Evaluation of a Chicken 600K SNP genotyping array in non-model species of grouse

From chip to SNP: Rapid development and evaluation of a targeted capture genotyping-by-sequencing approach to support research and management of a plaguing rodent

The management of invasive species has been greatly enhanced by population genetic analyses of multilocus single-nucleotide polymorphism (SNP) datasets that provide critical information regarding pest population structure, invasion pathways, and reproductive biology. For many applications there is a need for protocols that offer rapid, robust and efficient genotyping on the order of hundreds to thousands of SNPs, that can be tailored to specific study populations and that are scalable for long-term monitoring schemes. Despite its status as a model laboratory species, there are few existing resources for studying wild populations of house mice (Mus musculus spp.) that strike this balance between data density and laboratory efficiency. Here we evaluate the utility of a custom targeted capture genotyping-by-sequencing approach to support research on plaguing house mouse populations in Australia. This approach utilizes 3,651 hybridization capture probes targeting genome-wide SNPs identified from a sample of mice collected in grain-producing regions of southeastern Australia genotyped using a commercially available microarray platform. To assess performance of the custom panel, we genotyped wild caught mice (N = 320) from two adjoining farms and demonstrate the ability to correctly assign individuals to source populations with high confidence (mean >95%), as well as robust kinship inference within sites. We discuss these results in the context of proposed applications for future genetic monitoring of house mice in Australia.

Application potential of chicken DNA chip in domestic pigeon species - Preliminary results

Introducing the SNP technology to pigeon breeding will enhance the competitiveness of a sector that produces one of the healthiest and best quality meats. The present study aimed to test the applicability of the Illumina Chicken_50K_CobbCons array on 24 domestic pigeon individuals from the Mirthys hybrids and Racing pigeon breeds. A total of 53,313 SNPs were genotyped. Principal component analysis shows a significant overlap between the two groups. The chip performed poorly in this data set, with a call rate per sample of 0.474 (49%). The low call rate was likely due to an increase in the evolutionary distance. A total of 356 SNPs were retained after a relatively strict quality control. We have demonstrated that it is technically feasible to use a chicken microarray chip on pigeon samples. Presumably, with a larger sample size and by assigning phenotypic data, efficiency would be improved, allowing more thorough analyses, such as genome-wide association studies.

New insights into genetics underlying of plumage color

Plumage color can be considered as a social signal in chickens and a breeding identification tool among breeders. The relationship between plumage color and trait groups of immunity, growth and fertility is still a controversial issue. This research aimed to determine the genome-wide additive and epistatic variants affecting plumage color variation in chickens using the chicken Illumina 60k high-density SNP array. Two scenarios of genome-wide additive association studies using all SNPs and independent SNPs were carried out. To perform epistatic association analysis, the LD pruning approach was used to reduce the complexity of the analysis. We detected seven novel significant loci using all of the SNPs in the model and 14 SNPs using the LD pruning approach associated with plumage color. Moreover, 89 significantly associated SNP-SNP interactions (P-value <10^-6 ) distributed in 25 chromosomes were identified, indicating that all of the signals together putatively influence the quantitative variation of plumage color. By annotating genes relevant to top SNPs, we have distinguished 18 potential candidate genes comprising HNF4beta, CKMT1B, TBC1D22A, RPL8, CACNA2D1, FZD4, SGMS1, IRF8, OPTN, LOC420362, TRABD, OvoDA1, DAD1, USP6, RBM12B, MIR1772, MIR1709 and MIR6696 and also 89 putative gene-gene combinations responsible for plumage color variation in chickens. Furthermore, several KEGG pathways including metabolic pathway, cytokine-cytokine receptor interaction, focal adhesion, melanogenesis, glycosaminoglycan biosynthesis-keratan sulfate and sphingolipid metabolism were enriched in the gene-set analysis. The results indicated that plumage color is a highly polygenic trait which, in turn, can be affected by multiple coding genes, regulatory genes and gene-gene epistasis interactions. In addition to genes with additive effects, epistatic genes with tiny individual effect sizes but significant effects in a pair have the potential to control plumage coloration in chickens.

Exploiting genomic synteny in Felidae: cross-species genome alignments and SNV discovery can aid conservation management

Background

While recent advances in genomics has enabled vast improvements in the quantification of genome-wide diversity and the identification of adaptive and deleterious alleles in model species, wildlife and non-model species have largely not reaped the same benefits. This has been attributed to the resources and infrastructure required to develop essential genomic datasets such as reference genomes. In the absence of a high-quality reference genome, cross-species alignments can provide reliable, cost-effective methods for single nucleotide variant (SNV) discovery. Here, we demonstrated the utility of cross-species genome alignment methods in gaining insights into population structure and functional genomic features in cheetah (Acinonyx jubatas), snow leopard (Panthera uncia) and Sumatran tiger (Panthera tigris sumatrae), relative to the domestic cat (Felis catus).

Results

Alignment of big cats to the domestic cat reference assembly yielded nearly complete sequence coverage of the reference genome. From this, 38,839,061 variants in cheetah, 15,504,143 in snow leopard and 13,414,953 in Sumatran tiger were discovered and annotated. This method was able to delineate population structure but limited in its ability to adequately detect rare variants. Enrichment analysis of fixed and species-specific SNVs revealed insights into adaptive traits, evolutionary history and the pathogenesis of heritable diseases.

Conclusions

The high degree of synteny among felid genomes enabled the successful application of the domestic cat reference in high-quality SNV detection. The datasets presented here provide a useful resource for future studies into population dynamics, evolutionary history and genetic and disease management of big cats. This cross-species method of variant discovery provides genomic context for identifying annotated gene regions essential to understanding adaptive and deleterious variants that can improve conservation outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07899-2.

Pseudoreplication in genomics-scale datasets

In genomics-scale datasets, loci are closely packed within chromosomes and hence provide correlated information. Averaging across loci as if they were independent creates pseudoreplication, which reduces the effective degrees of freedom (df') compared to the nominal degrees of freedom, df. This issue has been known for some time, but consequences have not been systematically quantified across the entire genome. Here we measured pseudoreplication (quantified by the ratio df'/df) for a common metric of genetic differentiation (F_ST ) and a common measure of linkage disequilibrium between pairs of loci (r² ). Based on data simulated using models (SLiM and msprime) that allow efficient forward-in-time and coalescent simulations while precisely controlling population pedigrees, we estimated df' and df'/df by measuring the rate of decline in the variance of mean F_ST and mean r² as more loci were used. For both indices, df' increases with N_e and genome size, as expected. However, even for large N_e and large genomes, df' for mean r² plateaus after a few thousand loci, and a variance components analysis indicates that the limiting factor is uncertainty associated with sampling individuals rather than genes. Pseudoreplication is less extreme for F_ST , but df'/df ≤0.01 can occur in datasets using tens of thousands of loci. Commonly-used block-jackknife methods consistently overestimated var(F_ST ), producing very conservative confidence intervals. Predicting df' based on our modeling results as a function of N_e , L, S, and genome size provides a robust way to quantify precision associated with genomics-scale datasets.

Genomic data of different resolutions reveal consistent inbreeding estimates but contrasting homozygosity landscapes for the threatened Aotearoa New Zealand hihi

Inbreeding can lead to a loss of heterozygosity in a population and when combined with genetic drift may reduce the adaptive potential of a species. However, there is uncertainty about whether resequencing data can provide accurate and consistent inbreeding estimates. Here, we performed an in-depth inbreeding analysis for hihi (Notiomystis cincta), an endemic and nationally vulnerable passerine bird of Aotearoa New Zealand. We first focused on subsampling variants from a reference genome male, and found that low-density data sets tend to miss runs of homozygosity (ROH) in some places and overestimate ROH length in others, resulting in contrasting homozygosity landscapes. Low-coverage resequencing and 50 K SNP array densities can yield comparable inbreeding results to high-coverage resequencing approaches, but the results for all data sets are highly dependent on the software settings employed. Second, we extended our analysis to 10 hihi where low-coverage whole genome resequencing, RAD-seq and SNP array genotypes are available. We inferred ROH and individual inbreeding to evaluate the relative effects of sequencing depth versus SNP density on estimating inbreeding coefficients and found that high rates of missingness downwardly bias both the number and length of ROH. In summary, when using genomic data to evaluate inbreeding, studies must consider that ROH estimates are heavily dependent on analysis parameters, data set density and individual sequencing depth.