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Multimarker and rare variants genomewide association studies for bone weight in Simmental cattle
Author(s) -
Miao J.,
Wang X.,
Bao J.,
Jin S.,
Chang T.,
Xia J.,
Yang L.,
Zhu B.,
Xu L.,
Zhang L.,
Gao X.,
Chen Y.,
Li J.,
Gao H.
Publication year - 2018
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12326
Subject(s) - snp , single nucleotide polymorphism , biology , genetic association , genome wide association study , minor allele frequency , association test , genetics , lasso (programming language) , allele , gene , bonferroni correction , genotype , statistics , mathematics , world wide web , computer science
Summary Bone weight, defined as the total weight of the bones in all the forequarter and hindquarter joints, can reflect somebody conformation traits and skeletal diseases. To gain a better understanding of the genetic determinants of bone weight, we used a composite strategy including multimarker and rare‐marker association to perform genomewide association studies ( GWAS ) for that character in Simmental cattle. Our strategy consisted of three models: (i) A traditional linear mixed model ( LMM ) was applied ( Q + K ‐ LMM ); (ii) single nucleotide polymorphisms ( SNP s) with p ‐values less than .05 from the LMM were selected to undergo the least absolute shrinkage and selector operator (Lasso) in the second stage ( LMM ‐Lasso); (iii) genes containing two or more rare SNP s were examined by performing the sequence kernel association test (gene‐based SKAT ). A total of 1,225 cattle were genotyped with an Illumina Bovine HD BeadChip containing 770,000 SNP s. After the quality‐control procedures, 1,217 individuals with 608,696 common SNP s and 105,787 rare SNP s (with 0.001 < minor allele frequency [MAF] <0.05) remained in the sample for analysis. A traditional LMM successfully mapped three genes associated with bone weight, while LMM ‐Lasso identified nine genes, which included all genes found by traditional LMM . Only a single gene, EPHB 3 , surpassed the significance threshold after Bonferroni correction in gene‐based SKAT . In conclusion, based on functional annotation and results from previous endeavours, we believe that LCORL , RIMS 2 , LAP 3 , PRKAR 2B , CHSY 1 , MAP 2K6 and EPHB 3 are candidate genes for bone weight. In general, such a comprehensive strategy for GWAS may be useful for researchers seeking to probe the full genetic architecture underlying economic traits in livestock.