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

Abstract

Background: The 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.

Methods: First, 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.

Results: There 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.

Conclusions: Performance 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.

Fig. 1

The overlap of single nucleotide polymorphisms detected by different variant callers

Fig. 2

Comparison of the genotype concordance, non-reference sensitivity, non-reference genotype concordance and precision of GATK, freebayes and SAMtools over various minor allele frequency bins. SNPs were binned into 100 groups according to their array-derived MAF. The mean of each metric was calculated within each minor allele frequency bin. The statistics of different genotype concordance metrics were measured according to Linderman et.al [20]. The orange squares represent variant caller GATK. The green circles stand for variant caller freebayes. The blue triangles stand for variant caller SAMtools

Fig. 3

Imputation accuracy assessed by leave-one-out cross-validation. Genotype correlation (top panel) and genotype concordance (bottom panel) between the sequenced and imputed genotypes for 24 sequenced individuals with different imputing programs

Fig. 4

The percentage of genotype conflicts in father-progeny pairs. The conflicts were calculated within 952,826 (365,802, 37,556) imputed SNPs on chromosome 3 (6, 28) for 134 pairs of sequenced fathers and imputed progeny. Imputation was performed using Minimac (left), FImpute (middle) or IMPUTE2 (right)

Fig. 5

Mean of imputation accuracy of different software against minor allele frequency among 5 replications. SNP were binned by their sequence-derived MAF

Fig. 6

Imputation accuracy with 95 % CI of masked SNPs in different generations obtained with different imputation software package. The imputation accuracy is the correlation between the sequenced and imputed genotypes which were masked as dummy genotypes on 3 chromosomes (3, 6 and 28) with 5 replications. Imputation accuracy measured as the correlation between original imputed and original true genotype per individual is shown in (a), while imputation accuracy measured as the correlation between standardized imputed and standardized true genotype per individual is shown in (b)