How imputation can mitigate SNP ascertainment Bias

Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study

As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (A matrix) and the genomic relationship matrix (G matrix), respectively, have been proposed. We assessed the performance of MCA and MCG methods using 575 Japanese Black cows. Pedigree data were provided to trace back up to five generations to construct the A matrix with changing the pedigree depth from 1 to 5 (five MCA methods). Genotype information on 36,426 single-nucleotide polymorphisms was used to calculate the G matrix based on VanRaden's methods 1 and 2 (two MCG methods). The MCG always selected one cow per iteration, while MCA sometimes selected multiple cows. The number of commonly selected cows between the MCA and MCG methods was generally lower than that between different MCA methods or between different MCG methods. For the studied population, MCG appeared to be more reasonable than MCA in selecting cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study.

Assessment of linkage disequilibrium patterns between structural variants and single nucleotide polymorphisms in three commercial chicken populations

BACKGROUND

Structural variants (SV) are causative for some prominent phenotypic traits of livestock as different comb types in chickens or color patterns in pigs. Their effects on production traits are also increasingly studied. Nevertheless, accurately calling SV remains challenging. It is therefore of interest, whether close-by single nucleotide polymorphisms (SNPs) are in strong linkage disequilibrium (LD) with SVs and can serve as markers. Literature comes to different conclusions on whether SVs are in LD to SNPs on the same level as SNPs to other SNPs. The present study aimed to generate a precise SV callset from whole-genome short-read sequencing (WGS) data for three commercial chicken populations and to evaluate LD patterns between the called SVs and surrounding SNPs. It is thereby the first study that assessed LD between SVs and SNPs in chickens.

RESULTS

The final callset consisted of 12,294,329 bivariate SNPs, 4,301 deletions (DEL), 224 duplications (DUP), 218 inversions (INV) and 117 translocation breakpoints (BND). While average LD between DELs and SNPs was at the same level as between SNPs and SNPs, LD between other SVs and SNPs was strongly reduced (DUP: 40%, INV: 27%, BND: 19% of between-SNP LD). A main factor for the reduced LD was the presence of local minor allele frequency differences, which accounted for 50% of the difference between SNP - SNP and DUP - SNP LD. This was potentially accompanied by lower genotyping accuracies for DUP, INV and BND compared with SNPs and DELs. An evaluation of the presence of tag SNPs (SNP in highest LD to the variant of interest) further revealed DELs to be slightly less tagged by WGS SNPs than WGS SNPs by other SNPs. This difference, however, was no longer present when reducing the pool of potential tag SNPs to SNPs located on four different chicken genotyping arrays.

CONCLUSIONS

The results implied that genomic variance due to DELs in the chicken populations studied can be captured by different SNP marker sets as good as variance from WGS SNPs, whereas separate SV calling might be advisable for DUP, INV, and BND effects.

SoySNP618K array: A high-resolution single nucleotide polymorphism platform as a valuable genomic resource for soybean genetics and breeding

Innovations in genomics have enabled the development of low-cost, high-resolution, single nucleotide polymorphism (SNP) genotyping arrays that accelerate breeding progress and support basic research in crop science. Here, we developed and validated the SoySNP618K array (618,888 SNPs) for the important crop soybean. The SNPs were selected from whole-genome resequencing data containing 2,214 diverse soybean accessions; 29.34% of the SNPs mapped to genic regions representing 86.85% of the 56,044 annotated high-confidence genes. Identity-by-state analyses of 318 soybeans revealed 17 redundant accessions, highlighting the potential of the SoySNP618K array in supporting gene bank management. The patterns of population stratification and genomic regions enriched through domestication were highly consistent with previous findings based on resequencing data, suggesting that the ascertainment bias in the SoySNP618K array was largely compensated for. Genome-wide association mapping in combination with reported quantitative trait loci enabled fine-mapping of genes known to influence flowering time, E2 and GmPRR3b, and of a new candidate gene, GmVIP5. Moreover, genomic prediction of flowering and maturity time in 502 recombinant inbred lines was highly accurate (>0.65). Thus, the SoySNP618K array is a valuable genomic tool that can be used to address many questions in applied breeding, germplasm management, and basic crop research.

Selection signatures for heat tolerance in Brazilian horse breeds

Since domestication, horse breeds have adapted to their environments and differentiated from one another. This paper uses two methods to detect selection signatures in 23 horse breeds, eight of which are Brazilian (610 animals), both cold-blooded and warm-blooded, from temperate and tropical regions. These animals were genotyped using the GGP Equine BeadChip and we analysed the data by Principal Component Analysis (PCA). The samples were separated into groups based on their geographical area of origin and PCA results studied. The genomic regions under selection were detected by hapFLK and PCAdapt methodologies, identifying six regions under selection with at least one Brazilian horse breed. These regions contain genes associated with heat tolerance, skin colour, body size, energy production/metabolism, genes involved in protein degradation/turnover/DNA repair, genes reducing the impact of oxidative stress/cellular repair, and transcriptional regulation. This work confirmed LCORL and NCAPG gene regions in previous studies associated with body size on Equine Chromosome Autosome 3 (ECA3). On the same ECA3, a region implicating genes linked to coat colour was identified, also previously related to heat stress. Regions with genes coding heat shock proteins were found on ECA1 and 2, and many candidate genes for oxidation-reduction which are a natural response to heat stress. However, a larger sample size and whole-genome SNPs are needed to understand better and identify new candidate regions as well as their functional relation with heat tolerance.