. , 2021, Animal genetics.

[3]  Yiqiang Zhao,et al.  Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy , 2021, GigaScience.

[4]  P. Carnier,et al.  Estimation of Additive and Dominance Genetic Effects on Body Weight, Carcass and Ham Quality Traits in Heavy Pigs , 2021, Animals : an open access journal from MDPI.

[5]  J. Noguera,et al.  Identification of strong candidate genes for backfat and intramuscular fatty acid composition in three crosses based on the Iberian pig , 2020, Scientific Reports.

[6]  B. Guldbrandtsen,et al.  Genome-wide association mapping for dominance effects in female fertility using real and simulated data from Danish Holstein cattle , 2020, Scientific Reports.

[7]  P. Stothard,et al.  Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: I: feed efficiency and component traits , 2020, BMC Genomics.

[8]  P. Stothard,et al.  Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: II: carcass merit traits , 2020, BMC Genomics.

[9]  Z. Vitezica,et al.  Estimating dominance genetic variances for growth traits in American Angus males using genomic models , 2019, Journal of animal science.

[10]  D. Tulpan,et al.  The relationship of pork carcass weight and leanness parameters in the Ontario commercial pork industry , 2019, Translational animal science.

[11]  J. Tetens,et al.  GWAS for Meat and Carcass Traits Using Imputed Sequence Level Genotypes in Pooled F2-Designs in Pigs , 2019, G3: Genes, Genomes, Genetics.

[12]  E. Zheng,et al.  Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations , 2019, PloS one.

[13]  Shuhong Zhao,et al.  Genome-Wide Association Study Reveals Candidate Genes for Growth Relevant Traits in Pigs , 2019, Front. Genet..

[14]  Wanbo Li,et al.  A whole-genome sequence based association study on pork eating quality traits and cooking loss in a specially designed heterogeneous F6 pig population. , 2018, Meat science.

[15]  Brian L Browning,et al.  A One-Penny Imputed Genome from Next-Generation Reference Panels. , 2018, American journal of human genetics.

[16]  A. Harada,et al.  The Rab11-binding protein RELCH/KIAA1468 controls intracellular cholesterol distribution , 2018, The Journal of cell biology.

[17]  J. Bennewitz,et al.  Mapping QTL for production traits in segregating Piétrain pig populations using genome-wide association study results of F2 crosses. , 2018, Animal genetics.

[18]  C. Wang,et al.  A genome-wide association study of growth and fatness traits in two pig populations with different genetic backgrounds. , 2018, Journal of animal science.

[19]  Z. Zhang,et al.  A whole genome sequence association study for puberty in a large Duroc × Erhualian F2 population , 2018, Animal genetics.

[20]  J. V. D. van der Werf,et al.  Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations , 2017, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[21]  K. Wimmers,et al.  Genetics of body fat mass and related traits in a pig population selected for leanness , 2017, Scientific Reports.

[22]  M. Goddard,et al.  Including nonadditive genetic effects in mating programs to maximize dairy farm profitability. , 2017, Journal of dairy science.

[23]  J. Steibel,et al.  Genome-wide association study in an F2 Duroc x Pietrain resource population for economically important meat quality and carcass traits. , 2017, Journal of animal science.

[24]  M. Lopes,et al.  Estimation of Additive, Dominance, and Imprinting Genetic Variance Using Genomic Data , 2015, G3: Genes, Genomes, Genetics.

[25]  Lusheng Huang,et al.  Genome-wide association analyses for meat quality traits in Chinese Erhualian pigs and a Western Duroc × (Landrace × Yorkshire) commercial population , 2015, Genetics Selection Evolution.

[26]  W. G. Hill,et al.  Dominance genetic variation contributes little to the missing heritability for human complex traits. , 2015, American journal of human genetics.

[27]  Gustavo de Los Campos,et al.  Unraveling Additive from Nonadditive Effects Using Genomic Relationship Matrices , 2014, Genetics.

[28]  Henk Bovenhuis,et al.  A Genome-Wide Association Study Reveals Dominance Effects on Number of Teats in Pigs , 2014, PloS one.

[29]  R. Veerkamp,et al.  Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle , 2014, Nature Genetics.

[30]  F. Schenkel,et al.  A new approach for efficient genotype imputation using information from relatives , 2014, BMC Genomics.

[31]  K. Wimmers,et al.  Genome-wide association analysis for growth, muscularity and meat quality in Piétrain pigs. , 2014, Animal genetics.

[32]  Shengwen Wang,et al.  Mixed Model Methods for Genomic Prediction and Variance Component Estimation of Additive and Dominance Effects Using SNP Markers , 2014, PloS one.

[33]  P. Visscher,et al.  Advantages and pitfalls in the application of mixed-model association methods , 2014, Nature Genetics.

[34]  Luis Varona,et al.  On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope , 2013, Genetics.

[35]  D. Do,et al.  Genome-Wide Association Study Reveals Genetic Architecture of Eating Behavior in Pigs and Its Implications for Humans Obesity by Comparative Mapping , 2013, PloS one.

[36]  F. Schenkel,et al.  Meta-analysis of genetic parameter estimates for reproduction, growth and carcass traits of pigs in the tropics , 2013 .

[37]  A. Elofsson,et al.  Ligand binding properties of human galanin receptors , 2013, Molecular membrane biology.

[38]  M. Lund,et al.  Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers , 2012, PloS one.

[39]  J. Steibel,et al.  Estimation of linkage disequilibrium in four US pig breeds , 2012, BMC Genomics.

[40]  Bin Fan,et al.  Genome-Wide Association Study Identifies Loci for Body Composition and Structural Soundness Traits in Pigs , 2011, PloS one.

[41]  P. Visscher,et al.  GCTA: a tool for genome-wide complex trait analysis. , 2011, American journal of human genetics.

[42]  T. Park,et al.  Diet-induced obesity regulates the galanin-mediated signaling cascade in the adipose tissue of mice. , 2010, Molecular nutrition & food research.

[43]  L. Varona,et al.  A note on mate allocation for dominance handling in genomic selection , 2010, Genetics Selection Evolution.

[44]  Marina Gispert,et al.  Comparison of different devices for predicting the lean meat percentage of pig carcasses. , 2009, Meat science.

[45]  Pornpimol Charoentong,et al.  ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks , 2009, Bioinform..

[46]  A. Mäki-Tanila An overview on quantitative and genomic tools for utilising dominance genetic variation in improving animal production , 2008 .

[47]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[48]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[49]  W. Osburn,et al.  Evaluation of Duroc- vs. Pietrain-sired pigs for carcass and meat quality measures. , 2003, Journal of animal science.

[50]  T. Ishida,et al.  Estimation of additive and dominance genetic variances in selected layer lines. , 2000 .

[51]  K. Roeder,et al.  Genomic Control for Association Studies , 1999, Biometrics.

[52]  H. Busk,et al.  Determination of lean meat in pig carcasses with the Autofom classification system. , 1999, Meat science.

[53]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .