# Understanding the "flattening" of gene contributions to human complex trait habitability

> **NIH NIH F32** · STANFORD UNIVERSITY · 2022 · $72,302

## Abstract

Genome-wide association studies (GWAS) indicate that heritable variation for most human complex
traits is widely distributed across the genome. This is surprising since we expect each trait to have
specific mechanisms and pathways underlying its biology and yet, for most traits, such mechanisms
and pathways are not evident from GWAS results. Recently, it has been suggested that this
phenomenon might be explained by the natural selection’s suppression of heritability. If variants at
genes directly underlying trait biology have large effect sizes on the trait which cause increased
selection, they will only appear at low frequencies and therefore only make a limited contribution to
heritability. This intuition has recently been dubbed “flattening” because, by keeping large effect alleles
at low frequencies, natural selection flattens the distribution of heritability. In this work, I will take the
concept of “flattening” from basic intuition to fully fleshed out theory and from theory to the first genome-
wide measure of flattening per gene per trait. First, I will build a series of models to describe the
evolutionary and genomic factors that determine flattening, including pleiotropy and human
demographic history. Then, I will validate my models by directly quantifying the effects of flattening on
38 blood and urine markers using UK Biobank data. These biomarkers provide a unique and exciting
opportunity to test my predictions because we know the regulatory networks underlying the synthesis
of these biomarkers. Finally, I will estimate the heritability deficit of each gene for each trait by
comparing the gene’s contribution to heritability to a neutral expectation. The heritability deficit will be
the first ever genome-wide measure of flattening per gene per trait and I will use it to characterize the
extent of flattening on available UK Biobank traits.

## Key facts

- **NIH application ID:** 10376272
- **Project number:** 5F32HG011202-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Yuval B Simons
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $72,302
- **Award type:** 5
- **Project period:** 2020-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10376272

## Citation

> US National Institutes of Health, RePORTER application 10376272, Understanding the "flattening" of gene contributions to human complex trait habitability (5F32HG011202-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10376272. Licensed CC0.

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