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大规模基于序列的隐性变异筛选可用于鉴定和监测猪中罕见的有害变异。

Large scale sequence-based screen for recessive variants allows for identification and monitoring of rare deleterious variants in pigs.

机构信息

Topigs Norsvin Research Center, 's-Hertogenbosch, the Netherlands.

Animal Breeding and Genomics, Wageningen University & Research, Wageningen, the Netherlands.

出版信息

PLoS Genet. 2024 Jan 10;20(1):e1011034. doi: 10.1371/journal.pgen.1011034. eCollection 2024 Jan.

Abstract

Most deleterious variants are recessive and segregate at relatively low frequency. Therefore, high sample sizes are required to identify these variants. In this study we report a large-scale sequence based genome-wide association study (GWAS) in pigs, with a total of 120,000 Large White and 80,000 Synthetic breed animals imputed to sequence using a reference population of approximately 1,100 whole genome sequenced pigs. We imputed over 20 million variants with high accuracies (R2>0.9) even for low frequency variants (1-5% minor allele frequency). This sequence-based analysis revealed a total of 14 additive and 9 non-additive significant quantitative trait loci (QTLs) for growth rate and backfat thickness. With the non-additive (recessive) model, we identified a deleterious missense SNP in the CDHR2 gene reducing growth rate and backfat in homozygous Large White animals. For the Synthetic breed, we revealed a QTL on chromosome 15 with a frameshift variant in the OBSL1 gene. This QTL has a major impact on both growth rate and backfat, resembling human 3M-syndrome 2 which is related to the same gene. With the additive model, we confirmed known QTLs on chromosomes 1 and 5 for both breeds, including variants in the MC4R and CCND2 genes. On chromosome 1, we disentangled a complex QTL region with multiple variants affecting both traits, harboring 4 independent QTLs in the span of 5 Mb. Together we present a large scale sequence-based association study that provides a key resource to scan for novel variants at high resolution for breeding and to further reduce the frequency of deleterious alleles at an early stage in the breeding program.

摘要

大多数有害变异是隐性的,且在相对较低的频率下分离。因此,需要大量的样本量来识别这些变异。在这项研究中,我们报告了一项大规模的基于序列的全基因组关联研究(GWAS),共有 12 万头长白猪和 8 万头合成猪通过大约 1100 头全基因组测序猪的参考群体进行序列推断。我们甚至对低频变异(1-5%的次要等位基因频率)进行了高精度(R2>0.9)的超过 2000 万个变异的推断。这种基于序列的分析总共揭示了 14 个与生长速度和背膘厚度有关的加性和 9 个非加性显著数量性状位点(QTL)。对于非加性(隐性)模型,我们在 CDHR2 基因中发现了一个有害的错义 SNP,导致长白猪纯合子生长速度和背膘降低。对于合成品种,我们在 15 号染色体上发现了一个 OBSL1 基因的移码变异 QTL。该 QTL 对生长速度和背膘都有重大影响,类似于与同一基因相关的人类 3M 综合征 2。对于加性模型,我们确认了两个品种在 1 号和 5 号染色体上的已知 QTL,包括 MC4R 和 CCND2 基因的变异。在 1 号染色体上,我们分解了一个复杂的 QTL 区域,该区域有多个变异影响两个性状,在 5 Mb 的范围内有 4 个独立的 QTL。总之,我们进行了一项大规模的基于序列的关联研究,为在高分辨率下扫描新型变异提供了关键资源,以便在早期的选育计划中进一步降低有害等位基因的频率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caf4/10805306/303d72e30244/pgen.1011034.g001.jpg

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