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Genomic and geographic distribution of private SNPs and pathways in human populations.

ABSTRACT: AIMS: Geography-based genetic differentials operating on entire biochemical pathways may reflect different adaptive evolutionary processes that separated populations may have undergone. They may also influence treatment outcome for a variety of drugs - an emerging and important area of study. This research article leverages the International HapMap Consortium data to identify pathway components that differ in genotype frequency for four populations: individuals of Northern European descent from the USA (CEU), individuals from West Africa (YRI), Japan (JPT) and China (CHB). MATERIALS #ENTITYSTARTX00026; METHODS: By identifying loci with fixed or large frequency differences (? = 1) between paired population samples (CEU vs YRI, CEU vs CHB, CEU vs JPT, YRI vs CHB, YRI vs JPT and CHB vs JPT), and reconstructing the physiological functions of genes at these loci, we report a list of pathways affected by natural selection during human evolution. RESULTS: Of the 3.7 million HapMap SNPs, 463 loci (which mapped to 38 genes) were fixed (? = 1) in at least one population pair. These private loci included four nonsynonymous coding SNPs: rs4536103 (NEUROG3), rs1385699 (EDA2R), rs11946338 (ARHGAP24) and rs4422842 (CACNA1B). A total of four additional genes demonstrated evidence of recent positive selection: three genes in European subjects (IER5L, NPNT and SESTD1) and a single gene in Asian subjects (EXOC6B). DISCUSSION: Gene ontology and pathway analyses suggest that cellular differentiation, apoptosis and activation of the NF-?B transcription factor vary between populations in genomic regions of fixed (private) SNPs identified in this study. Variability in these pathways may provide important clues into the mechanisms of human adaptation to different environments. An improved understanding of their variability may also help to explain race-specific differences in the treatment outcomes observed for a variety of modern drugs.

SUBMITTER: Baye TM

PROVIDER: S-EPMC2843937 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Genomic and geographic distribution of private SNPs and pathways in human populations.

Baye Tesfaye M TM Wilke Russell A RA Olivier Michael M

Personalized medicine 20091101 6

AIMS: Geography-based genetic differentials operating on entire biochemical pathways may reflect different adaptive evolutionary processes that separated populations may have undergone. They may also influence treatment outcome for a variety of drugs - an emerging and important area of study. This research article leverages the International HapMap Consortium data to identify pathway components that differ in genotype frequency for four populations: individuals of Northern European descent from ...[more]

PMID: 20352079

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Project description:Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm. We validate our methods on the HapMap populations and achieve perfect intercontinental differentiation with 14 PCA-correlated SNPs. The Chinese and Japanese populations can be easily differentiated using less than 100 PCA-correlated SNPs ascertained after evaluating 1.7 million SNPs from HapMap. We show that, in general, structure informative SNPs are not portable across geographic regions. However, we manage to identify a general set of 50 PCA-correlated SNPs that effectively assigns individuals to one of nine different populations. Compared to analysis with the measure of informativeness, our methods, although unsupervised, achieved similar results. We proceed to demonstrate that our algorithm can be effectively used for the analysis of admixed populations without having to trace the origin of individuals. Analyzing a Puerto Rican dataset (192 individuals, 7,257 SNPs), we show that PCA-correlated SNPs can be used to successfully predict structure and ancestry proportions. We subsequently validate these SNPs for structure identification in an independent Puerto Rican dataset. The algorithm that we introduce runs in seconds and can be easily applied on large genome-wide datasets, facilitating the identification of population substructure, stratification assessment in multi-stage whole-genome association studies, and the study of demographic history in human populations.

Dataset Information

Genomic and geographic distribution of private SNPs and pathways in human populations.

Publications

Genomic and geographic distribution of private SNPs and pathways in human populations.

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