Genome-wide Association Studies SNPs Research Articles

A quality control algorithm for filtering SNPs in genome-wide association studies

A quality control algorithm for filtering SNPs in genome-wide association studies

Genome wide association study SNPs in the human genome diversity project samples: does selection affect unlinked SNPs with shared trait associations?

Genome wide association study SNPs in the human genome diversity project samples: does selection affect unlinked SNPs with shared trait associations?

Genome wide association study SNPs in the human genome diversity project samples: does selection affect unlinked SNPs with shared trait associations?

Genome wide association study SNPs in the human genome diversity project samples: does selection affect unlinked SNPs with shared trait associations?

Investigation of the fine structure of European populations with applications to disease association studies

An investigation into fine-scale European population structure was carried out using high-density genetic variation on nearly 6000 individuals originating from across Europe. The individuals were collected as control samples and were genotyped with more than 300 000 SNPs in genome-wide association studies using the Illumina Infinium platform. A major East-West gradient from Russian (Moscow) samples to Spanish samples was identified as the first principal component (PC) of the genetic diversity. The second PC identified a North-South gradient from Norway and Sweden to Romania and Spain. Variation of frequencies at markers in three separate genomic regions, surrounding LCT, HLA and HERC2, were strongly associated with this gradient. The next 18 PCs also accounted for a significant proportion of genetic diversity observed in the sample. We present a method to predict the ethnic origin of samples by comparing the sample genotypes with those from a reference set of samples of known origin. These predictions can be performed using just summary information on the known samples, and individual genotype data are not required. We discuss issues raised by these data and analyses for association studies including the matching of case-only cohorts to appropriate pre-collected control samples for genome-wide association studies.

The value of gene-based selection of tag SNPs in genome-wide association studies

Genome-wide association scans are rapidly becoming reality, but there is no present consensus regarding genotyping strategies to optimise the discovery of true genetic risk factors. For a given investment in genotyping, should tag SNPs be selected in a gene-centric manner, or instead, should coverage be optimised based on linkage disequilibrium alone? We explored this question using empirical data from the HapMap-ENCODE project, and we found that tags designed specifically to capture common variation in exonic and evolutionarily conserved regions provide good coverage for 15-30% of the total common variation (depending on the population sample studied), and yield genotype savings compared with an anonymous tagging approach that captures all common variation. However, the same number of tags based on linkage disequilibrium alone captures substantially more (30-46%) of the total common variation. Therefore, the best strategy depends crucially on the unknown degree to which functional variation resides in recognisable exons and evolutionarily conserved sequence. A hypothetical but reasonable scenario might be one in which trait-causing variation is equally distributed between exons plus conserved sequence, and the rest of the genome. In this scenario, our analysis suggests that a tagging approach that captures variation in exons and conserved sequence provides only modestly better coverage of putatively causal variation than does anonymous tagging. In HapMap CEU samples (with northern and western European ancestry), we observed roughly equivalent coverage for equal investment for both tagging strategies.

Linkage disequilibrium sharing and haplotype-tagged SNP portability between populations.

The discovery of the block-like structure of linkage disequilibrium (LD) in human populations holds the promise of delineating the etiology of common diseases. However, understanding the magnitude, mechanism, and utility of between-population LD sharing is critical for future genome-wide association studies. In this study, substantial LD sharing between six non-African populations was observed, although much less between African-American and non-African, based on 20,000 SNPs of chromosome 21. We also demonstrated the respective roles of recombination and demographic events in shaping LD sharing. Furthermore, we showed that the haplotype-tagged SNPs chosen from one population are portable to the others in East Asia. Therefore, we concluded that the magnitude of LD sharing between human populations justifies the use of representative populations for selecting haplotype-tagged SNPs in genome-wide association studies of complex diseases.

Genome-wide association studies: theoretical and practical concerns

To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors - including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases - that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.