A haplotype-based GWAS method gain novel insights of agriculturally complex traits using a maize multi-parent synthetic population
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- 英文论文题名：A haplotype-based GWAS method gain novel insights of agriculturally complex traits using a maize multi-parent synthetic population
- 论文作者：Yingjie Xiao ; Wenyu Yang ; Jianbing Yan
- 英文论文作者：Yingjie Xiao ; Wenyu Yang ; Jianbing Yan ; National Key Laboratory of Crop Genetic Improvement ; Huazhong Agricultural University
- 年：2016
- 作者机构：National Key Laboratory of Crop Genetic Improvement,Huazhong Agricultural University;
- 英文论文关键词：Zea Mays ; multi-parent population ; haplotype method ; missing heritability
- 会议召开时间：2016-10-09
- 会议录名称：2016年全国植物生物学大会摘要集
- 语种：英文
- 分类号：S513
- 学会代码：ZGZO
- 会议名称：2016年全国植物生物学大会
- 会议地点：中国湖北武汉
- 主办单位：中国遗传学会、中国细胞生物学学会、中国植物学会、中国植物生理与植物分子生物学学会、中国作物学会
- 学会名称：中国植物学会
- 页数：1
- 文件大小：852k
- 原文格式：D
- 会议级别：全国
摘要
Maize(Zea Mays L.) is one of the most important crops globally for food,feed and fuel. Aiming to efficiently design breeding schemes,the global molecular breeders are struggling to clarify the determinants underlying agriculturally important traits,almost inherited as the ′quantitative traits′,whose genetic basis are attributed to many modest-effect loci,epistasis and interaction with environments. To tradeoff between diversity and confounding influence,we here propose a maize synthetic population,tailor-made for genetics and breeding,with firstly thoroughly intercross between 24 maize elite inbred lines(centered by ′HZS′ pedigree but genetically diverse) and subsequently sufficiently inbreeding(single-seed decent for over six generations). Up to present,we have collected an unprecedently huge dataset in this maize synthetic population: 1) NGS-based genetic variants(50M SNP,2.8M In Del and 0.66 M SV); 2) multi-environmental phenotypes(from agronomic to yield related traits). The large-scale data for synthetic population endows the popular GWAS,single-marker based linear mixed model,sufficient power for identifying specific loci or known genes. However,frustratingly,only one-half or less heritability allow to be accounted for jointly by all identified loci for any a given trait,the genetic base of complex traits remains ambiguous,also named as ′missing heritability′. Here,we provide a probabilistic estimate of mosaic structure for any a progeny in synthetic population that presumed as a reshuffle across 24 parental genomes,using a combined method of the hidden markov model(HMM) and linkage disequilibrium(LD). We developed a novel statistical methodology,′haplotype-based GWAS′,that tested allelic effect attributed to each parent state rather than the substitute effect between states in each marker,with an assumption that multiple-and independent-causal variants jointly influence trait variations in a given inherited genomic region. The haplotype-based method provides opportunities to further dissect complex traits,and potentially settle the missed heritability that never solved before solely using traditional method.
Maize(Zea Mays L.) is one of the most important crops globally for food,feed and fuel. Aiming to efficiently design breeding schemes,the global molecular breeders are struggling to clarify the determinants underlying agriculturally important traits,almost inherited as the ′quantitative traits′,whose genetic basis are attributed to many modest-effect loci,epistasis and interaction with environments. To tradeoff between diversity and confounding influence,we here propose a maize synthetic population,tailor-made for genetics and breeding,with firstly thoroughly intercross between 24 maize elite inbred lines(centered by ′HZS′ pedigree but genetically diverse) and subsequently sufficiently inbreeding(single-seed decent for over six generations). Up to present,we have collected an unprecedently huge dataset in this maize synthetic population: 1) NGS-based genetic variants(50M SNP,2.8M In Del and 0.66 M SV); 2) multi-environmental phenotypes(from agronomic to yield related traits). The large-scale data for synthetic population endows the popular GWAS,single-marker based linear mixed model,sufficient power for identifying specific loci or known genes. However,frustratingly,only one-half or less heritability allow to be accounted for jointly by all identified loci for any a given trait,the genetic base of complex traits remains ambiguous,also named as ′missing heritability′. Here,we provide a probabilistic estimate of mosaic structure for any a progeny in synthetic population that presumed as a reshuffle across 24 parental genomes,using a combined method of the hidden markov model(HMM) and linkage disequilibrium(LD). We developed a novel statistical methodology,′haplotype-based GWAS′,that tested allelic effect attributed to each parent state rather than the substitute effect between states in each marker,with an assumption that multiple-and independent-causal variants jointly influence trait variations in a given inherited genomic region. The haplotype-based method provides opportunities to further dissect complex traits,and potentially settle the missed heritability that never solved before solely using traditional method.
Maize(Zea Mays L.) is one of the most important crops globally for food,feed and fuel. Aiming to efficiently design breeding schemes,the global molecular breeders are struggling to clarify the determinants underlying agriculturally important traits,almost inherited as the ′quantitative traits′,whose genetic basis are attributed to many modest-effect loci,epistasis and interaction with environments. To tradeoff between diversity and confounding influence,we here propose a maize synthetic population,tailor-made for genetics and breeding,with firstly thoroughly intercross between 24 maize elite inbred lines(centered by ′HZS′ pedigree but genetically diverse) and subsequently sufficiently inbreeding(single-seed decent for over six generations). Up to present,we have collected an unprecedently huge dataset in this maize synthetic population: 1) NGS-based genetic variants(50M SNP,2.8M In Del and 0.66 M SV); 2) multi-environmental phenotypes(from agronomic to yield related traits). The large-scale data for synthetic population endows the popular GWAS,single-marker based linear mixed model,sufficient power for identifying specific loci or known genes. However,frustratingly,only one-half or less heritability allow to be accounted for jointly by all identified loci for any a given trait,the genetic base of complex traits remains ambiguous,also named as ′missing heritability′. Here,we provide a probabilistic estimate of mosaic structure for any a progeny in synthetic population that presumed as a reshuffle across 24 parental genomes,using a combined method of the hidden markov model(HMM) and linkage disequilibrium(LD). We developed a novel statistical methodology,′haplotype-based GWAS′,that tested allelic effect attributed to each parent state rather than the substitute effect between states in each marker,with an assumption that multiple-and independent-causal variants jointly influence trait variations in a given inherited genomic region. The haplotype-based method provides opportunities to further dissect complex traits,and potentially settle the missed heritability that never solved before solely using traditional method.
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