# Resolving Methodological Challenges in Genomics Research: Causality, Risk Prediction, and Reproducibility

> **NIH NIH R35** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $331,923

## Abstract

Project Summary
With the availability of the high-through sequencing technology, the scientific community is now
able to investigate complex phenotypes at both organismal and molecular levels. Nevertheless,
it is still considerably difficult to perform controlled experiments and randomized trials to
investigate the causal relationships between phenotypes at different levels. It is therefore critically
important to perform causal inference based on the observational data. In this project, we will
develop computational methods to facilitate systematic investigation of causal molecular
mechanisms underlying complex disease process. Specifically, we will target three outstanding
scientific issues: i) casual inference of molecular mechanisms of complex diseases; ii) analytic
approaches for risk prediction utilizing genomic information and causal molecular mechanisms,
and iii) statistical assessment of reproducibility in high-throughput genomic experiments. Finally,
we will build user-friendly computational software packages and make them available to the broad
community of biological and medical scientists.

## Key facts

- **NIH application ID:** 10029040
- **Project number:** 1R35GM138121-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Xiaoquan Wen
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $331,923
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10029040

## Citation

> US National Institutes of Health, RePORTER application 10029040, Resolving Methodological Challenges in Genomics Research: Causality, Risk Prediction, and Reproducibility (1R35GM138121-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10029040. Licensed CC0.

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