Development of highly reliable in silico SNP resource and genotyping assay from exome capture and sequencing: an example from black spruce (Picea mariana)

Abstract

Picea mariana is a widely distributed boreal conifer across Canada and the subject of advanced breeding programmes for which population genomics and genomic selection approaches are being developed. Targeted sequencing was achieved after capturing P. mariana exome with probes designed from the sequenced transcriptome of Picea glauca, a distant relative. A high capture efficiency of 75.9% was reached although spruce has a complex and large genome including gene sequences interspersed by some long introns. The results confirmed the relevance of using probes from congeneric species to perform successfully interspecific exome capture in the genus Picea. A bioinformatics pipeline was developed including stringent criteria that helped detect a set of 97,075 highly reliable in silico SNPs. These SNPs were distributed across 14,909 genes. Part of an Infinium iSelect array was used to estimate the rate of true positives by validating 4267 of the predicted in silico SNPs by genotyping trees from P. mariana populations. The true positive rate was 96.2% for in silico SNPs, compared to a genotyping success rate of 96.7% for a set 1115 P. mariana control SNPs recycled from previous genotyping arrays. These results indicate the high success rate of the genotyping array and the relevance of the selection criteria used to delineate the new P. mariana in silico SNP resource. Furthermore, in silico SNPs were generally of medium to high frequency in natural populations, thus providing high informative value for future population genomics applications.

Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana)

“…), including draft genome sequences [43–46], genetic and QTL maps [11, 47, 48], gene catalog and expression chips [49], large SNP registries and high throughput genotyping chips [40, 50], but comparatively few genomic resources exist for black spruce. Before investigating GS in black spruce, exome capture and sequencing were used together with an in-house bioinformatic pipeline to produce a registry of 97,000 high-confidence SNPs pertaining to around 15,000 gene sequence clusters [39]. In addition, we compiled successfully genotyped gene SNPs from previous black spruce genomic studies [9, 41, 51–53].…”

“…In addition, we compiled successfully genotyped gene SNPs from previous black spruce genomic studies [9, 41, 51–53]. Altogether, this information was used to develop an Infinium iSelect SNP genotyping array (Illumina, San Diego, CA) containing 5,300 SNPs representing as many distinct black spruce gene contigs [39]. Based on control DNA replicates, the chip genotyping reproducibility rate obtained was 99.9%.…”

“…Given the very high synteny and collinearity between the white spruce and black spruce genomes [51], we used the more complete linkage mapping information of white spruce gene homologs to approximate the genomic positions of black spruce genes carrying the SNPs considered herein. Of the 4,993 black spruce gene contigs of the present study (with gene nomenclature according to the white spruce gene catalogue of Rigault et al [49] as described in Pavy et al [39]), 2,928 genes had homologs mapped on the most recent white spruce reference genetic map containing nearly 9,000 genes [48]. These homologs were well distributed over the 12 linkage groups with, on average, 244 (+/- 15) gene homologs represented per chromosome.…”

“…The genetic test consisted of full-sib families generated using a partial diallel mating design. Trees were genotyped for 4993 SNP markers representative of as many distinct gene loci, and originating from a black spruce high-confidence SNP catalogue previously assembled and validated [39]. …”

SNP Discovery and Genetic Variation of Candidate Genes Relevant to Heat Tolerance and Agronomic Traits in Natural Populations of Sand Rice (Agriophyllum squarrosum)

“…With the advancement of sequencing technologies, genome scale analyses of sequence polymorphism has become feasible and cost effective for non-model plants (Silva-Junior et al, 2011; Zou et al, 2014; Pavy et al, 2016). Analysis of the genomes of populations from different environments could identify genes and alleles favored in specific local climates (Henry, 2014).…”