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Quality control of genotypes using heritability estimates of gene content at the marker

Colaborador(es): Forneris, Natalia Soledad. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Buenos Aires, Argentina. CONICET. Buenos Aires, Argentina | Legarra, Andres. INRA. Génétique. Physiologie et Systèmes d’Elevage. Castanet-Tolosan, France. Université de Toulouse. INP. ENSAT. Génétique. Physiologie et Systèmes d’Elevage. Castanet-Tolosan, France | Vitezica, Zulma Gladis. INRA. Génétique. Physiologie et Systèmes d’Elevage. Castanet-Tolosan, France. Université de Toulouse. INP. ENSAT. Génétique. Physiologie et Systèmes d’Elevage. Castanet-Tolosan, France | Tsuruta, Shogo. University of Georgia. Animal and Dairy Science. Athens, Georgia | Aguilar, Ignacio. Instituto Nacional de Investigación Agropecuaria. Canelones, Uruguay | Misztal, Ignacy. University of Georgia. Animal and Dairy Science. Athens, Georgia | Cantet, Rodolfo Juan Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Buenos Aires, Argentina. CONICET. Buenos Aires, Argentina.
ISSN: 1943-2631.Tipo de material: Artículos y capítulos. Recurso electrónico.Tema(s): GENE CONTENT | QUALITY CONTROL | SNP | GENOMIC SELECTION | REML | SHARED DATA RESOURCE | GENPRED | Recursos en línea: Haga clic para acceso en línea | LINK AL EDITOR En: Genetics vol.199, no.3 (2015), p.675–681, grafs.Resumen: Quality control filtering of single-nucleotide polymorphisms (SNPs) is a key step when analyzing genomic data. Here we present a practical method to identify low-quality SNPs, meaning markers whose genotypes are wrongly assigned for a large proportion of individuals, by estimating the heritability of gene content at each marker, where gene content is the number of copies of a particular reference allele in a genotype of an animal (0, 1, or 2). If there is no mutation at the marker, gene content has an additive heritability of 1 by construction. The method uses restricted maximum likelihood (REML) to estimate heritability of gene content at each SNP and also builds a likelihood-ratio test statistic to test for zero error variance in genotyping. As a by-product, estimates of the allele frequencies of markers at the base population are obtained. Using simulated data with 10% permutation error (4% actual error) in genotyping, the method had a specificity of 0.96 (4% of correct markers are rejected) and a sensitivity of 0.99 (1% of wrong markers are accepted) if markers with heritability lower than 0.975 are discarded. Checking of Mendelian errors resulted in a lower sensitivity (0.84) for the same simulation. The proposed method is further illustrated with a real data set with genotypes from 3534 animals genotyped for 50,433 markers from the Illumina PorcineSNP60 chip and a pedigree of 6473 individuals; those markers underwent very little quality control. A total of 4099 markers with P-values lower than 0.01 were discarded based on our method, with associated estimates of heritability as low as 0.12. Contrary to other techniques, our method uses all information in the population simultaneously, can be used in any population with markers and pedigree recordings, and is simple to implement using standard software for REML estimation. Scripts for its use are provided.
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Quality control filtering of single-nucleotide polymorphisms (SNPs) is a key step when analyzing genomic data. Here we present a practical method to identify low-quality SNPs, meaning markers whose genotypes are wrongly assigned for a large proportion of individuals, by estimating the heritability of gene content at each marker, where gene content is the number of copies of a particular reference allele in a genotype of an animal (0, 1, or 2). If there is no mutation at the marker, gene content has an additive heritability of 1 by construction. The method uses restricted maximum likelihood (REML) to estimate heritability of gene content at each SNP and also builds a likelihood-ratio test statistic to test for zero error variance in genotyping. As a by-product, estimates of the allele frequencies of markers at the base population are obtained. Using simulated data with 10% permutation error (4% actual error) in genotyping, the method had a specificity of 0.96 (4% of correct markers are rejected) and a sensitivity of 0.99 (1% of wrong markers are accepted) if markers with heritability lower than 0.975 are discarded. Checking of Mendelian errors resulted in a lower sensitivity (0.84) for the same simulation. The proposed method is further illustrated with a real data set with genotypes from 3534 animals genotyped for 50,433 markers from the Illumina PorcineSNP60 chip and a pedigree of 6473 individuals; those markers underwent very little quality control. A total of 4099 markers with P-values lower than 0.01 were discarded based on our method, with associated estimates of heritability as low as 0.12. Contrary to other techniques, our method uses all information in the population simultaneously, can be used in any population with markers and pedigree recordings, and is simple to implement using standard software for REML estimation. Scripts for its use are provided.

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