# Detecting pleiotropic effects through integration of omics data

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $421,502

## Abstract

In 2019, ~5.8 million Americans suffered from Alzheimer’s disease (AD) of which ~5.6 million
were >65 years of age. One in 10 Americans age >65 has AD. Although common and rare variants
in >20 genes have been implicated in LOAD etiology, with APOE playing a major role, many more
remain to be discovered. Clinical risk factors for AD include stroke, hypertension, T2D, obesity,
and dyslipidemia all of which are known to have a major genetic component contributing to their
etiology. However, it is currently unknown if the same variants cause AD and another risk factor
(pleiotropy), e. g. T2D, or if these effects are due to mediation. Pleiotropy, although an important
phenomenon in genetic etiology, has not been adequately studied and methods are limited to
detect pleiotropy for rare and imputed variants. Through the parent grant we have developed a
framework to study pleiotropy which can be applied to AD and its risk factors to bring about a
better understanding of AD etiology. We now have the unique ability to incorporate in this project
the study of the genetics of AD by not only leveraging our analysis framework but also data from
the UK Biobank (discovery) and AD-specific (replication) data sets. We will analyze these
datasets for cross phenotype associations and test these variants for pleiotropy using mediation
analysis for AD and a number of comorbid phenotypes, that include type 2 diabetes (T2D), stroke,
blood pressure (BP), adiposity, and blood lipids. The discovery dataset is the UK Biobank study,
a population-based prospective study that has extensive genotype and phenotype data on
~500,000 subjects from the United Kingdom. This dataset was selected as the main resource for
genotype and phenotype data because it is one of the largest population-based studies available
to the scientific community. Due to the lack of AD cases at this point in the UK Biobank, we will
apply a proxy family history phenotype. This approach was previously applied in the UK Biobank
for a number of traits, including AD, with ~314,000 subjects with information on parental history
of AD, 42,034 of whom reported at least one parent with AD. For our replication sample, we will
be using four data sets: The National Institute on Aging Late-Onset Alzheimer Disease
(NIALOAD); The Washington Heights–Inwood Columbia Aging Project (WHICAP), The Estudio
Familiar de Influencia Genetica en Alzheimer (EFIGA), and The Alzheimer’s Disease
Neuroimaging Initiative (ADNI). These data sets will provide us with WGS data for 22,924
individuals of which 10,373 are AD cases. The results from this study may help to elucidate the
causal relationship between genetic variants for AD and comorbid phenotypes.

## Key facts

- **NIH application ID:** 10123694
- **Project number:** 3R01HL145660-02S1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Andrew DeWan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $421,502
- **Award type:** 3
- **Project period:** 2019-03-15 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10123694

## Citation

> US National Institutes of Health, RePORTER application 10123694, Detecting pleiotropic effects through integration of omics data (3R01HL145660-02S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10123694. Licensed CC0.

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