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Natural variation in five candidate genes of the steroidal glycoalkaloid (SGA) metabolic pathway and whole-genome single nucleotide polymorphism (SNP) genotyping were studied in six wild [Solanum chacoense (chc 80-1), S. commersonii, S. demissum, S. sparsipilum, S. spegazzinii, S. stoloniferum] and cultivated S. tuberosum Group Phureja (phu DH) potato species with contrasting levels of SGAs. Amplicons were sequenced for five candidate genes: 3-hydroxy-3-methylglutaryl coenzyme A reductase 1 and 2 (HMG1, HMG2) and 2.3-squalene epoxidase (SQE) of primary metabolism, and solanidine galactosyltransferase (SGT1), and glucosyltransferase (SGT2) of secondary metabolism. SNPs (n = 337) producing 354 variations were detected within 3.7 kb of sequenced DNA. More polymorphisms were found in introns than exons and in genes of secondary compared to primary metabolism. Although no significant deviation from neutrality was found, dN/dS ratios < 1 and negative values of Tajima's D test suggested purifying selection and genetic hitchhiking in the gene fragments. In addition, patterns of dN/dS ratios across the SGA pathway suggested constraint by natural selection. Comparison of nucleotide diversity estimates and dN/dS ratios showed stronger selective constraints for genes of primary rather than secondary metabolism. SNPs (n = 24) with an exclusive genotype for either phu DH (low SGA) or chc 80-1 (high SGA) were identified for HMG2, SQE, SGT1 and SGT2. The SolCAP 8303 Illumina Potato SNP chip genotyping revealed eight informative SNPs on six pseudochromosomes, with homozygous and heterozygous genotypes that discriminated high, intermediate and low levels of SGA accumulation. These results can be used to evaluate SGA accumulation in segregating or association mapping populations.

Climate is the primary driver of the distribution of tree species worldwide, and the potential for adaptive evolution will be an important factor determining the response of forests to anthropogenic climate change. Although association mapping has the potential to improve our understanding of the genomic underpinnings of climatically relevant traits, the utility of adaptive polymorphisms uncovered by such studies would be greatly enhanced by the development of integrated models that account for the phenotypic effects of multiple single-nucleotide polymorphisms (SNPs) and their interactions simultaneously. We previously reported the results of association mapping in the widespread conifer Sitka spruce (Picea sitchensis). In the current study we used the recursive partitioning algorithm 'Random Forest' to identify optimized combinations of SNPs to predict adaptive phenotypes. After adjusting for population structure, we were able to explain 37% and 30% of the phenotypic variation, respectively, in two locally adaptive traits--autumn budset timing and cold hardiness. For each trait, the leading five SNPs captured much of the phenotypic variation. To determine the role of epistasis in shaping these phenotypes, we also used a novel approach to quantify the strength and direction of pairwise interactions between SNPs and found such interactions to be common. Our results demonstrate the power of Random Forest to identify subsets of markers that are most important to climatic adaptation, and suggest that interactions among these loci may be widespread.