Characterization of Normal Genomic Variability

我们在上一阶段进行了两组独立但互补的实验；第一个了解全球人口基因组变异的范围和分布；第二，除了CpG甲基化外，表征人脑中这种变异性对mRNA和miRNA表达的影响。

We have performed two separate but complementary sets of experiments in the last period; the first to understand the scope and distribution of genomic variability on worldwide human populations; the second, to characterize the effects of such variability in human brain on expression of mRNA and miRNA in addition to CpG methylation. 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. Our results produce new inferences about inter-population variation, support the utility of CNVs in human population-genetic research, and serve as a genomic resource for human-genetic studies in diverse worldwide populations. In addition we have defined the nature of a large inversion polymorphism that occurs in distinct human populations and is thought to be under positive selection (it is suggested that this is in response to infectious disease). Gene expression influences normal brain development and function as well as propensity to some neurological diseases. The expression levels of mRNAs are controlled at multiple levels, including polymorphic sequence variants in DNA, genomic modifications such as methylation, and the expression of microRNAs (miRNAs). Understanding how multiple layers of control interact in a complex and heterogenous tissue such as the brain is challenging but can be addressed using high throughput techniques designed at capturing large amounts of information for each level of variability. In the current study, we have used a panel of four brain regions (frontal and temporal cortex, cerebellum and pons) from 150 neurologically normal individuals. For each individual, we genotyped 500,000 single nucleotide polymorphisms (SNPs) and measured DNA methylation at 27,000 CpG sites in all four brain regions. We also measured expression of 740 microRNA and 22,500 polyadenylated RNA transcripts. In this way, we will capture information on variability in mRNA expression based on two major variables of genotype and brain region, with methylation status and miRNA expression as modifiers. For initial analysis, we have used unsupervised clustering to focus on differences between brain regions. Brain regions coud be fully separated by examining any of CpG methylation, miRNA and polyA RNA expression. The separation by the different RNA measures is especially interesting, given that only 350-400 miRNA species but 13-14,000 polyA RNA were reliably detected in different brain regions. This result implies that miRNAs exert relatively large effects influencing RNA expression networks. Ongoing analysis involves these datasets with genotyping to identify how normal genetic variation within the human population affects expression.