A fast and robust strategy to remove variant level artifacts in Alzheimer’s Disease Sequencing Project data
Bibliographic record
Abstract
Abstract Whole-exome sequencing (WES) and whole-genome sequencing (WGS) are expected to be critical to further elucidate the missing genetic heritability of Alzheimer’s disease (AD) risk by identifying rare coding and/or noncoding variants that contribute to AD pathogenesis. In the United States, the Alzheimer’s Disease Sequencing Project (ADSP) has taken a leading role in sequencing AD-related samples at scale, with the resultant data being made publicly available to researchers to generate new insights into the genetic etiology of AD. In order to achieve sufficient power, the ADSP has adapted a study design where subsets of larger AD cohorts are collected and sequenced across multiple centers, using a variety of sequencing kits. This approach may lead to variable variant quality across sequencing centers and/or kits. Here, we performed exome-wide and genome-wide association analyses on AD risk using the latest ADSP WES and WGS data releases. We observed that many variants displayed large variation in allele frequencies across sequencing centers/kits and contributed to spurious association signals with AD risk. We also observed that sequencing kit/center adjustment in association models could not fully account for these spurious signals. To address this issue, we designed and implemented novel filters that aim to capture and remove these center/kit-specific artifactual variants. We conclude by deriving a novel, fast, and robust approach to filter variants that represent sequencing center- or kit-related artifacts underlying spurious associations with AD risk in ADSP WES and WGS data. This approach will be important to support future robust genetic association studies on ADSP data, as well as other studies with similar designs. Author Summary Next generation sequencing data represents a highly valuable resource to uncover rare coding and/or noncoding genetic variants that contribute to Alzheimer’s disease risk. In order to achieve large sample sizes that are required for such data, the Alzheimer’s Disease Sequencing Project (ADSP) has taken the leading role in sequencing Alzheimer’s disease related samples at scale in the United States. The ADSP’s study design however leads to variable variant quality across the involved sequencing centers, necessitating a quality control approach that ensures robust genetic association analyses. Here, we present and validate a rigorous quality control pipeline, where we specifically developed a new strategy to handle inter-center variant quality issues in the ADSP. In doing so, we provide a first glance into exome- and genome-wide associations with Alzheimer’s disease risk using the latest releases of ADSP data (respectively 20.5k and 16.9k individuals). In sum, our pipeline is important to support future robust genetic association studies on ADSP data, as well as other studies with similar design. This in turn will contribute to accelerating Alzheimer’s disease gene discovery and gene-driven therapy development.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".