AUTHOR=Yan Guorong , Guo Tianfu , Xiao Shijun , Zhang Feng , Xin Wenshui , Huang Tao , Xu Wenwu , Li Yiping , Zhang Zhiyan , Huang Lusheng 

TITLE=Imputation-Based Whole-Genome Sequence Association Study Reveals Constant and Novel Loci for Hematological Traits in a Large-Scale Swine F2 Resource Population

JOURNAL=Frontiers in Genetics

VOLUME=Volume 9 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2018.00401

DOI=10.3389/fgene.2018.00401

ISSN=1664-8021

ABSTRACT=The whole-genome sequences of progenies with low-density single nucleotide polymorphism (SNP) genotypes can be imputed with high accuracy based on the deep coverage sequenced key ancestors. With this imputation technology, a more powerful genome-wide association study (GWAS) can be carried out using imputed whole-genome genetic variants and the interested phenotypes to overcome the shortage of low power detection and large confidence interval of low-density SNP markers in classic association studies. In this study, 19 ancestors of a large-scale swine F2 White Duroc × Erhualian population were deep sequenced for their genome with an average coverage > 25X. Combining with 98 pigs from 10 different breeds with high-quality deep sequenced genomes, we imputed the whole genomic variants of 1020 F2 pigs genotyped by ProcineSNP60 BeadChip with high accuracy and we obtained 14,851,440 sequence variants after quality control. Based on this, 87 novel quantitative traits loci (QTLs) for 18 hematological traits at three different physiological stages of the F2 pigs were identified, among which most of the novel QTLs have been repeated in two of the three stages. Further analysis pinpointed the FGF14 and LCLAT1 gene at SSC11 and SSC3 may affect the MCH at day 240 and MCV at day 18, respectively. The present study shows that combing high-quality imputed genome variants and correlated phenome traits into GWAS can improve the QTL detection power significantly. The large number of different QTLs for hematological traits identified at multiple growth stages implies the complexity and time specificity of these traits.