NDLI: Regularized regression method for genome-wide association studies

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Identifying rare variants from exome scans: the GAW17 experience

Genetic Analysis Workshop 17 mini-exome simulation

Identifying influential regions in extremely rare variants using a fixed-bin approach

Identification of genes and variants associated with quantitative traits using Bayesian factor screening

Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection

A weighted accumulation test for associating rare genetic variation with quantitative phenotypes

A gene-based approach for testing association of rare alleles

Population structure analysis using rare and common functional variants

Pairwise shared genomic segment analysis in high-risk pedigrees: application to Genetic Analysis Workshop 17 exome-sequencing SNP data

Detection of associations with rare and common SNPs for quantitative traits: a nonparametric Bayes-based approach

Successful identification of rare variants using oligogenic segregation analysis as a prioritizing tool for whole-exome sequencing studies

Evaluation of a LASSO regression approach on the unrelated samples of Genetic Analysis Workshop 17

Identification of functional genetic variation in exome sequence analysis

Comparing nominal and real quality scores on next-generation sequencing genotype calls

Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression

Enhancing the discovery of rare disease variants through hierarchical modeling

Identifying rare disease variants in the Genetic Analysis Workshop 17 simulated data: a comparison of several statistical approaches

Pathway analysis following association study

Gene-based partial least-squares approaches for detecting rare variant associations with complex traits

Incorporating predicted functions of nonsynonymous variants into gene-based analysis of exome sequencing data: a comparative study

Identifying causal rare variants of disease through family-based analysis of Genetics Analysis Workshop 17 data set

Pathway analysis for family data using nested random-effects models

An aggregating U-Test for a genetic association study of quantitative traits

LASSO model selection with post-processing for a genome-wide association study data set

Interrogating population structure and its impact on association tests

Testing gene-environment interactions in gene-based association studies

Detecting functional rare variants by collapsing and incorporating functional annotation in Genetic Analysis Workshop 17 mini-exome data

Exploration and comparison of methods for combining population- and family-based genetic association using the Genetic Analysis Workshop 17 mini-exome

Use of principal components to aggregate rare variants in case-control and family-based association studies in the presence of multiple covariates

Challenges and directions: an analysis of Genetic Analysis Workshop 17 data by collapsing rare variants within family data

Identity by descent and association analysis of dichotomous traits based on large pedigrees

Comparison of genetic association strategies in the presence of rare alleles

Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data

Estimating heritability using family and unrelated individuals data

Methods for adjusting population structure and familial relatedness in association test for collective effect of multiple rare variants on quantitative traits

Family- and population-based designs identify different rare causal variants

Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data

Using the posterior distribution of deviance to measure evidence of association for rare susceptibility variants

Rare variant density across the genome and across populations

Prioritizing single-nucleotide variations that potentially regulate alternative splicing

Comparison of SNP-based and gene-based association studies in detecting rare variants using unrelated individuals

Collapsing ROC approach for risk prediction research on both common and rare variants

Region-based and pathway-based QTL mapping using a p-value combination method

Combining effects from rare and common genetic variants in an exome-wide association study of sequence data

Pathway-based joint effects analysis of rare genetic variants using Genetic Analysis Workshop 17 exon sequence data

Large-scale risk prediction applied to Genetic Analysis Workshop 17 mini-exome sequence data

Evaluating aggregate effects of rare and common variants in the 1000 Genomes Project exon sequencing data using latent variable structural equation modeling

Evaluating methods for combining rare variant data in pathway-based tests of genetic association

Comparison of scoring methods for the detection of causal genes with or without rare variants

New insights into old methods for identifying causal rare variants

Methods for detecting associations between phenotype and aggregations of rare variants

Enrichment analysis of genetic association in genes and pathways by aggregating signals from both rare and common variants

Two-stage analyses of sequence variants in association with quantitative traits

Distance-based phenotypic association analysis of DNA sequence data

Mutational load analysis of unrelated individuals

Identification of functional rare variants in genome-wide association studies using stability selection based on random collapsing

Application of Bayesian regression with singular value decomposition method in association studies for sequence data

Confidence set of putative quantitative trait loci in whole genome scans with application to the Genetic Analysis Workshop 17 simulated data

Resampling procedures to identify important SNPs using a consensus approach

Treating phenotype as given: a simple resampling method for genome-wide association studies

Disease risk prediction with rare and common variants

Case-control association testing by graphical modeling for the Genetic Analysis Workshop 17 mini-exome sequence data

Power of association tests in the presence of multiple causal variants

Two-stage study designs combining genome-wide association studies, tag single-nucleotide polymorphisms, and exome sequencing: accuracy of genetic effect estimates

Gene-based Higher Criticism methods for large-scale exonic single-nucleotide polymorphism data

Principal components ancestry adjustment for Genetic Analysis Workshop 17 data

Regularized regression method for genome-wide association studies

Identifying variants that contribute to linkage for dichotomous and quantitative traits in extended pedigrees

Using LASSO regression to detect predictive aggregate effects in genetic studies

Detecting rare functional variants using a wavelet-based test on quantitative and qualitative traits

Genetic signal maximization using environmental regression

Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes

Joint association analysis of bivariate quantitative and qualitative traits

Gene-based multiple trait analysis for exome sequencing data

Identity-by-descent filtering as a tool for the identification of disease alleles in exome sequence data from distant relatives

Strategies for selection of subjects for sequencing after detection of a linkage peak

Different approaches for dealing with rare variants in family-based genetic studies: application of a Genetic Analysis Workshop 17 problem

Using linkage analysis of large pedigrees to guide association analyses

A novel method to detect rare variants using both family and unrelated case-control data

Linkage analysis merging replicate phenotypes: an application to three quantitative phenotypes in two African samples

Enriching rare variants using family-specific linkage information

Old lessons learned anew: family-based methods for detecting genes responsible for quantitative and qualitative traits in the Genetic Analysis Workshop 17 mini-exome sequence data

A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis

A pathway-based association analysis model using common and rare variants

Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

Do rare variant genotypes predict common variant genotypes?

Capability of common SNPs to tag rare variants

Association tests for rare and common variants based on genotypic and phenotypic measures of similarity between individuals

Does pathway analysis make it easier for common variants to tag rare ones?

SNP set analysis for detecting disease association using exon sequence data

Penalized-regression-based multimarker genotype analysis of Genetic Analysis Workshop 17 data

Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model

Rare variant collapsing in conjunction with mean log p-value and gradient boosting approaches applied to Genetic Analysis Workshop 17 data

Search for compound heterozygous effects in exome sequence of unrelated subjects

Detecting disease rare alleles using single SNPs in families and haplotyping in unrelated subjects from the Genetic Analysis Workshop 17 data

Addition of multiple rare SNPs to known common variants improves the association between disease and gene in the Genetic Analysis Workshop 17 data

Finding genes that influence quantitative traits with tree-based clustering

Identifying rare variants using a Bayesian regression approach

A LASSO-based approach to analyzing rare variants in genetic association studies

Stratify or adjust? Dealing with multiple populations when evaluating rare variants

Novel tree-based method to generate markers from rare variant data

Identification of multiple rare variants associated with a disease

Performance of random forests and logic regression methods using mini-exome sequence data

Digging into the extremes: a useful approach for the analysis of rare variants with continuous traits?

Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach

Assessing the impact of missing genotype data in rare variant association analysis

Detection of rare functional variants using group ISIS

Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data

Genome-wide case-control study in GAW17 using coalesced rare variants

Are quantitative trait-dependent sampling designs cost-effective for analysis of rare and common variants?

Detection of rare variant effects in association studies: extreme values, iterative regression, and a hybrid approach

Comparison of statistical approaches to rare variant analysis for quantitative traits

Improved power by collapsing rare and common variants based on a data-adaptive forward selection strategy

Comparison of collapsing methods for the statistical analysis of rare variants

Effect of population stratification analysis on false-positive rates for common and rare variants

Collapsing-based and kernel-based single-gene analyses applied to Genetic Analysis Workshop 17 mini-exome data

Evaluation of pooled association tests for rare variant identification

Evaluating methods for the analysis of rare variants in sequence data

Identification of genes associated with complex traits by testing the genetic dissimilarity between individuals

Application of collapsing methods for continuous traits to the Genetic Analysis Workshop 17 exome sequence data

Year: 2011, Volume: 5, Issue: Suppl 8

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Regularized regression method for genome-wide association studies

Content Provider	PubMed Central
Author	Liu, Jin Wang, Kai Ma, Shuangge Huang, Jian
Copyright Year	2011
Abstract	We use a novel penalized approach for genome-wide association study that accounts for the linkage disequilibrium between adjacent markers. This method uses a penalty on the difference of the genetic effect at adjacent single-nucleotide polymorphisms and combines it with the minimax concave penalty, which has been shown to be superior to the least absolute shrinkage and selection operator (LASSO) in terms of estimator bias and selection consistency. Our method is implemented using a coordinate descent algorithm. The value of the tuning parameters is determined by extended Bayesian information criteria. The leave-one-out method is used to compute p-values of selected single-nucleotide polymorphisms. Its applicability to a simulated data from Genetic Analysis Workshop 17 replication one is illustrated. Our method selects three SNPs (C13S522, C13S523, and C13S524), whereas the LASSO method selects two SNPs (C13S522 and C13S523).
Related Links	http://dx.doi.org/10.1186/1753-6561-5-s9-s67
Starting Page	67
File Format	PDF
ISSN	17536561
e-ISSN	17536561
Journal	BMC Proceedings
Issue Number	Suppl 9
Volume Number	5
Language	English
Publisher	BioMed Central
Publisher Date	2011-01-01
Access Restriction	Open
Rights Holder	BioMed Central
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine Biochemistry, Genetics and Molecular Biology

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