Exclusion and Genomic Relatedness Methods for Assignment of Parentage Using Genotyping-by-Sequencing Data

Abstract

Genotypes are often used to assign parentage in agricultural and ecological settings. Sequencing can be used to obtain genotypes but does not provide unambiguous genotype calls, especially when sequencing depth is low in order to reduce costs. In that case, standard parentage analysis methods no longer apply. A strategy for using low-depth sequencing data for parentage assignment is developed here. It entails the use of relatedness estimates along with a metric termed excess mismatch rate which, for parent-offspring pairs or trios, is the difference between the observed mismatch rate and the rate expected under a model of inheritance and allele reads without error. When more than one putative parent has similar statistics, bootstrapping can provide a measure of the relatedness similarity. Putative parent-offspring trios can be further checked for consistency by comparing the offspring’s estimated inbreeding to half the parent relatedness. Suitable thresholds are required for each metric. These methods were applied to a deer breeding operation consisting of two herds of different breeds. Relatedness estimates were more in line with expectation when the herds were analyzed separately than when combined, although this did not alter which parents were the best matches with each offspring. Parentage results were largely consistent with those based on a microsatellite parentage panel with three discordant parent assignments out of 1561. Two models are investigated to allow the parentage metrics to be calculated with non-random selection of alleles. The tools and strategies given here allow parentage to be assigned from low-depth sequencing data.

Application of Low Coverage Genotyping by Sequencing in Selectively Bred Arctic Charr (Salvelinus alpinus)

“…Low coverage GBS offer an attractive solution that offers for both high SNP density and cost-effective genotyping (Dodds et al 2015). GBS using low sequencing coverage has been successfully used for estimating genome wide linkage disequilibrium (Bilton et al 2018a), constructing genetic maps (Bilton et al 2018b;Chagné et al 2018), parentage assignment (Dodds et al 2019), predicting gender in animals (Bilton et al 2019) and implementing genomic selection (Faville et al 2018).…”

“…SNPs passing QC filters were used for parentage assignment purposes. Assignment to most probable sire and dam was performed taking into account the sequence depth of the corresponding individuals as described in Dodds et al (2019). Specifically, the excess mismatch rate (EMM) metric was considered for parentage verification using a maximum threshold of 0.025.…”

Genotyping Strategies Using ddRAD Sequencing in Farmed Arctic Charr (Salvelinus alpinus)

“…Available, reduced representation methods include strategies that yield for the same cost either deep coverage but low density of genetic markers, or high genotyping density with shallow coverage—and thus, a greater proportion of missing data. Encouragingly, as suggested by both simulated data and empirical studies, low coverage genotyping by sequencing appears to be an attractive option for selective breeding practices [ 12 , 42 ].…”

“…A number of common enzymatic cutters used for genotyping by sequencing (GBS) [ 7 ] have been successfully applied in the form of low read coverage per interrogated site for deciphering genomic relationships at a high resolution [ 8 ]. Low coverage genotyping by sequencing has been successfully applied for estimating genome-wide linkage disequilibrium [ 9 ], genetic map construction [ 10 ], gender prediction [ 11 ], parentage assignment [ 12 ] and genomic selection purposes [ 13 , 14 ]. Nevertheless, the aforementioned strategy usually results in an increase of missing genotypic data and in a reduction of confidently identifying heterozygotes due to the low number of supporting reads as opposed to high coverage genotyping approaches.…”

Transcriptome Landscape of Intracellular Brucella ovis Surviving in RAW264.7 Macrophage Immune System

“…The FASTQ file record the base and the mass fraction of the read section [18][19][20]. The error rates of sequencing reads increase with the progress in sequencing due to the consumption of chemical reagents, which is a common feature of the Illumina high-throughput sequencing platform [21]. Quality control of the original sequencing data is performed as follows: removing adapter sequences from reads, removing bases containing non-AGCT at the 5′ end before shearing, trimming the ends of low-quality reads (sequencing quality value less than Q20), removing reads with N content up to 10%, and discarding small segments (length < 25 bp) after removing adapter sequences and mass pruning [22].…”