Dataset Information

Strategies for fine-mapping complex traits.

ABSTRACT: Genome-wide association studies (GWAS) have identified thousands of robust and replicable genetic associations for complex disease. However, the identification of the causal variants that underlie these associations has been more difficult. This problem of fine-mapping association signals predates GWAS, but the last few years have seen a surge of studies aimed at pinpointing causal variants using both statistical evidence from large association data sets and functional annotations of genetic variants. Combining these two approaches can often determine not only the causal variant but also the target gene. Recent contributions include analyses of custom genotyping arrays, such as the Immunochip, statistical methods to identify credible sets of causal variants and the addition of functional genomic annotations for coding and non-coding variation to help prioritize variants and discern functional consequence and hence the biological basis of disease risk.

SUBMITTER: Spain SL

PROVIDER: S-EPMC4572002 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Strategies for fine-mapping complex traits.

Spain Sarah L SL Barrett Jeffrey C JC

Human molecular genetics 20150708 R1

Genome-wide association studies (GWAS) have identified thousands of robust and replicable genetic associations for complex disease. However, the identification of the causal variants that underlie these associations has been more difficult. This problem of fine-mapping association signals predates GWAS, but the last few years have seen a surge of studies aimed at pinpointing causal variants using both statistical evidence from large association data sets and functional annotations of genetic var ...[more]

PMID: 26157023

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Project description:BACKGROUND:Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data. RESULTS:We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of Group-specific Component (GC) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C (CCDC88C) expression in CD8 cells and association with cow livability. CONCLUSIONS:Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.

Dataset Information

Strategies for fine-mapping complex traits.

Publications

Strategies for fine-mapping complex traits.

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