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利用罕见和常见功能变异进行群体结构分析。

Population structure analysis using rare and common functional variants.

作者信息

Baye Tesfaye M, He Hua, Ding Lili, Kurowski Brad G, Zhang Xue, Martin Lisa J

机构信息

Division of Asthma Research, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA.

出版信息

BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S8. doi: 10.1186/1753-6561-5-S9-S8.

Abstract

Next-generation sequencing technologies now make it possible to genotype and measure hundreds of thousands of rare genetic variations in individuals across the genome. Characterization of high-density genetic variation facilitates control of population genetic structure on a finer scale before large-scale genotyping in disease genetics studies. Population structure is a well-known, prevalent, and important factor in common variant genetic studies, but its relevance in rare variants is unclear. We perform an extensive population structure analysis using common and rare functional variants from the Genetic Analysis Workshop 17 mini-exome sequence. The analysis based on common functional variants required 388 principal components to account for 90% of the variation in population structure. However, an analysis based on rare variants required 532 significant principal components to account for similar levels of variation. Using rare variants, we detected fine-scale substructure beyond the population structure identified using common functional variants. Our results show that the level of population structure embedded in rare variant data is different from the level embedded in common variant data and that correcting for population structure is only as good as the level one wishes to correct.

摘要

新一代测序技术如今使得对个体全基因组中数十万种罕见遗传变异进行基因分型和测量成为可能。高密度遗传变异的特征分析有助于在疾病遗传学研究进行大规模基因分型之前,在更精细的尺度上控制群体遗传结构。群体结构在常见变异基因研究中是一个广为人知、普遍存在且重要的因素,但其在罕见变异中的相关性尚不清楚。我们使用遗传分析研讨会17的微外显子序列中的常见和罕见功能变异进行了广泛的群体结构分析。基于常见功能变异的分析需要388个主成分来解释群体结构中90%的变异。然而,基于罕见变异的分析需要532个显著主成分来解释相似水平的变异。使用罕见变异,我们检测到了超出使用常见功能变异所识别的群体结构之外的精细尺度亚结构。我们的结果表明,罕见变异数据中所包含的群体结构水平与常见变异数据中所包含的不同,并且对群体结构进行校正的效果取决于人们希望校正的水平。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb05/3287920/8b6bc55ea4f7/1753-6561-5-S9-S8-1.jpg

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