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.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Behavioral Genetics and Epidemiology Study Section (BGES)
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Bender, Patrick
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Harvard University
Biostatistics & Other Math Sci
Schools of Public Health
United States
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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
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