{"doi":"10.1007/s40484-017-0124-3","title":"On the use of kernel machines for Mendelian randomization","abstract":"<jats:sec><jats:title>Background</jats:title><jats:p>Properly adjusting for unmeasured confounders is critical for health studies in order to achieve valid testing and estimation of the exposure’s causal effect on outcomes. The instrumental variable (IV) method has long been used in econometrics to estimate causal effects while accommodating the effect of unmeasured confounders. Mendelian randomization (MR), which uses genetic variants as the instrumental variables, is an application of the instrumental variable method to biomedical research fields, and has become popular in recent years. One often‐used estimator of causal effects for instrumental variables and Mendelian randomization is the two‐stage least square estimator (TSLS). The validity of TSLS relies on the accurate prediction of exposure based on IVs in its first stage.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this note, we propose to model the link between exposure and genetic IVs using the least‐squares kernel machine (LSKM). Some simulation studies are used to evaluate the feasibility of LSKM in TSLS setting.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our results show that LSKM based on genotype score or genotype can be used effectively in TSLS. It may provide higher power when the association between exposure and genetic IVs is nonlinear.</jats:p></jats:sec>","journal":"Quantitative Biology","year":2017,"id":636120,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":943252,"name":"Debashis Ghosh","orcid":"0000-0001-8810-6392","position":1,"is_corresponding":false},{"id":361932,"name":"Weiming Zhang","orcid":"0000-0002-1953-4016","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"On the use of kernel machines for Mendelian randomization","abstract":"<jats:sec><jats:title>Background</jats:title><jats:p>Properly adjusting for unmeasured confounders is critical for health studies in order to achieve valid testing and estimation of the exposure’s causal effect on outcomes. The instrumental variable (IV) method has long been used in econometrics to estimate causal effects while accommodating the effect of unmeasured confounders. Mendelian randomization (MR), which uses genetic variants as the instrumental variables, is an application of the instrumental variable method to biomedical research fields, and has become popular in recent years. One often‐used estimator of causal effects for instrumental variables and Mendelian randomization is the two‐stage least square estimator (TSLS). The validity of TSLS relies on the accurate prediction of exposure based on IVs in its first stage.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this note, we propose to model the link between exposure and genetic IVs using the least‐squares kernel machine (LSKM). Some simulation studies are used to evaluate the feasibility of LSKM in TSLS setting.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our results show that LSKM based on genotype score or genotype can be used effectively in TSLS. It may provide higher power when the association between exposure and genetic IVs is nonlinear.</jats:p></jats:sec>","is_dataset_classified":null,"base_score":2.302585092994046,"endowment":2.302585092994046,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"30221016","pmcid":"PMC6135259","openalex_id":"https://openalex.org/W2769430900","authors":[],"funders":[{"funder_name":"National Science Foundation","grant_id":"NSF ABI 1457935","title":null},{"funder_name":"National Institutes of Health","grant_id":"R01 GM117946","title":null},{"funder_name":"National Institutes of Health","grant_id":"5R01GM117946-03","title":"Statistical Tests for Mapping Genetic Determinants of Complex Traits"},{"funder_name":"National Science Foundation","grant_id":"1457935","title":"Multivariate Statistical Methods for Genomic Data Integration"}],"total_grants":4,"fwci":0.2004,"citation_percentile":0.65354549,"influential_citations":0,"citation_trend":[{"year":2020,"count":1},{"year":2022,"count":2},{"year":2023,"count":1},{"year":2024,"count":2},{"year":2025,"count":3}],"oa_status":"gold","license":"Wiley Online Library User Agreement","oa_locations":[{"url":"https://journal.hep.com.cn/qb/EN/PDF/10.1007/s40484-017-0124-3","host_type":"journal"},{"url":"https://journal.hep.com.cn/qb/EN/PDF/10.1007/s40484-017-0124-3","host_type":"publisher"},{"url":"http://link.springer.com/article/10.1007/s40484-017-0124-3/fulltext.html","host_type":"publisher"},{"url":"http://link.springer.com/content/pdf/10.1007/s40484-017-0124-3.pdf","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1007/s40484-017-0124-3","host_type":"publisher"},{"url":"https://doi.org/10.1007/s40484-017-0124-3","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/30221016","host_type":"repository"},{"url":"http://europepmc.org/pmc/articles/PMC6135259","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6135259","host_type":"repository"},{"url":"https://link.springer.com/content/pdf/10.1007%2Fs40484-017-0124-3.pdf","host_type":""},{"url":"https://dx.doi.org/10.1007/s40484-017-0124-3","host_type":""}],"fields_of_study":["Genetic Associations and Epidemiology","Advanced Causal Inference Techniques","Statistical Methods and Inference","0301 basic medicine","03 medical and health sciences","0101 mathematics","01 natural sciences"],"mesh_terms":[],"keywords":["Mendelian randomization","Instrumental variable","Confounding","Estimator","Causal inference","Econometrics","Statistics","Kernel (algebra)","Computer science","Mathematics","Genotype","Biology","Genetic variants","Genetics","Casual Inference","Kernel Machine","Unmeasured Confounder"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T16:16:16.191099Z","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":[]}