Dataset Information

Familial cosegregation of rare genetic variants with disease in complex disorders.

ABSTRACT: Family-based designs are increasingly being used for identification of rare variants in complex disorders. This paper addresses two questions related to the utility of these designs. First, under what circumstances are rare disease-related variants expected to cosegregate with disease in families? Second, under what circumstances is a disease-variant association expected to be greater in studies restricted to familial cases than in studies of unselected cases? To investigate these questions, we developed a probability model of disease causation involving two loci. To address cosegregation, we examined the probability that an affected first-degree relative of a variant-carrying proband would also carry the variant. We find that this probability increases with increasing odds ratio (OR) for the variant, but declines with increasing sibling recurrence risk ratio (λs). For example, under reasonable assumptions, the 15q13.3 microdeletion in idiopathic generalized epilepsy, with an OR estimate of 68 in large case-control studies, is expected to be present in >95% of affected first-degree relatives of variant-carrying probands. However, for a variant with OR=5, the probability an affected relative has the variant ranges from 82% (when λs=2) to 58% (when λs=50). We also find that restriction of a study to familial cases does not necessarily increase a rare variant's association with disease, especially if λs is high and the variant contributes little to overall disease familial aggregation. These findings provide guidance for the design of family-based studies of rare variants in complex disorders.

SUBMITTER: Helbig I

PROVIDER: S-EPMC3598309 | biostudies-other | 2013 Apr

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BackgroundWith the expanding use of next-gen sequencing (NGS) to diagnose the thousands of rare Mendelian genetic diseases, it is critical to be able to interpret individual DNA variation. To calculate the significance of finding a rare protein-altering variant in a given gene, one must know the frequency of seeing a variant in the general population that is at least as damaging as the variant in question.MethodsWe developed a general method to better interpret the likelihood that a rare variant is disease causing if observed in a given gene or genic region mapping to a described protein domain, using genome-wide information from a large control sample. Based on data from 2504 individuals in the 1000 Genomes Project dataset, we calculated the number of individuals who have a rare variant in a given gene for numerous filtering threshold scenarios, which may be used for calculating the significance of an observed rare variant being causal for disease. Additionally, we calculated mutational burden data on the number of individuals with rare variants in genic regions mapping to protein domains.ResultsWe describe methods to use the mutational burden data for calculating the significance of observing rare variants in a given proportion of sequenced individuals. We present SORVA, an implementation of these methods as a web tool, and we demonstrate application to 20 relevant but diverse next-gen sequencing studies. Specifically, we calculate the statistical significance of findings involving multi-family studies with rare Mendelian disease and a large-scale study of a complex disorder, autism spectrum disorder. If we use the frequency counts to rank genes based on intolerance for variation, the ranking correlates well with pLI scores derived from the Exome Aggregation Consortium (ExAC) dataset (ρ = 0.515), with the benefit that the scores are directly interpretable.ConclusionsWe have presented a strategy that is useful for vetting candidate genes from NGS studies and allows researchers to calculate the significance of seeing a variant in a given gene or protein domain. This approach is an important step towards developing a quantitative, statistics-based approach for presenting clinical findings.

Dataset Information

Familial cosegregation of rare genetic variants with disease in complex disorders.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets