An integrated map of genetic variation from 1,092 human genomes

Abstract

SummaryThrough characterising the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help understand the genetic contribution to disease. We describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methodologies to integrate information across multiple algorithms and diverse data sources we provide a validated haplotype map of 38 million SNPs, 1.4 million indels and over 14 thousand larger deletions. We show that individuals from different populations carry different profiles of rare and common variants and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways and that each individual harbours hundreds of rare non-coding variants at conserved sites, such as transcription-factor-motif disrupting changes. This resource, which captures up to 98% of accessible SNPs at a frequency of 1% in populations of medical genetics focus, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.

Genetic effects on gene expression across human tissues

“…Genotypes were phased and imputed with SHAPEIT2 68 and IMPUTE2 69 , respectively, using multi-ethnic panel reference from 1000 Genomes Project Phase 3 70 . Variants were excluded from analysis if they: 1) had a call rate < 95%; 2) had minor allele frequencies < 1%; 3) deviated from Hardy–Weinberg equilibrium ( P <1.0×10 −6 ); or 4) had an imputation info score <0.4.…”

Across language families: Genome diversity mirrors linguistic variation withinEurope

“…To determine the geographic location that best represents each individual's ancestry, we used a strict criterion of sample selection excluding individuals who reported mixed grandparental ancestry. A Basque (Henn et al, 2012) and a Finnish (1000 Genomes Project Consortium, 2012) sample were then added (Fig. 1).…”

Assessment of first and second degree relatives of individuals with bipolar disorder shows increased genetic risk scores in both affected relatives and young At‐Risk Individuals

“…Weighted‐additive polygenic risk scores were computed using the score function in PLINK [Purcell et al, 2007]. Scores were weighted either by: (1) the effect size of each SNP, as determined by the odds ratio of the risk allele from the original discovery GWAS study by Sklar et al [2011]; or (2) by the PGC risk allele frequency from appropriate ethnic group to derive ethnicity‐specific PRS, using allele frequencies determined the phase 1 of the 1000 genomes project [Abecasis et al, 2012]. Frequencies derived from CEPH individuals (CEU) were applied to people of European ancestry, frequencies from individuals of African ancestry from south west US (ASW) were applied to African‐American individuals, and frequencies from Han Chinese individuals from Beijing (CHB) were applied to individuals of Asian ancestry.…”