{"doi":"10.7302/2978","title":"Novel Score Tests to Increase Power in Association Test by Integrating External Controls","abstract":"Recent advances in genotyping and sequencing technologies have enabled genetic association studies to leverage high-quality genotype or sequence data to identify high-impact variants accounting for a substantial portion of disease risk. The usage of external controls, whose genomes have already been genotyped and are publicly available, could be a cost-effective approach to increase the power of association testing. Various challenges in practice, however, hinder the use of external sources of controls, among which include differences in sequencing platforms, genotype calling procedures, population stratification, etc. Differences in these aspects could lead to a systematic batch effect between genetic data in different studies. There has been recent effort to integrate external controls while adjusting for possible batch effects, such as the integrating External Controls into Association Test (iECAT). The original iECAT test, however, cannot adjust for covariates such as age, gender, etc. Hence, based on the insight of iECAT, we propose a novel score-based test, iECAT-Score, that allows for covariate adjustment and constructs a shrinkage score statistic that is a weighted sum of the score statistics using exclusively internal samples and uses both internal and external control samples. We show by simulation studies that our method has increased power over the original iECAT while controlling for type I error rates. We present the application of our method to the association studies of age-related macular degeneration (AMD) utilizing data from the International AMD Genomics Consortium (IAMDGC) and Michigan Genomics Initiative (MGI). The iECAT-Score test has improved power for testing association between a single variant and the disease status, and yet single-variant tests could be underpowered to identify causal rare variants. Hence, in the second project, we extend the single-variant iECAT-Score test to a region-based test, which assesses the combined genetic effect of rare variants within a gene or region. The iECAT-Score region-based test aggregates the single-variant test statistics using a weighted linear or quadratic sum, or a linear combination of both. Through simulation studies and the application of our method to the rare-variant association studies of AMD from the IAMDGC and UK Biobank data, we show that our proposed iECAT-Score region-based test efficiently identifies disease-associated genes while controlling for type I error rates. When sequenced data are used in association studies, quality of the genotype calls could influence the performance of the testing methods. The quality of genotype calls is subject to many factors such as read depth, genotype-calling error rates, quality control (QC) pipelines, etc., all of which could result in bias in the estimation of minor allele frequencies (MAFs), leading to more profound batch effect between internal and external control samples. As whole genome/exome sequencing become the design of choice, to address the associated problems using genotyped data, we propose in the third project to integrate the above-mentioned QC parameters utilizing sequencing data. Through the incorporation of these factors, we develop a framework of integrating external controls that is applicable to both genotyped and sequencing data, further honing the statistical methods needed to identify disease-causing variants within the human genome.","journal":"Deep Blue (University of Michigan)","year":2021,"id":229837,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9486,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":834575,"name":"Yatong Li","orcid":"0009-0003-2337-9930","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-18T23:55:01.675482Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}