Comparison among three variant callers and assessment of the accuracy of imputation from SNP array data to whole-genome sequence level in chicken

BMC Genomics

Research Article

Christian Reimer¹  Guiyan Ni¹  Henner Simianer¹  Malena Erbe²  Hubert Pausch³  Tim M. Strom⁴  Rudolf Preisinger⁵ 

[1] Animal Breeding and Genetics Group, Georg-August-Universität, Göttingen, Germany;Animal Breeding and Genetics Group, Georg-August-Universität, Göttingen, Germany;Institute for Animal Breeding, Bavarian State Research Centre for Agriculture, Grub, Germany;Chair of Animal Breeding, Technische Universität München, Freising, Germany;Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany;Lohmann Tierzucht GmbH, Cuxhaven, Germany;

关键词: Whole-genome sequencing data;    Variant calling;    Imputation accuracy;    Layer chicken;   

DOI  :  10.1186/s12864-015-2059-2

 received in 2015-06-09, accepted in 2015-10-09,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundThe technical progress in the last decade has made it possible to sequence millions of DNA reads in a relatively short time frame. Several variant callers based on different algorithms have emerged and have made it possible to extract single nucleotide polymorphisms (SNPs) out of the whole-genome sequence. Often, only a few individuals of a population are sequenced completely and imputation is used to obtain genotypes for all sequence-based SNP loci for other individuals, which have been genotyped for a subset of SNPs using a genotyping array.MethodsFirst, we compared the sets of variants detected with different variant callers, namely GATK, freebayes and SAMtools, and checked the quality of genotypes of the called variants in a set of 50 fully sequenced white and brown layers. Second, we assessed the imputation accuracy (measured as the correlation between imputed and true genotype per SNP and per individual, and genotype conflict between father-progeny pairs) when imputing from high density SNP array data to whole-genome sequence using data from around 1000 individuals from six different generations. Three different imputation programs (Minimac, FImpute and IMPUTE2) were checked in different validation scenarios.ResultsThere were 1,741,573 SNPs detected by all three callers on the studied chromosomes 3, 6, and 28, which was 71.6 % (81.6 %, 88.0 %) of SNPs detected by GATK (SAMtools, freebayes) in total. Genotype concordance (GC) defined as the proportion of individuals whose array-derived genotypes are the same as the sequence-derived genotypes over all non-missing SNPs on the array were 0.98 (GATK), 0.97 (freebayes) and 0.98 (SAMtools). Furthermore, the percentage of variants that had high values (>0.9) for another three measures (non-reference sensitivity, non-reference genotype concordance and precision) were 90 (88, 75) for GATK (SAMtools, freebayes). With all imputation programs, correlation between original and imputed genotypes was >0.95 on average with randomly masked 1000 SNPs from the SNP array and >0.85 for a leave-one-out cross-validation within sequenced individuals.ConclusionsPerformance of all variant callers studied was very good in general, particularly for GATK and SAMtools. FImpute performed slightly worse than Minimac and IMPUTE2 in terms of genotype correlation, especially for SNPs with low minor allele frequency, while it had lowest numbers in Mendelian conflicts in available father-progeny pairs. Correlations of real and imputed genotypes remained constantly high even if individuals to be imputed were several generations away from the sequenced individuals.

【 授权许可】

CC BY   
© Ni et al. 2015