{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T02:14:11Z","timestamp":1789697651991,"version":"4.0.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Data cleaning is an important first step in most statistical analyses, including efforts to map the genetic loci that contribute to variation in quantitative traits. Here we illustrate approaches to quality control and cleaning of array-based genotyping data for multiparent populations (experimental crosses derived from more than two founder strains), using MegaMUGA array data from a set of 291 Diversity Outbred (DO) mice. Our approach employs data visualizations that can reveal problems at the level of individual mice or with individual SNP markers. We find that the proportion of missing genotypes for each mouse is an effective indicator of sample quality. We use microarray probe intensities for SNPs on the X and Y chromosomes to confirm the sex of each mouse, and we use the proportion of matching SNP genotypes between pairs of mice to detect sample duplicates. We use a hidden Markov model (HMM) reconstruction of the founder haplotype mosaic across each mouse genome to estimate the number of crossovers and to identify potential genotyping errors. To evaluate marker quality, we find that missing data and genotyping error rates are the most effective diagnostics. We also examine the SNP genotype frequencies with markers grouped according to their minor allele frequency in the founder strains. For markers with high apparent error rates, a scatterplot of the allele-specific probe intensities can reveal the underlying cause of incorrect genotype calls. The decision to include or exclude low-quality samples can have a significant impact on the mapping results for a given study. We find that the impact of low-quality markers on a given study is often minimal, but reporting problematic markers can improve the utility of the genotyping array across many studies.<\/jats:p>","DOI":"10.1534\/g3.119.400165","type":"journal-article","created":{"date-parts":[[2019,3,15]],"date-time":"2019-03-15T23:16:47Z","timestamp":1552691807000},"page":"1571-1579","source":"Crossref","is-referenced-by-count":28,"title":["Cleaning Genotype Data from Diversity Outbred Mice"],"prefix":"10.1093","volume":"9","author":[{"given":"Karl W","family":"Broman","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, University of Wisconsin\u2013Madison, Madison, Wisconsin 53706"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel M","family":"Gatti","sequence":"additional","affiliation":[{"name":"The Jackson Laboratory, Bar Harbor, Maine 04609"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen L","family":"Svenson","sequence":"additional","affiliation":[{"name":"The Jackson Laboratory, Bar Harbor, Maine 04609"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u015aaunak","family":"Sen","sequence":"additional","affiliation":[{"name":"Department of Preventive Medicine, University of Tennessee Health Sciences Center, Memphis, Tennessee 38163"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gary A","family":"Churchill","sequence":"additional","affiliation":[{"name":"The Jackson Laboratory, Bar Harbor, Maine 04609"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,5,1]]},"reference":[{"key":"2021101606435457600_bib1","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1534\/genetics.111.132647","article-title":"Genotype probabilities at intermediate generations in the construction of recombinant inbred lines.","volume":"190","author":"Broman","year":"2012","journal-title":"Genetics"},{"key":"2021101606435457600_bib2","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1534\/g3.111.001818","article-title":"Haplotype probabilities in advanced intercross populations.","volume":"2","author":"Broman","year":"2012","journal-title":"G3 (Bethesda)"},{"key":"2021101606435457600_bib3","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1534\/genetics.114.172742","article-title":"R\/qtlcharts: interactive graphics for quantitative trait locus mapping.","volume":"199","author":"Broman","year":"2015","journal-title":"Genetics"},{"key":"2021101606435457600_bib4","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1534\/genetics.118.301595","article-title":"R\/qtl2: Software for mapping quantitative trait loci with high-dimensional data and multi-parent populations.","volume":"211","author":"Broman","year":"2019","journal-title":"Genetics"},{"key":"2021101606435457600_bib5","doi-asserted-by":"publisher","first-page":"2177","DOI":"10.1534\/g3.115.019778","article-title":"Identification and correction of sample mix-ups in expression genetic data: A case study.","volume":"5","author":"Broman","year":"2015","journal-title":"G3 (Bethesda)"},{"key":"2021101606435457600_bib6","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-92125-9","volume-title":"A guide to QTL mapping with R\/qtl","author":"Broman","year":"2009"},{"key":"2021101606435457600_bib7","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/BF02996226","article-title":"Stocks for detecting linkage in the mouse and the theory of their design.","volume":"50","author":"Carter","year":"1951","journal-title":"J. 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