Genome-wide association studies (GWAs) have led to the discovery of novel, robust associations for numerous complex diseases and phenotypes. While the new findings can be replicated reliably in other studies, the amount of phenotypic variation that is explained by the new association findings is small compared to the estimated total heritability of most diseases/traits. This suggests that the current GWAs are not able to identify most of the disease loci. Potential reasons are the study heterogeneity/confounding and the lack of sufficient statistical power to address the inherent multiple testing problem. For family-based designs, we will develop novel statistical methodology that achieves higher power levels than the currently used methodology and, at the same time, are completely robust again confounding. The application of the new methods to genome-wide association studies for Alzheimer's'Disease and Attention Deficit Hyperactivity Disorder will provide new insights that will help the scientific community to identify new genes for these diseases which are major public health problems in the United States.

Public Health Relevance

Alzheimer's disease and Attention Deficit Hyperactivity Disorder are major public health problems in the United States. The proposed statistical methodology will provide new analysis approaches that will enable researchers and clinicians to identify genetic risk loci for these diseases and other complex disease and phenotypes. In turn, an improved understanding of the genetic architecture of these conditions will result in a better and more efficient care for those who suffer from these diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH087590-02
Application #
7995260
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Bender, Patrick
Project Start
2009-12-01
Project End
2013-11-30
Budget Start
2010-12-01
Budget End
2011-11-30
Support Year
2
Fiscal Year
2011
Total Cost
$367,948
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Hecker, Julian; Xu, Xin; Townes, F William et al. (2018) Family-based tests for associating haplotypes with general phenotype data: Improving the FBAT-haplotype algorithm. Genet Epidemiol 42:123-126
Loehlein Fier, Heide; Prokopenko, Dmitry; Hecker, Julian et al. (2017) On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows. Genet Epidemiol 41:332-340
Hecker, Julian; Maaser, Anna; Prokopenko, Dmitry et al. (2017) Reporting Correct p Values in VEGAS Analyses. Twin Res Hum Genet 20:257-259
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Prokopenko, Dmitry; Hecker, Julian; Silverman, Edwin K et al. (2016) Utilizing the Jaccard index to reveal population stratification in sequencing data: a simulation study and an application to the 1000 Genomes Project. Bioinformatics 32:1366-72
Prokopenko, Dmitry; Hecker, Julian; Silverman, Edwin et al. (2015) Using Network Methodology to Infer Population Substructure. PLoS One 10:e0130708
Hecker, Julian; Prokopenko, Dmitry; Lange, Christoph et al. (2015) On the Recombination Rate Estimation in the Presence of Population Substructure. PLoS One 10:e0145152
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Erk, Susanne; Meyer-Lindenberg, Andreas; Schmierer, Phöbe et al. (2014) Hippocampal and frontolimbic function as intermediate phenotype for psychosis: evidence from healthy relatives and a common risk variant in CACNA1C. Biol Psychiatry 76:466-75

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