ArticleBMC bioinformatics2015
Rare variants analysis using penalization methods for whole genome sequence data.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A review of model evaluation metrics for machine learning in genetics and genomics.Frontiers in bioinformatics · 2024Review
- A tree-based gene-environment interaction analysis with rare features.Statistical analysis and data mining · 2022Article
- locStra: Fast analysis of regional/global stratification in whole-genome sequencing studies.Genetic epidemiology · 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
- Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics.BMC genomics · 2019Article
- Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies.BMC bioinformatics · 2019Article
- A Multi-Trait Approach Identified Genetic Variants Including a Rare Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function.Scientific reports · 2019Article
- An improved burden-test pipeline for identifying associations from rare germline and somatic variants.BMC genomics · 2017Article
- On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows.Genetic epidemiology · 2017Article
- Longitudinal data analysis for rare variants detection with penalized quadratic inference function.Scientific reports · 2017Article
- Block-based association tests for rare variants using Kullback-Leibler divergence.Journal of human genetics · 2016Article
- Identification of Rare Variants in Metabolites of the Carnitine Pathway by Whole Genome Sequencing Analysis.Genetic epidemiology · 2016Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundAvailability of affordable and accessible whole genome sequencing for biomedical applications poses a number of statistical challenges and opportunities, particularly related to the analysis of rare variants and sparseness of the data. Although efforts have been devoted to address these challenges, the performance of statistical methods for rare variants analysis still needs further consideration.
resultWe introduce a new approach that applies restricted principal component analysis with convex penalization and then selects the best predictors of a phenotype by a concave penalized regression model, while estimating the impact of each genomic region on the phenotype. Using simulated data, we show that the proposed method maintains good power for association testing while keeping the false discovery rate low under a verity of genetic architectures. Illustrative data analyses reveal encouraging result of this method in comparison with other commonly applied methods for rare variants analysis.
conclusionBy taking into account linkage disequilibrium and sparseness of the data, the proposed method improves power and controls the false discovery rate compared to other commonly applied methods for rare variant analyses.
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