ArticleBMC medical genomics2018
Calculating the statistical significance of rare variants causal for Mendelian and complex disorders.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- A novel two-sample Mendelian randomization framework integrating common and rare variants: application to assess the effect of HDL-C on preeclampsia risk.Briefings in bioinformatics · 2026Article
- Unraveling the cellular characteristics of cardiomyopathy with rare variant-driven gene signatures using multi-omics analysis.Scientific reports · 2025Article
- Homozygous variant in translocase of outer mitochondrial membrane 7 leads to metabolic reprogramming and microcephalic osteodysplastic dwarfism with moyamoya disease.EBioMedicine · 2024Article
- Rare heterozygous variants in paediatric steroid resistant nephrotic syndrome - a population-based analysis of their significance.Scientific reports · 2024Article
- STR mutations on chromosome 15q cause thyrotropin resistance by activating a primate-specific enhancer of MIR7-2/MIR1179.Nature genetics · 2024Article
- Idiopathic erythrocytosis: a germline disease?Clinical and experimental medicine · 2024Article
- Family-Based Whole-Exome Analysis of Specific Language Impairment (SLI) Identifies Rare Variants inBrain sciences · 2021Article
- Utility of Gene Panels for the Diagnosis of Inborn Errors of Metabolism in a Metabolic Reference Center.Genes · 2021Article
- Comprehensive Analysis of Rare Variants of 101 Autism-Linked Genes in a Hungarian Cohort of Autism Spectrum Disorder Patients.Frontiers in genetics · 2019Article
- Targeted resequencing identifies genes with recurrent variation in cerebral palsy.NPJ genomic medicine · 2019Article
- Diagnosing rare diseases after the exome.Cold Spring Harbor molecular case studies · 2018Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.