ArticleAnnals of human genetics2009
A variable-sized sliding-window approach for genetic association studies via principal component analysis.
Article in Annals of human genetics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed.
- A GBS-based genetic linkage map and quantitative trait loci (QTL) associated with resistance toFrontiers in plant science · 2023Article
- An Efficient Score Test Integrated with Empirical Bayes for Genome-Wide Association Studies.Frontiers in genetics · 2021Article
- A Genome-Wide Association Study for Calving Interval in Holstein Dairy Cows Using Weighted Single-Step Genomic BLUP Approach.Animals : an open access journal from MDPI · 2020Article
- Soft windowing application to improve analysis of high-throughput phenotyping data.Bioinformatics (Oxford, England) · 2020Article
- Maximal Segmental Score Method for Localizing Recessive Disease Variants Based on Sequence Data.Frontiers in genetics · 2020Article
- Single-Step Genome-Wide Association Study for Resistance toG3 (Bethesda, Md.) · 2019Article
- CpG-related SNPs in the MS4A region have a dose-dependent effect on risk of late-onset Alzheimer disease.Aging cell · 2019Article
- Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle.BMC genetics · 2019Article
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- Regionally Smoothed Meta-Analysis Methods for GWAS Datasets.Genetic epidemiology · 2016Article
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- Localization of association signal from risk and protective variants in sequencing studies.Frontiers in genetics · 2012Article
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- Genome-wide association filtering using a highly locus-specific transmission/disequilibrium test.Human genetics · 2010Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Recently with the rapid improvements in high-throughout genotyping techniques, researchers are facing the very challenging task of analysing large-scale genetic associations, especially at the whole-genome level, without an optimal solution. In this study, we propose a new approach for genetic association analysis that is based on a variable-sized sliding-window framework and employs principal component analysis to find the optimum window size. With the help of the bisection algorithm in window-size searching, our method is more computationally efficient than available approaches. We evaluate the performance of the proposed method by comparing it with two other methods-a single-marker method and a variable-length Markov chain method. We demonstrate that, in most cases, the proposed method out-performs the other two methods. Furthermore, since the proposed method is based on genotype data, it does not require any computationally intensive phasing program to account for uncertain haplotype phase.
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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.