{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T21:39:53Z","timestamp":1768513193282,"version":"3.49.0"},"posted":{"date-parts":[[2024,8,26]]},"group-title":"Bioinformatics","reference-count":13,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:00:00Z","timestamp":1724630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,8,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Genotyping single nucleotide polymorphisms (SNPs) is fundamental to disease research, as researchers seek to establish links between genetic variation and disease. Although significant advances in genome technology have been made with the development of bead-based SNP genotyping and Genome Studio software, some SNPs still fail to be genotyped, resulting in \u201cno-calls\u201d that impede downstream analyses. To recover these genotypes, we introduce Cluster Buster, a genotyping neural network and visual inspection system designed to improve the quality of neurodegenerative disease (NDD) research. Concordance analysis with whole genome sequencing (WGS) and imputed genotypes validated the reliability of predicted genotypes, with dozens of high-performing SNPs across\n                  <jats:italic>LRRK2, APOE<\/jats:italic>\n                  , and\n                  <jats:italic>GBA<\/jats:italic>\n                  loci achieving at least 90% concordance per SNP location. Further analysis of concordance between Genome Studio genotypes and imputed and WGS genotypes revealed discrepancies between the genotyping technologies, highlighting the need for selective application of Cluster Buster on SNP locations based on concordance rates. Cluster Buster\u2019s implementation significantly reduces manual labor for recovering no-call SNPs, refining genotype quality for the Global Parkinson\u2019s Genetics Program (GP2). This system facilitates better imputation and GWAS outcomes, ultimately contributing to a deeper understanding of genetic factors in NDDs.\n                <\/jats:p>","DOI":"10.1101\/2024.08.23.609429","type":"posted-content","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T11:35:46Z","timestamp":1724931346000},"source":"Crossref","is-referenced-by-count":0,"title":["Cluster Buster: A Machine Learning Algorithm for Genotyping SNPs from Raw Data"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0830-9289","authenticated-orcid":false,"given":"Jessica","family":"Martin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9847-0282","authenticated-orcid":false,"given":"Nicole","family":"Kuznetsov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5702-0980","authenticated-orcid":false,"given":"Kristin","family":"Levine","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4341-3991","authenticated-orcid":false,"given":"Mathew J.","family":"Koretsky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8968-6461","authenticated-orcid":false,"given":"Samantha","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0319-4325","authenticated-orcid":false,"given":"Mike A.","family":"Nalls","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0637-3671","authenticated-orcid":false,"given":"Dan","family":"Vitale","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2024082908350680000_2024.08.23.609429v1.1","doi-asserted-by":"publisher","DOI":"10.4088\/JCP.v68n0419"},{"key":"2024082908350680000_2024.08.23.609429v1.2","doi-asserted-by":"publisher","DOI":"10.1101\/2024.03.26.586362"},{"key":"2024082908350680000_2024.08.23.609429v1.3","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3656"},{"key":"2024082908350680000_2024.08.23.609429v1.4","doi-asserted-by":"publisher","DOI":"10.5281\/ZENODO.7904831"},{"key":"2024082908350680000_2024.08.23.609429v1.5","unstructured":"Illumina Inc. (2005). Illumina GenCall Data Analysis Software. San Diego. https:\/\/www.illumina.com\/Documents\/products\/technotes\/technote_gencall_data_analysis_software.pdf"},{"key":"2024082908350680000_2024.08.23.609429v1.6","unstructured":"Illumina Inc. (2010). Interpreting Infinium\u00ae Assay Data for Whole-Genome Structural Variation. San Diego. https:\/\/www.illumina.com\/Documents\/products\/technotes\/technote_cytoanalysis.pdf"},{"key":"2024082908350680000_2024.08.23.609429v1.7","unstructured":"Mart\u00edn Abadi , Ashish Agarwal , Paul Barham , Eugene Brevdo , Zhifeng Chen , Craig Citro , Greg S. Corrado , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Ian Goodfellow , Andrew Harp , Geoffrey Irving , Michael Isard , Rafal Jozefowicz , Yangqing Jia , Lukasz Kaiser , Manjunath Kudlur , Josh Levenberg , Dan Man\u00e9 , Mike Schuster , Rajat Monga , Sherry Moore , Derek Murray , Chris Olah , Jonathon Shlens , Benoit Steiner , Ilya Sutskever , Kunal Talwar , Paul Tucker , Vincent Vanhoucke , Vijay Vasudevan , Fernanda Vi\u00e9gas , Oriol Vinyals , Pete Warden , Martin Wattenberg , Martin Wicke , Yuan Yu , and Xiaoqiang Zheng (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org."},{"key":"2024082908350680000_2024.08.23.609429v1.8","unstructured":"Morico, M. (2023, January 17). About GP2. GP2. https:\/\/gp2.org\/about-gp2\/"},{"key":"2024082908350680000_2024.08.23.609429v1.9","unstructured":"O\u2019Malley, Tom and Bursztein, Elie and Long, James and Chollet, Francois and Jin, Haifeng and Invernizzi, Luca and others, 2019. https:\/\/github.com\/keras-team\/keras-tuner"},{"key":"2024082908350680000_2024.08.23.609429v1.10","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-60327-411-1_1"},{"key":"2024082908350680000_2024.08.23.609429v1.11","unstructured":"Shaun Purcell , Christopher Chang . 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