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Pathway analysis of seven common diseases assessed by genome-wide association.

ABSTRACT: Recent genome-wide association studies (GWAS) have identified DNA sequence variations that exhibit unequivocal statistical associations with many common chronic diseases. However, the vast majority of these studies identified variations that explain only a very small fraction of disease burden in the population at large, suggesting that other factors, such as multiple rare or low-penetrance variations and interacting environmental factors, are major contributors to disease susceptibility. Identifying multiple low-penetrance variations (or "polygenes") contributing to disease susceptibility will be difficult. We present a pathway analysis approach to characterizing the likely polygenic basis of seven common diseases using the Wellcome Trust Case Control Consortium (WTCCC) GWAS results. We identify numerous pathways implicated in disease predisposition that would have not been revealed using standard single-locus GWAS statistical analysis criteria. Many of these pathways have long been assumed to contain polymorphic genes that lead to disease predisposition. Additionally, we analyze the genetic relationships between the seven diseases, and based upon similarities with respect to the associated genes and pathways affected in each, propose a new way of categorizing the diseases.

SUBMITTER: Torkamani A

PROVIDER: S-EPMC2602835 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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Pathway analysis of seven common diseases assessed by genome-wide association.

Torkamani Ali A Topol Eric J EJ Schork Nicholas J NJ

Genomics 20080916 5

Recent genome-wide association studies (GWAS) have identified DNA sequence variations that exhibit unequivocal statistical associations with many common chronic diseases. However, the vast majority of these studies identified variations that explain only a very small fraction of disease burden in the population at large, suggesting that other factors, such as multiple rare or low-penetrance variations and interacting environmental factors, are major contributors to disease susceptibility. Identi ...[more]

PMID: 18722519

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Project description:There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined approximately 2,000 individuals for each of 7 major diseases and a shared set of approximately 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 x 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10(-5) and 5 x 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.

Project description:The grain hardness index (HI) is one of the important reference bases for wheat quality and commodity properties; therefore, it is essential and useful to identify loci associated with the HI in wheat breeding. The grain hardness index of the natural population including 150 common wheat genotypes was measured in this study. The phenotypic data diversity of HI based on four environments and the best linear unbiased prediction (BLUP) was analyzed. The results showed that the grain HI of the natural population ranged from 15.00 to 83.00, the variation range was from 5.10% to 24.44%, and the correlation coefficient was 0.872-0.980. BLUP value was used to grade and assign the grain HI to hard wheat, mixed wheat, and soft wheat, and the assigned phenotypes were used for genome-wide association analysis. Two types of grain hardness index phenotypic values were used for genome-wide association analysis (GWAS) using a 55K SNP array. A total of five significant association loci (p < 0.001) were excavated, among which four loci could be detected in three or more environments. They were distributed on chromosomes 1A and 7D, and the phenotypic contribution rate was 7.52% to 10.66%. A total of 48 sites related to grain hardness were detected by the assignment method, among which five were stable genetic sites, distributed on chromosomes 1A(2), 3B(1), 4B(1), and 7D(1), with phenotypic contribution rates ranging from 7.63% to 11.12%. Of the five loci detected by the assignment method, two stable loci were co-located in the phenotypic mapping results of the hardness index. One of the loci was consistent with previous reports and located on chromosome 1A, and one locus was unreported on chromosome 7D. Therefore, it may be a feasible attempt to use the assignment method to conduct genome-wide association analysis of the grain hardness index. In this study, a total of five genetic loci for grain hardness stability were excavated, and two of the loci were located in the two phenotypic values, two of which were not reported.

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Pathway analysis of seven common diseases assessed by genome-wide association.

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Pathway analysis of seven common diseases assessed by genome-wide association.

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