{"doi":"10.17615/pbzs-pw19","title":"Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT)","abstract":"Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.","journal":"UNC Libraries","year":2020,"id":140582,"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":0.9591,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":29693,"name":"Judong Shen","orcid":"0000-0001-6150-1034","position":1,"is_corresponding":false},{"id":463453,"name":"Yun Li","orcid":"0000-0001-8414-2724","position":2,"is_corresponding":false},{"id":605388,"name":"Eugene Urrutia","orcid":null,"position":3,"is_corresponding":false},{"id":288813,"name":"Seunggeun Lee","orcid":"0000-0002-8097-3878","position":4,"is_corresponding":false},{"id":36362,"name":"Ni Zhao","orcid":"0000-0002-7762-3949","position":5,"is_corresponding":false},{"id":536644,"name":"Arnab Maity","orcid":"0000-0002-3364-8933","position":6,"is_corresponding":false},{"id":318790,"name":"Michael C. Wu","orcid":"0000-0002-3357-6570","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-18T23:17:17.077903Z","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":[]}