
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.08.23.609429
preprint
1
Article
Cluster Buster: A Machine Learning Algorithm for Genotyping SNPs from Raw Data
Martin Jessica http://orcid.org/0009-0000-0830-9289

Kuznetsov Nicole http://orcid.org/0009-0005-9847-0282

Levine Kristin http://orcid.org/0000-0002-5702-0980

Koretsky Mathew J. http://orcid.org/0000-0003-4341-3991

Hong Samantha http://orcid.org/0009-0001-8968-6461

Nalls Mike A. http://orcid.org/0000-0003-0319-4325

Vitale Dan http://orcid.org/0000-0002-0637-3671

26 8 2024
2024.08.23.609429https://creativecommons.org/publicdomain/zero/1.0/ To the extent possible under law, the person who associated CC0 with this work has waived all copyright and related or neighboring rights to this work.
http://biorxiv.org/lookup/doi/10.1101/2024.08.23.609429
nihpp-2024.08.23.609429.pdf
Abstract

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 “no-calls” 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 LRRK2, APOE , and GBA 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’s implementation significantly reduces manual labor for recovering no-call SNPs, refining genotype quality for the Global Parkinson’s Genetics Program (GP2). This system facilitates better imputation and GWAS outcomes, ultimately contributing to a deeper understanding of genetic factors in NDDs.
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pmc
