# Whole chromosome fine-mapping integrating diverse ancestries for psychiatric disorders

> **NIH NIH K99** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $129,288

## Abstract

PROJECT SUMMARY ABSTRACT
 Statistical fine-mapping identifies a handful of putative causal variants from hundreds of GWAS loci for
psychiatric disorders, but current findings are primarily based on analysis of the European cohort, leading to bias
in causal genetic variant discovery and limiting the resolution of findings. The status quo persists due to the
lacking of data and the unavailability of suitable methods. Several large international genetic projects, including
PUMAS and A-BIG-NET, have recently launched to target non-European populations and make data more
accessible. The absence of proper fine-mapping methods for populations with complex genetic structures is a
significant hindrance in the field.
 The candidate proposes to address gaps between the forthcoming data and appropriate methods by
developing a suite of open-source statistical methods and publicly available analytical resources. The candidate
will: 1) develop a local-ancestry-aware admixed population fine-mapping method, enabling the fine-mapping of
entire chromosome data by optimizing the algorithm and effectively managing memory; 2) develop a burden test
approach to prioritize putative causal genes for psychiatric disorders by fine-mapping the gene-based burden of
Neanderthal introgressed sequences; 3) develop the most inclusive fine-mapping method for psychiatric
disorders by integrating fine-mapped results across diverse ancestries.
 The proposed research and training plan was carefully designed to confer expertise in four domains: 1)
psychiatric genetics and psychiatric phenotyping, 2) statistical methods development and software engineering,
3) large-scale Blended Genome and Exome (BGE) sequencing data analysis, 4) admixed population genomic
data analysis, and 5) professional development. These skills are fundamental to the candidate’s goal of
becoming a leading investigator who develops and applies statistical methods to understand underlying causal
genetic factors for psychiatric disorders. In addition to research training, the candidate will take coursework to
gain greater expertise in statistical method development, participate in regular seminars, attend workshops and
conferences, and gain mentorship and teaching experience. All research will be conducted in the Analytic and
Translational Genetics Unit at Massachusetts General Hospital and the Broad Institute with mentorship from
renowned scientists Drs. Hailiang Huang, Tian Ge and Jordan Smoller. Additional guidance from leading experts
Drs. Benjamin Neale, Kenneth Kendler, Xiaofeng Zhu, and Elizabeth Atkinson will ensure exceptional guidance
and support. Overall, the training environment is outstanding, the mentors and advisors are world-class, the
proposed studies address an urgent unmet need, and the additional skills gained in this award will poise the
candidate to establish independent leadership in inclusive fine-mapping analysis for psychiatric disorders.

## Key facts

- **NIH application ID:** 10984631
- **Project number:** 1K99MH135172-01A1
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Kai Yuan
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $129,288
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10984631

## Citation

> US National Institutes of Health, RePORTER application 10984631, Whole chromosome fine-mapping integrating diverse ancestries for psychiatric disorders (1K99MH135172-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10984631. Licensed CC0.

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