ArticleGenetics2011
An optimal weighted aggregated association test for identification of rare variants involved in common diseases.
Article in Genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a Generalized Linear Model Framework.Mathematics (Basel, Switzerland) · 2023Article
- MARS: leveraging allelic heterogeneity to increase power of association testing.Genome biology · 2021Article
- [An improved association analysis pipeline for tumor susceptibility variant in haplotype amplification area].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2020Article
- Evidence for craniofacial enhancer variation underlying nonsyndromic cleft lip and palate.Human genetics · 2020Article
- Power and sample size calculations for high-throughput sequencing-based experiments.Briefings in bioinformatics · 2018Article
- An improved burden-test pipeline for identifying associations from rare germline and somatic variants.BMC genomics · 2017Article
- Widespread Allelic Heterogeneity in Complex Traits.American journal of human genetics · 2017Article
- The impact of rare and low-frequency genetic variants in common disease.Genome biology · 2017Review
- Colocalization of GWAS and eQTL Signals Detects Target Genes.American journal of human genetics · 2016Article
- Increasing Generality and Power of Rare-Variant Tests by Utilizing Extended Pedigrees.American journal of human genetics · 2016Article
- Article
- A method to incorporate prior information into score test for genetic association studies.BMC bioinformatics · 2014Article
- A comprehensive evaluation of collapsing methods using simulated and real data: excellent annotation of functionality and large sample sizes required.Frontiers in genetics · 2014Article
- Evaluating the impact of genotype errors on rare variant tests of association.Frontiers in genetics · 2014Article
- Family-based association tests for sequence data, and comparisons with population-based association tests.European journal of human genetics : EJHG · 2013Article
- Article
- Multiple genetic variant association testing by collapsing and kernel methods with pedigree or population structured data.Genetic epidemiology · 2013Article
- Exome sequencing and systems biology converge to identify novel mutations in the L-type calcium channel, CACNA1C, linked to autosomal dominant long QT syndrome.Circulation. Cardiovascular genetics · 2013Article
- A geometric framework for evaluating rare variant tests of association.Genetic epidemiology · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The advent of next generation sequencing technologies allows one to discover nearly all rare variants in a genomic region of interest. This technological development increases the need for an effective statistical method for testing the aggregated effect of rare variants in a gene on disease susceptibility. The idea behind this approach is that if a certain gene is involved in a disease, many rare variants within the gene will disrupt the function of the gene and are associated with the disease. In this article, we present the rare variant weighted aggregate statistic (RWAS), a method that groups rare variants and computes a weighted sum of differences between case and control mutation counts. We show that our method outperforms the groupwise association test of Madsen and Browning in the disease-risk model that assumes that each variant makes an equally small contribution to disease risk. In addition, we can incorporate prior information into our method of which variants are likely causal. By using simulated data and real mutation screening data of the susceptibility gene for ataxia telangiectasia, we demonstrate that prior information has a substantial influence on the statistical power of association studies. Our method is publicly available at http://genetics.cs.ucla.edu/rarevariants.
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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.