A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic

By B. E. Madsen, Sharon R. Browning. PLoS Genetics, 2009

1,118 citations recorded by the source corpus.

Resequencing is an emerging tool for identification of rare disease-associated mutations. Rare mutations are difficult to tag with SNP genotyping, as genotyping studies are designed to detect common variants. However, studies have shown that genetic heterogeneity is a probable scenario for common diseases, in which multiple rare mutations together explain a large proportion of the genetic basis for the disease. Thus, we propose a weighted-sum method to jointly analyse a group of mutations in order to test for groupwise association with disease status. For example, such a group of mutations may result from resequencing a gene. We compare the proposed weighted-sum method to alternative methods and show that it is powerful for identifying disease-associated genes, both on simulated and Encode data. Using the weighted-sum method, a resequencing study can identify a disease-associated gene with an overall population attributable risk (PAR) of 2%, even when each individual mutation has much lower PAR, using 1,000 to 7,000 affected and unaffected individuals, depending on the underlying genetic model. This study thus demonstrates that resequencing studies can identify important genetic associations, provided that specialised analysis methods, such as the weighted-sum method, are used.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/78b29c4f30e0d98c4fdd29d58a5c2cf2b241cb90

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Settling the score: variant prioritization and Mendelian disease (2017, background): “Burden tests were originally proposed as a means to identify common rather than rare diseases, but the efficacy of the approach for Mendelian disease discovery is now making it popular for these applications as well 63 .”
Dissecting the genetics of complex traits using summary association statistics (2016, background): “Burden tests may either assume that standardized effect sizes are the same for each rare variant 108 (i.e. per-allele effect sizes are proportional to 1/ 𝑝 F 1 − 𝑝 F , where p i is the allele frequency), or apply weights or thresholds based on allele frequency or functional information 109,110 .”
Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics (2011, background): “…de novo or private to families, and these would need to be characterized through sequencing of clinical samples (Li and Leal 2009; Cirulli and Goldstein 2010), probably requiring specialized pooled-variant analysis strategies (Kryukov et al. 2009; Madsen and Browning 2009; Price et al. 2010).”
The Next-Generation Sequencing Revolution and Its Impact on Genomics (2013, positive): “Further innovations have allowed for weighting of individual variants (for example, by variant frequency in the weighted sum statistic [Madsen and Browning, 2009] or the data [Han and Pan, 2010; Lin and Tang, 2011; Wu et al.”
Pooled association tests for rare variants in exon-resequencing studies. (2010, background): “Another approach is to weight counts of each variant on the basis of the estimated variance under the null hypothesis of no association.(16) This scheme applies much higher weights to very rare variants, and it implicitly assumes that the log odds ratio is approximately inversely proportional to the”
Exome sequencing as a tool for Mendelian disease gene discovery (2011, positive): “To address this issue, analysis strategies have been developed to assess the collective effects of rare variants across a gene or across multiple genes 86–89 .”
Rare-variant association analysis: study designs and statistical tests. (2014, methodology): “One class of aggregation tests can be termed burden tests: they collapse information for multiple genetic variants into a single genetic score 50–54 , 72 where w j is a threshold indicator or weight for variant j .”
Rare-variant association testing for sequencing data with the sequence kernel association test. (2011, methodology): “However, standard methods used to test for association with single common genetic variants are underpowered for rare variants unless sample sizes or effect sizes are very large.(12,13) A logical alternative approach is to employ burden tests that assess the cumulative effects of multiple variants in”
Characterising and Predicting Haploinsufficiency in the Human Genome (2010, neutral): “genes, pathways) enriched for functional variants [27].”
Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. (2012, methodology): “This weighted burden test is equivalent to the weighted sum test of Madsen and Browning [10] and Han and Pan [13], where Madsen and Browning [10] assumed wj = 1/ √ p̃j(1− p̃j), where p̃j is the estimated MAF for SNP j using controls.”
Optimal tests for rare variant effects in sequencing association studies. (2012, background): “The earliest and most commonly used class of region based rare variant tests are the burden tests, which collapse or summarize the rare variants within region as a single genetic variable which can then be tested for association with any trait of interest [6, 10, 11, 14].”
Identifying Causal Variants at Loci with Multiple Signals of Association (2014, methodology): “These methods have become very popular in the context of rare variant association studies (Li and Leal 2008; Madsen and Browning 2009; Jul et al. 2011; Long et al. 2013; Navon et al. 2013).”
Testing for an Unusual Distribution of Rare Variants (2011, positive): “Tests that up-weight variants based on allele frequencies find that power can be improved, if there is a relationship between allele frequency and effect size [7].”