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Principal Investigator: Debashree Ray
Organization: JOHNS HOPKINS UNIVERSITY
Fiscal Year: 2024
Award: $204,688
Funding agency: National Human Genome Research Institute
PROJECT SUMMARY
Years of genetic research on various complex human traits have implicated several genetic
variants as risk factors for two or more diseases/traits, including seemingly unrelated traits. A
recent systematic evaluation of >500 traits from >4100 genome-wide association studies (GWAS)
has revealed that 90% of the variants associated with these traits influence at least two traits.
This phenomenon where a genetic region or locus confers risk to more than one trait is known as
pleiotropy. Discovering patterns of pleiotropy is crucial for a comprehensive understanding of
biological mechanisms of human diseases and traits (e.g. understand how genetic variation leads
to trait variation and inter-trait correlations, and how traits may be causally related to each other),
and can have translational impact in the long run (e.g. guide identification of molecular targets or
help predict side-effects in drug development). While the scientific significance of studying
pleiotropy or genetic overlap is well-understood, statistical methods for identifying common
genetic basis between traits are still lacking. This is especially true for traits sampled under a
family-based design. To address methodological challenges in investigating pleiotropy, in Aim 1,
we propose a novel, innovative statistical method for identifying common genetic variants
influencing two possibly correlated traits. In Aim 2, we non-trivially extend our method in Aim 1 to
identify rare genetic variants influencing two independent traits. We use only aggregate-level
genotype-phenotype association results (or GWAS summary statistics), thus helping protect
human subjects data and facilitating global collaborations. The proposed methods, while having
the potential to substantially outperform current approaches in the area, will be more general and
distinct from existing research efforts in the following ways: applicability to correlated traits (Aim
1; e.g. a disease-related endophenotype and a molecular trait, or two different -omics traits), to
traits measured on independent sets of individuals (Aims 1, 2; e.g. separate case-control studies
on two diseases), to traits sharing some samples (Aim 1; e.g. case-control studies with shared
controls), to study designs where individuals may not be randomly sampled or unrelated (Aims
1, 2; e.g. case-parent trio design), to rare variants (Aim 2), and our open-access tools will allow
genomics researchers across the world to readily adopt and apply our methods even in resource-
poor environments (Aims 1, 2). Successful implementation of these aims will provide the broader
scientific community with novel, powerful and scalable methods, along with well-documented free
software, to statistically investigate questions of pleiotropy between complex human diseases/
traits across the entire allele frequency spectrum using GWAS summary statistics only.
Terms: <Address><Adopted><Adult-Onset Diabetes Mellitus><Allele Frequency><Androgenetic Alopecia><Area><Autoimmune><Benchmarking><Best Practice Analysis><Biological><Biology><Calibration><Cancers><Case-Base Studies><Case-Comparison Studies><Case-Compeer Studies><Case-Referent Studies><Case-Referrent Studies><Case/Control Studies><Causality><Cell Communication and Signaling><Cell Signaling><Collaborations><Communities><Complex><Computer software><Coronary Arteriosclerosis><Coronary Artery Disease><Coronary Artery Disorder><Coronary Atherosclerosis><Craniofacial Abnormalities><Crohn disease><Crohn's><Crohn's disease><Crohn's disorder><Data><Data Set><Detection><Development><Diagnosis><Disease><Disorder><Ensure><Etiology><Evaluation><Family><Frequencies><GWA study><GWAS><Gene Frequency><Gene variant><Genetic><Genetic Diversity><Genetic Predisposition><Genetic Predisposition to Disease><Genetic Research><Genetic Susceptibility><Genetic Variation><Genetic propensity><Genomics><Genotype><Granulomatous Enteritis><Health><Human><Human Genome><Individual><Inherited Predisposition><Inherited Susceptibility><Intervention><Intervention Strategies><Intracellular Communication and Signaling><Investigation><Investigators><Ketosis-Resistant Diabetes Mellitus><Knowledge><Low-resource area><Low-resource community><Low-resource environment><Low-resource region><Low-resource setting><Male Pattern Baldness><Malignant Neoplasms><Malignant Tumor><Malignant Tumor of the Prostate><Malignant neoplasm of prostate><Malignant prostatic tumor><Math><Mathematics><Maturity-Onset Diabetes Mellitus><Measures><Methodology><Methods><Modeling><Modern Man><Molecular><Molecular Target><NIDDM><Non-Insulin Dependent Diabetes><Non-Insulin-Dependent Diabetes Mellitus><Noninsulin Dependent Diabetes><Noninsulin Dependent Diabetes Mellitus><Normalcy><Normalities><Paralysis Agitans><Parents><Parkinson><Parkinson Disease><Pattern><Phenotype><Population><Primary Parkinsonism><Prostate CA><Prostate Cancer><Prostate malignancy><Prostatic Cancer><Public Health Applications><Public Health Applications Research><Research><Research Design><Research Personnel><Researchers><Resource-constrained area><Resource-constrained community><Resource-constrained environment><Resource-constrained region><Resource-constrained setting><Resource-limited area><Resource-limited community><Resource-limited environment><Resource-limited region><Resource-limited setting><Resource-poor area><Resource-poor community><Resource-poor environment><Resource-poor region><Resource-poor setting><Risk><Risk Factors><Sample Size><Sampling><Signal Transduction><Signal Transduction Systems><Signaling><Slow-Onset Diabetes Mellitus><Software><Span 60><Stable Diabetes Mellitus><Statistical Distributions><Statistical Methods><Study Type><T2 DM><T2D><T2DM><Testing><Therapeutic><Tonsillectomy><Type 2 Diabetes Mellitus><Type 2 diabetes><Type II Diabetes Mellitus><Type II diabetes><Ulcerated Colitis><Ulcerative Colitis><Variant><Variation><adult onset diabetes><allele variant><allelic frequency><allelic variant><alopecia hereditaria><androgenic alopecia><atherosclerotic coronary disease><benchmark><biologic><biological signal transduction><case control><case-controlled><case-controlled studies><causation><common baldness><coronary arterial disease><craniofacial anomalies><craniofacial defects><craniofacial malformation><design><designing><developmental><disease causation><drug development><early onset><eleocolitis><endophenotype><gene testing><gene-based testing><genetic architecture><genetic etiology><genetic mechanism of disease><genetic testing><genetic variant><genetic vulnerability><genetically predisposed><genome scale><genome wide association><genome wide association scan><genome wide association studies><genome wide association study><genome-wide><genomewide><genomewide association scan><genomewide association studies><genomewide association study><genomic variant><hereditary alopecia><human disease><human subject protection><human whole genome><innovate><innovation><innovative><interventional strategy><ketosis resistant diabetes><large scale simulation><male pattern alopecia><malignancy><maturity onset diabetes><neoplasm/cancer><normality><novel><open source><parent><pleiotropic effect><pleiotropism><pleiotropy><rare allele><rare mutation><rare variant><regional enteritis><side effect><statistic methods><statistics><study design><theories><tool><trait><translational impact><type 2 DM><type II DM><type two diabetes><user friendly computer software><user friendly software><user-friendly><whole genome association analysis><whole genome association studies><whole genome association study>