Quality control of genotypes using heritability estimates of gene content at the marker
Quality control filtering of single nucleotide polymorphisms (SNP) 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 o...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/44365 |
| Acceso en línea: | http://hdl.handle.net/11336/44365 |
| Access Level: | acceso abierto |
| Palabra clave: | Gene Content Quality Control Snp Genomic Selection Reml Shared Data Resource Genpred https://purl.org/becyt/ford/4.2 https://purl.org/becyt/ford/4 |
| Sumario: | Quality control filtering of single nucleotide polymorphisms (SNP) 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 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 byproduct, 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 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 dataset with genotypes from 3,534 animals genotyped for 50,433 markers from the Illumina PorcineSNP60 chip, and a pedigree of 6,473 individuals; those markers did undergo very little quality control. A number of 4,099 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 simultaneously all information in the population, 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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