# Generalizing polygenic risk prediction methods across populations for insights into psychiatric disease

> **NIH NIH R00** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $248,988

## Abstract

PROJECT SUMMARY/ABSTRACT
 Globally, mental illness is responsible for the most years lived with disability, and is especially challenging to
address due to the lack of measurable biomarkers from inaccessible brain tissue. Genetics offers an objective
measure of natural biological variability among diverse populations, providing a cornerstone of precision
medicine with especially great promise for psychiatry, both as a gene discovery tool for therapeutic targets and
as a substitute biomarker. While the Psychiatric Genomics Consortium (PGC) has amassed and jointly analyzed
large-scale case-control datasets, these and other genome-wide association studies (GWAS) are Eurocentric,
and the generalizability of these studies to diverse populations is low with standard approaches.
 The candidate hypothesizes that differences in allele frequency and the correlation structure of genetic
variants along the genome (i.e. linkage disequilibrium or LD) are the primary culprits of poor generalizability, and
proposes to test and improve the accuracy of genetic risk prediction across diverse populations. The proposed
study will: 1) quantify genetic risk prediction accuracy of schizophrenia and related disorders across diverse
populations (N≈100k cases, N≈213k controls); 2) build novel statistical methods that model LD differences
across populations to improve genetic risk prediction when GWAS results are available in one or more
populations, and risk prediction is desired in a mismatched population; and 3) build a method tailored to recently
admixed populations that jointly models the mosaic of ancestry structure and LD to improve genetic risk
prediction accuracy.
 The proposed studies and training plan were carefully designed to confer expertise in three domains: 1) the
genetics of psychiatric disorders, 2) statistical methods development, and 3) large-scale data analysis and tools.
These skills are fundamental to the candidate’s goal of becoming a leading investigator using human genetics
as a lens into the evolution of complex traits, particularly psychiatric disorders. In addition to research training,
the candidate will take coursework, participate in regular seminars, attend workshops and conferences, and gain
mentorship experience locally and in Africa. All research will be conducted in the Analytic and Translational
Genetics Unit at MGH and the Broad Institute with mentorship from Dr. Mark J. Daly, an established and prolific
leader in human genetics. Additional mentorship from leading experts, Drs. Ben Neale, Karestan Koenen, Eimear
Kenny, Jordan Smoller, and Sekar Kathiresan, ensures exceptional guidance. Overall, the training environment
is outstanding, the mentors and advisors are world-class, the proposed studies address a crucial and timely
unmet need, and the additional skills developed during this award will undoubtedly provide a strong foundation
for the candidate to establish independent leadership in population, statistical, and psychiatric genom...

## Key facts

- **NIH application ID:** 10240929
- **Project number:** 4R00MH117229-03
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Alicia Martin
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $248,988
- **Award type:** 4N
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10240929

## Citation

> US National Institutes of Health, RePORTER application 10240929, Generalizing polygenic risk prediction methods across populations for insights into psychiatric disease (4R00MH117229-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10240929. Licensed CC0.

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