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Rare variant density across the genome and across populations

机译:整个基因组和整个群体的稀有变异密度

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Next-generation sequencing allows for a new focus on rare variant density for conducting analyses of association to disease and for narrowing down the genomic regions that show evidence of functionality. In this study we use the 1000 Genomes Project pilot data as distributed by Genetic Analysis Workshop 17 to compare rare variant densities across seven populations. We made the comparisons using regressions of rare variants on total variant counts per gene for each population and Tajima’s D values calculated for each gene in each population, using data on 3,205 genes. We found that the populations clustered by continent for both the regression slopes and Tajima’s D values, with the African populations (Yoruba and Luhya) showing the highest density of rare variants, followed by the Asian populations (Han and Denver Chinese followed by the Japanese) and the European populations (CEPH [European-descent] and Tuscan) with the lowest densities. These significant differences in rare variant densities across populations seem to translate to measures of the rare variant density more commonly used in rare variant association analyses, suggesting the need to adjust for ancestry in such analyses. The selection signal was high for AHNAK , HLA-A , RANBP2 , and RGPD4 , among others. RANBP2 and RGPD4 showed a marked difference in rare variant density and potential selection between the Luhya and the other populations. This may suggest that differences between populations should be considered when delimiting genomic regions according to functionality and that these differences can create potential for disease heterogeneity.
机译:下一代测序使人们对稀有变异密度有了新的关注,以进行与疾病关联的分析并缩小显示功能证据的基因组区域。在这项研究中,我们使用了基因分析研讨会17分发的1000个基因组计划试点数据,比较了七个种群的稀有变异密度。我们使用稀有变体对每个种群每个基因的总变异数进行回归,并使用3205个基因的数据对每个种群中每个基因计算的田岛D值进行了比较。我们发现,在回归斜率和田岛D值上,各洲的种群都聚集在一起,其中非洲种群(约鲁巴和卢赫亚)的稀有变种密度最高,其次是亚洲种群(汉族和丹佛华人,其次是日本人)以及密度最低的欧洲人口(CEPH(欧洲裔)和托斯卡纳)。人群中稀有变异体密度的这些显着差异似乎可以转化为对稀有变异体关联分析中更常用的稀有变异体密度的度量,这表明需要在此类分析中调整血统。 AHNAK,HLA-A,RANBP2和RGPD4等的选择信号较高。 RANBP2和RGPD4在Luhya和其他种群之间的稀有变异密度和潜在选择上显示出显着差异。这可能表明在根据功能划分基因组区域时应考虑人群之间的差异,并且这些差异可能会造成疾病异质性。

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