Dataset Information

Calculating the statistical significance of rare variants causal for Mendelian and complex disorders.

ABSTRACT: BACKGROUND:With 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. METHODS:We 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. RESULTS:We 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 (??=?0.515), with the benefit that the scores are directly interpretable. CONCLUSIONS:We 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.

SUBMITTER: Rao AR

PROVIDER: S-EPMC6001062 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

Calculating the statistical significance of rare variants causal for Mendelian and complex disorders.

Rao Aliz R AR Nelson Stanley F SF

BMC medical genomics 20180613 1

<h4>Background</h4>With 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.<h4>Methods</h4>We developed a general method to better interpret the likelihood t ...[more]

PMID: 29898714

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Project description:BackgroundCausal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing.ResultsFirst, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causal graphs; previous work on graph randomization has studied undirected graphs and directed but unsigned graphs.ConclusionWe present algorithmic solutions to two statistical significance questions necessary to apply the causal graph methodology, a powerful tool for biological network analysis. The algorithms we present are both fast and provably correct. Our work may be of independent interest in non-biological contexts as well, as it generalizes mathematical results that have been studied extensively in other fields.

Project description:ImportanceClinical exome sequencing (CES) is rapidly becoming a common molecular diagnostic test for individuals with rare genetic disorders.ObjectiveTo report on initial clinical indications for CES referrals and molecular diagnostic rates for different indications and for different test types.Design, setting, and participantsClinical exome sequencing was performed on 814 consecutive patients with undiagnosed, suspected genetic conditions at the University of California, Los Angeles, Clinical Genomics Center between January 2012 and August 2014. Clinical exome sequencing was conducted as trio-CES (both parents and their affected child sequenced simultaneously) to effectively detect de novo and compound heterozygous variants or as proband-CES (only the affected individual sequenced) when parental samples were not available.Main outcomes and measuresClinical indications for CES requests, molecular diagnostic rates of CES overall and for phenotypic subgroups, and differences in molecular diagnostic rates between trio-CES and proband-CES.ResultsOf the 814 cases, the overall molecular diagnosis rate was 26% (213 of 814; 95% CI, 23%-29%). The molecular diagnosis rate for trio-CES was 31% (127 of 410 cases; 95% CI, 27%-36%) and 22% (74 of 338 cases; 95% CI, 18%-27%) for proband-CES. In cases of developmental delay in children (<5 years, n = 138), the molecular diagnosis rate was 41% (45 of 109; 95% CI, 32%-51%) for trio-CES cases and 9% (2 of 23, 95% CI, 1%-28%) for proband-CES cases. The significantly higher diagnostic yield (P value = .002; odds ratio, 7.4 [95% CI, 1.6-33.1]) of trio-CES was due to the identification of de novo and compound heterozygous variants.Conclusions and relevanceIn this sample of patients with undiagnosed, suspected genetic conditions, trio-CES was associated with higher molecular diagnostic yield than proband-CES or traditional molecular diagnostic methods. Additional studies designed to validate these findings and to explore the effect of this approach on clinical and economic outcomes are warranted.

Dataset Information

Calculating the statistical significance of rare variants causal for Mendelian and complex disorders.

Publications

Calculating the statistical significance of rare variants causal for Mendelian and complex disorders.

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