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An imputed whole-genome sequence-based GWAS approach pinpoints causal mutations for complex traits in a specific swine population

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Science China(Life Sciences) 2022年第4期65卷 781-794页

作者： Guorong Yan Xianxian Liu Shijun Xiao Wenshui Xin Wenwu Xu Yiping Li Tao Huang Jiangtao Qin Lei Xie Junwu Ma Zhiyan Zhang Lusheng Huang State Key Laboratory for Pig Genetic Improvement and Production Technology Jiangxi Agricultural UniversityNanchang330045China Institute of Photomedicine Shanghai Skin Disease HospitalSchool of MedicineTongji UniversityShanghai200092China

Sequencing-based genome-wide association studies(GWAS) have facilitated the identification of causal associations between genetic variants and traits in diverse species. However, it is cost-prohibitive for the majority of research groups to sequence a large number of samples. Here, we carried out genotype imputation to increase the density of single nucleotide polymorphisms in a large-scale Swine F;population using a reference panel including 117 individuals, followed by a series of GWAS analyses. The imputation accuracies reached 0.89 and 0.86 for allelic concordance and correlation, respectively. A quantitative trait nucleotide(QTN) affecting the chest vertebrate was detected directly, while the investigation of another QTN affecting the residual glucose failed due to the presence of similar haplotypes carrying wild-type and mutant allelesin the reference panel used in this study. A high imputation accuracy was confirmed by Sanger sequencing technology for the most significant loci. Two candidate genes,CPNE5 and MYH3, affecting meat-related traits were proposed. Collectively, we illustrated four scenarios in imputation-based GWAS that may be encountered by researchers, and our results will provide an extensive reference for future genotype imputation-based GWAS analyses in the future.

关键词： genotype imputation GWAS Fine mapping causal mutation pig

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inGAP-family:Accurate Detection of Meiotic Recombination Loci and causal mutations by Filtering Out Artificial Variants due to Genome Complexities

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 524-535页

作者： Qichao Lian Yamao Chen Fang Chang Ying Fu Ji Qi State Key Laboratory of Genetic Engineering Institute of Plant BiologySchool of Life SciencesFudan UniversityShanghai 200433China

Accurately identifying DNA polymorphisms can bridge the gap between phenotypes and genotypes and is essential for molecular marker assisted genetic studies.Genome complexities,including large-scale structural variations,bring great challenges to bioinformatic analysis for obtaining high-confidence genomic variants,as sequence differences between non-allelic loci of two or more genomes can be misinterpreted as polymorphisms.It is important to correctly filter out artificial variants to avoid false genotyping or estimation of allele frequencies.Here,we present an efficient and effective framework,inGAP-family,to discover,filter,and visualize DNA polymorphisms and structural variants(SVs)from alignment of short reads.Applying this method to polymorphism detection on real datasets shows that elimination of artificial variants greatly facilitates the precise identification of meiotic recombination points as well as causal mutations in mutant genomes or quantitative trait loci.In addition,inGAP-family provides a user-friendly graphical interface for detecting polymorphisms and SVs,further evaluating predicted variants and identifying mutations related to genotypes.It is accessible at https://***/projects/ingap-family/.

关键词： inGAP-family Genomic variation Structural variation Meiotic analysis Genetic mapping causal mutation

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