POWERFUL INFERENCE AND PREDICTION FOR GENETIC ASSOCIATION
Shen Xiaotong
University Of Minnesota Twin Citiescity: Minneapolis country: United States (us)
Grant 5R01HL105397-02 from National Heart, Lung, And Blood Institute
Keywords: Accounting; Affect; Arrhythmia; Atrial Fibrillation; base; Biological; Candidate Disease Gene; Cardiac; cohort; Communities; Complex; Computer software; Data; Data Analyses; Development; Diagnostic; Dimensions; Disease; Disease Outcome; Documentation; Equation; Family; Frequencies (time pattern); gene environment interaction; Genes; Genetic; genetic association; Genetic Heterogeneity; genetic variant; genome wide association study; Genotype; Goals; Group Structure; Grouping; Hereditary Disease; Hospitalization; human disease; Individual; innovation; instrument; Knowledge; Lead; Linear Models; Maps; Medicine; Methodology; Methods; Modeling; Network-based; novel; Other Genetics; Pathway interactions; Phase; Population; Predisposition; Preventive; Public Domains; public health relevance; Publishing; Research; Research Design; Resources; Risk Factors; software development; Statistical Methods; Structure; Techniques; Testing; theories; tool; trait; Variant; Writing
Relevance: This proposed research is expected not only to contribute valuable analysis tools to the elucida- tion of genetic components of complex human diseases and traits, but also to advance statistical methodology and theory for high-dimensional data,
Project start date: 2011-03-10
Project end date: 2014-12-31
Budget start date: 1-JAN-2012
Budget end date: 31-DEC-2012
5R01HL105397-02 (2012): $322754
Sponsored Links Excellgen http://Excellgen.com
Grants awarded to Shen Xiaotong
POWERFUL INFERENCE AND PREDICTION FOR GENETIC ASSOCIATION
Shen Xiaotong, Professor
University Of Minnesota Twin Citiescity: Minneapolis country: United States (us)
Grant 1R01HL105397-01 from National Heart, Lung, And Blood Institute
Abstract: Genetic association studies aim to map disease genes through comparisons of frequencies of genetic variants among affected and unaffected individuals. Due to usually weak associations between genetic variants and disease, it is critical to apply powerful statistical tests to maximize the chance to locate disease loci. We propose developing novel and powerful multi-locus methods based on penalized regression to detect genetic association for population- or family-based studies with un- phased genotype data. Specifically, statistical methods and theory will be developed and evaluated for statistical inference and prediction based on penalized regression with novel nonconvex penalties for linear models, generalized linear models and generalized estimating equations. We will apply the developed methods to large cohorts in the Candidate gene Association Resource (CARe) for multi- locus analysis to discover atrial fibrillation (AF)-associated variants, possibly by considering gene by gene and gene by environment interactions. Known and newly discovered genetic and other risk factors will be used to predict AF. This proposed research is expected not only to contribute valuable analysis tools to the elucidation of genetic components of complex human diseases and traits, but also to advance statistical methodology and theory for high-dimensional data,
Keywords: Accounting; Affect; Analysis, Data; Arrhythmia; association test; Atrial Fibrillation; Auricular Fibrillation; base; Biological; Candidate Disease Gene; Candidate Gene; Cardiac; Cardiac Arrhythmia; cohort; Communities; Complex; computer program/software; Computer Programs; Computer software; Data; Data Analyses; develop software; developing computer software; Development; Diagnostic; Dimensions; Disease; Disease Outcome; disease/disorder; Disorder; Documentation; environment effect on gene; Equation; Family; Frequencies (time pattern); Frequency; gene environment interaction; Genes; Genetic; genetic association; Genetic Condition; Genetic Diseases; genetic disorder; Genetic Heterogeneity; genetic variant; genome wide association scan; genome wide association studies; genome wide association study; genome-wide scan; genomewide association scan; genomewide association studies; genomewide association study; genomewide scan; Genotype; Goals; Group Structure; Grouping; groupings; GWAS; Heart Arrhythmias; heavy metal lead; heavy metal Pb; Hereditary Disease; hereditary disorder; HOSP; Hospitalization; human disease; Individual; innovate; innovation; innovative; instrument; Knowledge; Lead; Linear Models; Maps; Medicine; Method LOINC Axis 6; Methodology; Methods; Methods and Techniques; Methods, Other; Modeling; Molecular Disease; Network-based; novel; Other Genetics; pathway; Pathway interactions; Pb element; Phase; Population; Predisposition; Preventive; Public Domains; public health relevance; Publishing; Research; Research Design; Research Resources; Resources; Risk Factors; Science of Medicine; Software; software development; Statistical Methods; Structure; study design; Study Type; Susceptibility; Techniques; Testing; theories; tool; trait; Variant; Variation; whole genome association studies; whole genome association study; Writing
Relevance: This proposed research is expected not only to contribute valuable analysis tools to the elucida- tion of genetic components of complex human diseases and traits, but also to advance statistical methodology and theory for high-dimensional data,
Project start date: 2011-03-10
Project end date: 2014-12-31
Budget start date: 10-MAR-2011
Budget end date: 31-DEC-2011
PFA/PA: PA-10-067
1R01HL105397-01 (2011): $354066