Imputed Genomic Data Reveals a Moderate Effect of Low Frequency Variants to the Heritability of Complex Human Traits
Bibliographic record
Abstract
Abstract The genetic architecture of complex human traits remains largely unknown. The distribution of heritability across the minor allele frequency (MAF) spectrum for a trait will be a function of the MAF of its causal variants and their effect sizes. Assumptions about these relationships underpin the tools used to estimate heritability. We examine the performance of two widely used tools, Haseman-Elston (HE) Regression and genomic-relatedness-based restricted maximum-likelihood (GREML). Our simulations show that HE is less biased than GREML under a wide variety of models and that the estimated standard error for HE tends to be substantially overestimated. We then applied HE Regression to infer the heritability of 72 quantitative biomedical traits from up to 50,000 individuals with genotype and imputation data from the UK Biobank. We found that adding each individuals’ geolocation as covariates corrected for population stratification that could not be accounted for by principal components alone (particularly for rare variants). The biomedical traits we analyzed had an average heritability of 0.27, with low frequency variants (MAF≤0.05) explaining an average of 47.7% of the total heritability (and lower frequency variants with MAF≤0.02 explaining a majority of our increased heritability over previous estimates). Variants in regions of low linkage disequilibrium (LD) accounted for 3.3-fold more heritability than the variants in regions of high LD, an effect primarily driven by low frequency variants. These findings suggest a moderate action of negative selection on the causal variants of these traits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".