Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

Several families have been reported with autosomal-dominant frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), genetically linked to chromosome 9p21. Here, we report an expansion of a noncoding GGGGCC hexanucleotide repeat in the gene C9ORF72 that is strongly associated with disease in a large FTD/ALS kindred, previously reported to be conclusively linked to chromosome 9p. This same repeat expansion was identified in the majority of our families with a combined FTD/ALS phenotype and TDP-43-based pathology. Analysis of extended clinical series found the C9ORF72 repeat expansion to be the most common genetic abnormality in both familial FTD (11.7%) and familial ALS (23.5%). The repeat expansion leads to the loss of one alternatively spliced C9ORF72 transcript and to formation of nuclear RNA foci, suggesting multiple disease mechanisms. Our findings indicate that repeat expansion in C9ORF72 is a major cause of both FTD and ALS. Copyright © 2011 Elsevier Inc. All rights reserved.

Summary Through 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.