Abstract 1304: The contribution of rare and low-frequency variants to colorectal cancer heritability
Bibliographic record
Abstract
Abstract Studies that estimate complex disease heritability based on genome-wide common SNP array data have shown that a large fraction of heritability is contributed from variants that do not reach genome-wide significance at current genome-wide association study (GWAS) sample sizes. The contribution of rare variants to heritability has yet to be explored for many complex diseases. Despite the decreasing cost of sequencing, it still remains prohibitively expensive to sequence sufficient samples for well-powered genetic association studies of rare variants. However, with increasingly large and denser imputation reference panels it has become feasible to accurately impute variants with minor allele frequencies (MAFs) as low as 0.1%, enabling study of a subset of rare risk variants. In previous work, we used restricted maximum likelihood (REML) to estimate the total additive heritability of colorectal cancer (CRC) based on common SNP array genotypes. Here, we expand this work using imputed genotype data and a larger sample size. We performed whole-genome sequencing of 1,961 CRC cases and 981 controls, and subsequently imputed these haplotypes into 11,895 unrelated CRC cases and 14,659 unrelated controls that are part of the Colorectal Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). We estimated heritability from individual-level imputed genotype data using LD- and MAF-stratified GREML, as implemented in GCTA. In total, we analyzed 17,649,167 imputed genetic variants with minor allele count >3. Published heritability estimates for CRC from family-based studies vary from 12% to 35%. Based on common genotyped SNPs, we previously estimated heritability to be 7.42% (95% CI: 4.71-10.12%) on the underlying liability scale, assuming a population prevalence of 0.004. For imputed genotypes, we estimate the total heritability to be 12.0% (95% CI: 9.65-14.35). Using a likelihood ratio test, we demonstrate a significant contribution of variants with MAF ≤1% to CRC genetic risk (P=0.003). Because of the imperfect imputation accuracy for very rare variants, their contribution is likely higher. These results suggest that with additional sequencing, improved imputation accuracy, and larger GWAS, we should expect to start discovering rare variant associations for CRC risk. Citation Format: Jeroen R. Huyghe, Sai Chen, Hyun M. Kang, Tabitha Harrison, Sonja I. Berndt, Stephane Bézieau, Hermann Brenner, Graham Casey, Andrew T. Chan, Jenny Chang-Claude, Steven J. Gallinger, Stephen B. Gruber, Andrea Gsur, Michael Hoffmeister, Thomas Hudson, Mark A. Jenkins, Loic Le Marchand, Polly A. Newcomb, John D. Potter, Conghui Qu, Martha L. Slattery, Joshua D. Smith, Emily White, Goncalo R. Abecasis, Li Hsu, Deborah A. Nickerson, Ulrike Peters, on behalf of CCFR and GECCO. The contribution of rare and low-frequency variants to colorectal cancer heritability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1304. doi:10.1158/1538-7445.AM2017-1304
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".