ArticleGenetic epidemiology2011
Mining gold dust under the genome wide significance level: a two-stage approach to analysis of GWAS.
Article in Genetic epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 50 citations in OpenAlex.
- Exploring the potential anti-Alzheimer disease mechanisms of Alpiniae Oxyphyliae Fructus by network pharmacology study and molecular docking.Metabolic brain disease · 2023Article
- Genome wide association study identifies novel candidate genes for growth and body conformation traits in goats.Scientific reports · 2022Article
- Author's reply to "A novel seven-gene panel predicts the sensitivity and prognosis of head and neck squamous cell carcinoma treated with platinum-based radio(chemo)therapy".European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2021Article
- DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies.NAR genomics and bioinformatics · 2021Article
- Genomics models in radiotherapy: From mechanistic to machine learning.Medical physics · 2020Review
- Genome-wide association studies using binned genotypes.Heredity · 2020Article
- Single-Locus and Multi-Locus Genome-Wide Association Studies for Intramuscular Fat in Duroc Pigs.Frontiers in genetics · 2019Article
- Re-assessment of multiple testing strategies for more efficient genome-wide association studies.European journal of human genetics : EJHG · 2018Article
- Machine Learning and Radiogenomics: Lessons Learned and Future Directions.Frontiers in oncology · 2018Review
- Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.Scientific reports · 2016Article
- Assessing statistical significance in multivariable genome wide association analysis.Bioinformatics (Oxford, England) · 2016Article
- Regularized machine learning in the genetic prediction of complex traits.PLoS genetics · 2014Article
- A model-free approach for detecting interactions in genetic association studies.Briefings in bioinformatics · 2014Article
- Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts.PLoS genetics · 2013Article
- Strategy to control type I error increases power to identify genetic variation using the full biological trajectory.Genetic epidemiology · 2013Article
- Meta-analysis methods for genome-wide association studies and beyond.Nature reviews. Genetics · 2013Review
- Exploring the genetic basis of chronic periodontitis: a genome-wide association study.Human molecular genetics · 2013Article
- Reprioritizing genetic associations in hit regions using LASSO-based resample model averaging.Genetic epidemiology · 2012Article
- Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations.Algorithms for molecular biology : AMB · 2012Article
- Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression.Frontiers in neuroscience · 2012Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
We propose a two-stage approach to analyze genome-wide association data in order to identify a set of promising single-nucleotide polymorphisms (SNPs). In stage one, we select a list of top signals from single SNP analyses by controlling false discovery rate. In stage two, we use the least absolute shrinkage and selection operator (LASSO) regression to reduce false positives. The proposed approach was evaluated using simulated quantitative traits based on genome-wide SNP data on 8,861 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC) Study. Our first stage, targeted at controlling false negatives, yields better power than using Bonferroni-corrected significance level. The LASSO regression reduces the number of significant SNPs in stage two: it reduces false-positive SNPs and it reduces true-positive SNPs also at simulated causal loci due to linkage disequilibrium. Interestingly, the LASSO regression preserves the power from stage one, i.e., the number of causal loci detected from the LASSO regression in stage two is almost the same as in stage one, while reducing false positives further. Real data on systolic blood pressure in the ARIC study was analyzed using our two-stage approach which identified two significant SNPs, one of which was reported to be genome-significant in a meta-analysis containing a much larger sample size. On the other hand, a single SNP association scan did not yield any significant results.
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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.