Recent scientific and technological developments have made genome-wide association studies a reality. Their success in disentangling the genetic basis of complex diseases will depend largely on the efficient handling of the statistical challenges posed by such studies. Several hundred thousand SNPs will be genotyped and examined for the potential associations with phenotypes. Genome-wide association studies must translate the markedly increased amount of SNP-information into increased statistical power. For the number of statistical tests computed in a genome-wide association study, standard statistical methods for handling the multiple testing problem, such as false-discovery rate, are too conservative and are likely to dilute any true genetic signals. Novel statistical methodology is required to handle the multiple testing problems at this scale. We will develop novel statistical methodology to solve these major hurdles in genome-wide association studies. Our novel statistical methodology will enable researchers to examine the underlying genetic mechanism of complex diseases, such ad Alzheimer Disease and ADHD.

Public Health Relevance

Alzheimer Disease and ADHD are major public health problems in the United States. Our novel statistical methodology will provide a set of tools, which researchers and clinicians can use to identify factors (inherited or environmentally-induced) that affect the development of these diseases. In turn, a better understanding of the genetic mechanisms of these conditions can result in better and more efficient care of those who suffer from - or are most at risk for the development of -these diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH081862-02
Application #
7893048
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Bender, Patrick
Project Start
2009-07-14
Project End
2012-02-28
Budget Start
2010-03-19
Budget End
2012-02-28
Support Year
2
Fiscal Year
2010
Total Cost
$415,447
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
Schlauch, Daniel; Fier, Heide; Lange, Christoph (2017) Identification of genetic outliers due to sub-structure and cryptic relationships. Bioinformatics 33:1972-1979
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
Erk, Susanne; Meyer-Lindenberg, Andreas; Linden, David E J et al. (2014) Replication of brain function effects of a genome-wide supported psychiatric risk variant in the CACNA1C gene and new multi-locus effects. Neuroimage 94:147-154
Qiao, Dandi; Cho, Michael H; Fier, Heide et al. (2014) On the simultaneous association analysis of large genomic regions: a massive multi-locus association test. Bioinformatics 30:157-64
Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu et al. (2014) Voxelwise multivariate analysis of multimodality magnetic resonance imaging. Hum Brain Mapp 35:831-46

Showing the most recent 10 out of 37 publications