ArticleGenetics2013
Rare variant association testing under low-coverage sequencing.
Article in Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A multiethnic whole genome sequencing study to identify novel loci for bone mineral density.Human molecular genetics · 2022Pooled it
- Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores.Genome medicine · 2019Article
- Genome Scans Reveal Homogenization and Local Adaptations in Populations of the Soybean Cyst Nematode.Frontiers in plant science · 2018Article
- A Statistical Approach to Fine Mapping for the Identification of Potential Causal Variants Related to Bone Mineral Density.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2017Article
- Low-, high-coverage, and two-stage DNA sequencing in the design of the genetic association study.Genetic epidemiology · 2017Article
- Article
- Population-genetic inference from pooled-sequencing data.Genome biology and evolution · 2014Article
- Evaluating the impact of genotype errors on rare variant tests of association.Frontiers in genetics · 2014Article
- Copper phenotype in Alzheimer's disease: dissecting the pathway.American journal of neurodegenerative disease · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Deep sequencing technologies enable the study of the effects of rare variants in disease risk. While methods have been developed to increase statistical power for detection of such effects, detecting subtle associations requires studies with hundreds or thousands of individuals, which is prohibitively costly. Recently, low-coverage sequencing has been shown to effectively reduce the cost of genome-wide association studies, using current sequencing technologies. However, current methods for disease association testing on rare variants cannot be applied directly to low-coverage sequencing data, as they require individual genotype data, which may not be called correctly due to low-coverage and inherent sequencing errors. In this article, we propose two novel methods for detecting association of rare variants with disease risk, using low coverage, error-prone sequencing. We show by simulation that our methods outperform previous methods under both low- and high-coverage sequencing and under different disease architectures. We use real data and simulation studies to demonstrate that to maximize the power to detect associations for a fixed budget, it is desirable to include more samples while lowering coverage and to perform an analysis using our suggested methods.
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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.