Characterization of Normal Genomic Variability

使用来自全球31个不同人群的500多个样本，我们对55万个位点进行了非常密集的全基因组单核苷酸多态性（SNP）基因分型。我们分析了这些数据以及基因型、单倍型和拷贝数变异在人群中的分布。我们表明，这些数据能够将个体分配到种群中，并且由此得出的预测支持对种群结构的精细推断。随着与非洲地理距离的增加，观察到连锁不平衡的增加，正如在人类种群向非洲外传播的连续创始人效应下所预期的那样。我们扩展了这项工作，利用这些群体的数据来确定未知基因型的插入是否可行，以及预测的最佳方法。这方面的工作现已扩大到包括撒哈拉以南非洲的许多次人口，特别是桑人。

Using more than 500 samples from 31 distinct worldwide human populations we performed very dense genome wide single nucleotide polymorphism (SNP) genotyping at 550,000 loci. We analyzed these data and the distribution of genotypes, haplotypes and copy number variants across populations. We showed that these data were able to assign individuals to populations and that the resulting predictions supported fine-scale inferences about population structure. Increasing linkage disequilibrium was observed with increasing geographic distance from Africa, as expected under a serial founder effect for the out-of-Africa spread of human populations. We extended upon this work to use the data from these populations to determine whether imputation of unknown genotypes is feasible and the best approach to this prediction. This particular aspect of work has now been expanded to include numerous sub-populations in sub-saharan Africa, particularly from the people of the San. To understand the effects of genetic variability on DNA methylation we have performed genome wide genotyping, exome sequencing and epigenome wide DNA methylation typing in 500 brain samples. These data show a striking effect of genetic variation on DNA methylation levels and show clearly that such variation is likely to be physically close the the DNA methylation site under influence. Further we show that these effects are generally consistent across tissues, although there are some notable exceptions to this noted as tissue specific methylation Quantitative Trait Loci. We have extended these analyses to reveal age related DNA methylation changes that occur across various tissues, including brain. We are still involved in ongoing work to perform a more dense genetic and epigenetic survey of these tissues, including exome sequencing, assay of 500,000 DNA methylation sites and mRNA sequencing. Further we are generating RNAseq data with the aim of integrating these data with the genetic and epigenetic data in order to better understand genetic control of transcription. In addition we have performed resequencing of disease related loci in these samples in an effort to understand the effects of rare genetic variability on biological traits. Most recently this work has included the generation of whole genome sequence.