Evaluation of genotyping concordance for commercial bovine SNP arrays using quality‐assurance samples

Abstract

SNP arrays are widely used in genetic research and agricultural genomics applications, and the quality of SNP genotyping data is of paramount importance. In the present study, SNP genotyping concordance and discordance were evaluated for commercial bovine SNP arrays based on two types of quality assurance (QA) samples provided by Neogen GeneSeek. The genotyping discordance rates (GDRs) between chips were on average between 0.06% and 0.37% based on the QA type I data and between 0.05% and 0.15% based on the QA type II data. The average genotyping error rate (GER) pertaining to single SNP chips, based on the QA type II data, varied between 0.02% and 0.08% per SNP and between 0.01% and 0.06% per sample. These results indicate that genotyping concordance rate was high (i.e. from 99.63% to 99.99%). Nevertheless, mitochondrial and Y chromosome SNPs had considerably elevated GDRs and GERs compared to the SNPs on the 29 autosomes and X chromosome. The majority of genotyping errors resulted from single allotyping errors, which also included the opposite instances for allele 'dropout' (i.e. from AB to AA or BB). Simultaneous allotyping errors on both alleles (e.g. mistaking AA for BB or vice versa) were relatively rare. Finally, a list of SNPs with a GER greater than 1% is provided. Interpretation of association effects of these SNPs, for example in genome-wide association studies, needs to be taken with caution. The genotyping concordance information needs to be considered in the optimal design of future bovine SNP arrays.

Optimized Genetic Testing for Polledness in Multiple Breeds of Cattle

“…Subsequent investigation of the remaining unpredicted (No Result) 9 samples found that 8 of them were due to genotyping failure for rs383143898 (P C ) and one failed for multiple markers associated with P F (Table S1). Both genotyping error rates were within the expected , 0.01% range (Wu et al 2019) and can be resolved by resampling. It is possible that higher prediction failure rates by MSAT and CPT in some breeds (e.g., Brahman and Wagyu) might suggest that they carry another mutation (e.g., Mongolian) given the spatial and temporal closeness with Turano cattle.…”

Study on the concordance between different SNP‐genotyping platforms in sheep

“…Even though the QC filtering steps carried out over the SNP-chip data do not directly consider the individual genotype quality, we observed that a significant number of erroneous genotypes were removed. Previous studies have recommended SNP QC filtering before applying subsequent association analyses because it reduces the rates of genotyping errors and false-positive results (Zhao et al 2018;Wu et al 2019). As shown in Fig.…”

“…The second group (CG2) refers to the genotypes generated with the Affymetrix and Illumina arrays that were concordant themselves but different from WGR variants. Following Wu et al (2019), most nonconcordant genotypes in this group may be due to a single allotyping error when comparing the SNP-chip genotypes versus the WGR genotypes (i.e., from heterozygous to homozygous). This explanation agrees with our observations as, in our case, multiple allotyping errors in the CG2 group of genotypes were also relatively rare (i.e., between opposing homozygotes).…”

“…2016, 2021; Wu et␣al . 2019) that may impact the results of the subsequent analyses. For example, genotyping errors have been found to decrease the power to detect genuine associations between phenotype and genotype data (Gordon et␣al .…”

“…In this context, we should consider that different studies have reported variable amounts of genotyping errors in SNP-chip datasets (Berry et al 2016(Berry et al , 2021Wu et al 2019) that may impact the results of the subsequent analyses. For example, genotyping errors have been found to decrease the power to detect genuine associations between phenotype and genotype data (Gordon et al 2002;Sun et al 2004).…”