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Functional mapping of disease susceptibility loci using cell biology.

ABSTRACT: In most genome-wide linkage studies, implication of a causative disease gene often requires years of expanding the study to more families and finer mapping of the initially described region. Even after such efforts, unobtainable sample sizes can be required to make statistically meaningful conclusions about a single gene. Here we demonstrate that by adding a layer of functional biology to statistical genetic results, this process can be accelerated. The diabetes susceptibility locus (chromosome 18p11) was systematically dissected by using a cell-based secretion assay and RNA interference, and we identified laminin alpha1 to have a role in pancreatic beta cell secretion. The screen was extended to identify laminin receptor 1 as a functional partner in regards to beta cell function. Our approach can potentially be widely used in the setting of high-throughput cellular screening of other loci to identify candidate genes.

SUBMITTER: Antinozzi PA

PROVIDER: S-EPMC1533777 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature

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Functional mapping of disease susceptibility loci using cell biology.

Antinozzi Peter A PA Garcia-Diaz Alejandro A Hu Chuan C Rothman James E JE

Proceedings of the National Academy of Sciences of the United States of America 20060228 10

In most genome-wide linkage studies, implication of a causative disease gene often requires years of expanding the study to more families and finer mapping of the initially described region. Even after such efforts, unobtainable sample sizes can be required to make statistically meaningful conclusions about a single gene. Here we demonstrate that by adding a layer of functional biology to statistical genetic results, this process can be accelerated. The diabetes susceptibility locus (chromosome ...[more]

PMID: 16537450

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Project description:BackgroundGenetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study's analysis plan.ResultsWe developed a Bayesian statistical model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super-track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non-informative predictors and evaluated the model's ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP's presence in the GC. Further, using data from a genome-wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome-wide scale and improves power to detect associations.ConclusionsWe show how diverse functional annotations can be efficiently combined to create 'functional signatures' that predict the a priori odds of a variant's association to a trait and how these signatures can be integrated into a standard genome-wide-scale association analysis, resulting in improved power to detect truly associated variants.

Dataset Information

Functional mapping of disease susceptibility loci using cell biology.

Publications

Functional mapping of disease susceptibility loci using cell biology.

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