{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T15:36:07Z","timestamp":1772638567805,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CIMB"],"abstract":"<jats:p>Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with complex traits and diseases, providing critical insights into genetic architecture, biological pathways, and disease mechanisms. With the advance of machine learning, the analytical scope of GWAS can be substantially expanded by enabling joint modeling, nonlinear effects, and integrative analysis. However, deep learning approaches remain underutilized in augmenting traditional GWAS frameworks, particularly in the presence of cryptic relatedness among sampled individuals. In this paper, we propose a deep neural network (DNN)-based machine learning method to assist genetic association testing in samples with related individuals. By approximating the phenotype\u2013genotype relationships in classical association tests and combining approximations across multiple tests, the proposed method aims to improve predictive performance in the identification of associated variants. Simulation studies demonstrate that our approach effectively complements conventional statistical methods and generally achieves increased power for detecting genetic associations. We further apply the method to data from the Framingham Heart Study, illustrating how DNN-based machine learning can facilitate the identification of genome-wide SNPs associated with average systolic blood pressure.<\/jats:p>","DOI":"10.3390\/cimb48030273","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T08:51:33Z","timestamp":1772614293000},"page":"273","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep-Neural-Network-Aided Genetic Association Testing in Samples with Related Individuals"],"prefix":"10.3390","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9916-3624","authenticated-orcid":false,"given":"Xiaowei","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Statistics, Virginia Tech, 250 Drillfield Drive, Blacksburg, VA 24061, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"D1005","DOI":"10.1093\/nar\/gky1120","article-title":"The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019","volume":"47","author":"Buniello","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bush, W.S., and Moore, J.H. (2012). Chapter 11: Genome-wide association studies. PLoS Comput. Biol., 8.","DOI":"10.1371\/journal.pcbi.1002822"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1214\/09-STS307","article-title":"Population structure and cryptic relatedness in genetic association studies","volume":"24","author":"Astle","year":"2009","journal-title":"Stat. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1111\/j.0006-341X.2004.00165.x","article-title":"Generalized linear mixed models with varying coefficients for longitudinal data","volume":"60","author":"Zhang","year":"2004","journal-title":"Biometrics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1002\/sim.6323","article-title":"Generalized estimating equations for genome-wide association studies using longitudinal phenotype data","volume":"34","author":"Sitlani","year":"2015","journal-title":"Stat. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1214\/13-AOAS692","article-title":"A functional data analysis approach for genetic association studies","volume":"8","author":"Reimherr","year":"2014","journal-title":"Ann. Appl. Stat."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1016\/j.ajhg.2010.04.005","article-title":"Pooled association tests for rare variants in exon-resequencing studies","volume":"86","author":"Price","year":"2010","journal-title":"Am. J. Hum. Genet."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.ajhg.2011.05.029","article-title":"Rare-variant association testing for sequencing data with the sequence kernel association test","volume":"89","author":"Wu","year":"2011","journal-title":"Am. J. Hum. Genet."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1038\/s41576-019-0127-1","article-title":"Benefits and limitations of genome-wide association studies","volume":"20","author":"Tam","year":"2019","journal-title":"Nat. Rev. Genet."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Schuran, M., Goudey, B., Dite, G.S., and Makalic, E. (2025). A survey on deep learning for polygenic risk scores. Brief. Bioinform., 26.","DOI":"10.1093\/bib\/bbaf373"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vitsios, D., Dhindsa, R., Middleton, L., Gussow, A.B., and Petrovski, S. (2021). Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning. Nat. Commun., 12.","DOI":"10.1038\/s41467-021-21790-4"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cui, T., El Mekkaoui, K., Reinvall, J., Havulinna, A.S., Marttinen, P., and Kaski, S. (2022). Gene\u2013gene interaction detection with deep learning. Commun. Biol., 5.","DOI":"10.1038\/s42003-022-04186-y"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bai\u00e3o, A.R., Cai, Z., Poulos, R.C., Robinson, P.J., Reddel, R.R., Zhong, Q., Vinga, S., and Gon\u00b8calves, E. (2025). A technical review of multi-omics data integration methods: From classical statistical to deep generative approaches. Brief. Bioinform., 26.","DOI":"10.1093\/bib\/bbaf355"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bromberg, Y., Prabakaran, R., Kabir, A., and Shehu, A. (2024). Variant effect prediction in the age of machine learning. Cold Spring Harb. Perspect. Biol., 16.","DOI":"10.1101\/cshperspect.a041467"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sun, S., Dong, B., and Zou, Q. (2021). Revisiting genome-wide association studies from statistical modelling to machine learning. Brief. Bioinform., 22.","DOI":"10.1093\/bib\/bbaa263"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sigala, R.E., Lagou, V., Shmeliov, A., Atito, S., Kouchaki, S., Awais, M., Prokopenko, I., Mahdi, A., and Demirkan, A. (2023). Machine learning to advance human genome-wide association studies. Genes, 15.","DOI":"10.20944\/preprints202311.1819.v1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.ajhg.2013.03.014","article-title":"MASTOR: Mixed-model association mapping of quantitative traits in samples with related individuals","volume":"92","author":"Jakobsdottir","year":"2013","journal-title":"Am. J. Hum. Genet."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1111\/j.0006-341X.2004.00180.x","article-title":"Best linear unbiased allele-frequency estimation in complex pedigrees","volume":"60","author":"McPeek","year":"2004","journal-title":"Biometrics"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Adams, A., and Hudson, R. (2004). Maximum-likelihood estimation of demographic parameters using the frequency spectrum of unlinked single-nucleotide polymorphisms. Genetics, 168.","DOI":"10.1534\/genetics.104.030171"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1038\/ng2116","article-title":"Measurement of the human allele frequency spectrum demonstrates greater genetic drift in East Asians than in Europeans","volume":"39","author":"Keinan","year":"2007","journal-title":"Nat. Genet."},{"key":"ref_21","first-page":"283","article-title":"Strategies for characterizing highly polymorphic markers in human gene mapping","volume":"51","author":"Ott","year":"1992","journal-title":"Am. J. Hum. Genet."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1002\/1098-2272(200101)20:1<17::AID-GEPI3>3.0.CO;2-Q","article-title":"A Bayesian hierarchical model for allele frequencies","volume":"20","author":"Lockwood","year":"2001","journal-title":"Genet. Epidemiol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mandal, D.M., Sorant, A.J., and Atwood, L.D. (2006). Allele frequency misspecification: Effect on power and Type I error of model-dependent linkage analysis of quantitative traits under random ascertainment. BMC Genet., 7.","DOI":"10.1186\/1471-2156-7-21"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1126\/science.1142382","article-title":"A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants","volume":"316","author":"Scott","year":"2007","journal-title":"Science"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1038\/nrg2554","article-title":"Human genetic variation and its contribution to complex traits","volume":"10","author":"Frazer","year":"2009","journal-title":"Nat. Rev. Genet."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1086\/420855","article-title":"Design and analysis of admixture mapping studies","volume":"74","author":"Hoggart","year":"2004","journal-title":"Am. J. Hum. Genet."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1086\/425281","article-title":"Statistical tests for admixture mapping with case\u2013control and cases-only data","volume":"75","author":"Montana","year":"2004","journal-title":"Am. J. Hum. Genet."},{"key":"ref_28","first-page":"22","article-title":"Allele frequency estimation from data on relatives","volume":"48","author":"Boehnke","year":"1991","journal-title":"Am. J. Hum. Genet."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1111\/j.1469-1809.1955.tb01360.x","article-title":"The estimation of gene frequencies in a random- mating population","volume":"20","author":"Ceppellini","year":"1955","journal-title":"Ann. Hum. Genet."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1111\/j.1469-1809.1972.tb00287.x","article-title":"Counting methods in genetical statistics","volume":"21","author":"Smith","year":"1957","journal-title":"Ann. Hum. Genet."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"665","DOI":"10.2307\/2532781","article-title":"Robust estimation of gene frequency and association parameters","volume":"50","author":"Olson","year":"1994","journal-title":"Biometrics"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Saputra, N.A., Riza, L.S., Setiawan, A., and Hamidah, I. (2024). A Systematic Review for Classification and Selection of Deep Learning Methods. Decis. Anal. J., 12.","DOI":"10.1016\/j.dajour.2024.100489"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"676","DOI":"10.6339\/25-JDS1167","article-title":"A Statistician\u2019s Selective Review of Neural Network Modeling: Algorithms and Applications","volume":"23","author":"Zhang","year":"2025","journal-title":"J. Data Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shen, X., Jiang, C., Wen, Y., Li, C., and Lu, Q. (2022). A Brief Review on Deep Learning Applications in Genomic Studies. Front. Syst. Biol., 2.","DOI":"10.3389\/fsysb.2022.877717"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kelemen, M., Xu, Y., Jiang, T., Zhao, J.H., Anderson, C.A., Wallace, C., Butterworth, A., and Inouye, M. (2025). Performance of deep-learning-based approaches to improve polygenic scores. Nat. Commun., 16.","DOI":"10.1038\/s41467-025-60056-1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ghose, U., Sproviero, W., Winchester, L., Amin, N., Zhu, T., Newby, D., Ulm, B.S., Papathanasiou, A., Shi, L., and Liu, Q. (2024). Genome-wide association neural networks identify genes linked to family history of Alzheimer\u2019s disease. Brief Bioinform., 26.","DOI":"10.1093\/bib\/bbae704"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, Y., Wen, J., Li, G., Chen, J., Sun, Q., Liu, W., Guan, W., Lai, B., Szatkiewicz, J., and He, X. (2023). DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network. Res. Sq.","DOI":"10.21203\/rs.3.rs-2399024\/v1"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0893-6080(05)80131-5","article-title":"Multilayer feedforward networks with a nonpolynomial activation function can approximate any function","volume":"6","author":"Leshno","year":"1993","journal-title":"Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1093\/aje\/kwm021","article-title":"The third generation cohort of the National Heart, Lung, and Blood Institute\u2019s Framingham Heart Study: Design, recruitment, and initial examination","volume":"165","author":"Splansky","year":"2007","journal-title":"Am. J. Epidemiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1161\/01.HYP.0000044938.94050.E3","article-title":"Antihypertensive treatments obscure familial contributions to blood pressure variation","volume":"41","author":"Cui","year":"2003","journal-title":"Hypertension"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2911","DOI":"10.1002\/sim.2165","article-title":"Adjusting for treatment effects in studies of quantitative traits: Antihypertensive therapy and systolic blood pressure","volume":"24","author":"Tobin","year":"2005","journal-title":"Stat. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.ajhg.2018.02.016","article-title":"L-GATOR: Genetic Association Testing for a Longitudinally Measured Quantitative Trait in Samples with Related Individuals","volume":"102","author":"Wu","year":"2018","journal-title":"Am. J. Hum. Genet."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1038\/ng.922","article-title":"Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure","volume":"43","author":"Wain","year":"2011","journal-title":"Nat. Genet."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1038\/ng.361","article-title":"Genome-wide association study identifies eight loci associated with blood pressure","volume":"41","author":"Johnson","year":"2009","journal-title":"Nat. Genet."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/nature10405","article-title":"Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk","volume":"478","author":"Ehret","year":"2011","journal-title":"Nature"},{"key":"ref_48","unstructured":"Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J. (2017, January 24\u201326). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. Proceedings of the International Conference on Learning Representations, Toulon, France."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1038\/s41588-019-0481-0","article-title":"A global overview of pleiotropy and genetic architecture in complex traits","volume":"51","author":"Watanabe","year":"2019","journal-title":"Nat. Genet."}],"container-title":["Current Issues in Molecular Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1467-3045\/48\/3\/273\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T09:44:18Z","timestamp":1772617458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1467-3045\/48\/3\/273"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["cimb48030273"],"URL":"https:\/\/doi.org\/10.3390\/cimb48030273","relation":{},"ISSN":["1467-3045"],"issn-type":[{"value":"1467-3045","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]}}}