Obtaining genome-wide genotype data from a set of individuals is the first step in many genomic studies, including genome-wide association and genomic selection. All genotyping methods suffer from some level of missing data, and genotype imputation can be used to fill in the missing data and improve the power of downstream analyses. Model organisms like human and cattle benefit from high-quality reference genomes and panels of reference genotypes that aid in imputation accuracy. In nonmodel organisms, however, genetic and physical maps often are either of poor quality or are completely absent, and there are no panels of reference genotypes available. There is therefore a need for imputation methods designed specifically for nonmodel organisms in which genomic resources are poorly developed and marker order is unreliable or unknown. Here we introduce LinkImpute, a software package based on a k-nearest neighbor genotype imputation method, LD-kNNi, which is designed for unordered markers. No physical or genetic maps are required, and it is designed to work on unphased genotype data from heterozygous species. It exploits the fact that markers useful for imputation often are not physically close to the missing genotype but rather distributed throughout the genome. Using genotyping-by-sequencing data from diverse and heterozygous accessions of apples, grapes, and maize, we compare LD-kNNi with several genotype imputation methods and show that LD-kNNi is fast, comparable in accuracy to the best-existing methods, and exhibits the least bias in allele frequency estimates.
During the last glacial–interglacial cycle, Arctic biotas experienced substantial climatic changes, yet the nature, extent and rate of their responses are not fully understood1–8. Here we report a large-scale environmental DNA metagenomic study of ancient plant and mammal communities, analysing 535 permafrost and lake sediment samples from across the Arctic spanning the past 50,000 years. Furthermore, we present 1,541 contemporary plant genome assemblies that were generated as reference sequences. Our study provides several insights into the long-term dynamics of the Arctic biota at the circumpolar and regional scales. Our key findings include: (1) a relatively homogeneous steppe–tundra flora dominated the Arctic during the Last Glacial Maximum, followed by regional divergence of vegetation during the Holocene epoch; (2) certain grazing animals consistently co-occurred in space and time; (3) humans appear to have been a minor factor in driving animal distributions; (4) higher effective precipitation, as well as an increase in the proportion of wetland plants, show negative effects on animal diversity; (5) the persistence of the steppe–tundra vegetation in northern Siberia enabled the late survival of several now-extinct megafauna species, including the woolly mammoth until 3.9 ± 0.2 thousand years ago (ka) and the woolly rhinoceros until 9.8 ± 0.2 ka; and (6) phylogenetic analysis of mammoth environmental DNA reveals a previously unsampled mitochondrial lineage. Our findings highlight the power of ancient environmental metagenomics analyses to advance understanding of population histories and long-term ecological dynamics.
Apple (Malus X. domestica Borkh.) is one of the world's most valuable fruit crops. Its large size and long juvenile phase make it a particularly promising candidate for marker-assisted selection (MAS). However, advances in MAS in apple have been limited by a lack of phenotype and genotype data from sufficiently large samples. To establish genotype-phenotype relationships and advance MAS in apple, we extracted over 24,000 phenotype scores from the USDA-Germplasm Resources Information Network (GRIN) database and linked them with over 8000 single nucleotide polymorphisms (SNPs) from 689 apple accessions from the USDA apple germplasm collection clonally preserved in Geneva, NY. We find significant genetic differentiation between Old World and New World cultivars and demonstrate that the genetic structure of the domesticated apple also reflects the time required for ripening. A genome-wide association study (GWAS) of 36 phenotypes confirms the association between fruit color and the MYB1 locus, and we also report a novel association between the transcription factor, NAC18.1, and harvest date and fruit firmness. We demonstrate that harvest time and fruit size can be predicted with relatively high accuracies (r > 0.46) using genomic prediction. Rapid decay of linkage disequilibrium (LD) in apples means millions of SNPs may be required for well-powered GWAS. However, rapid LD decay also promises to enable extremely high resolution mapping of causal variants, which holds great potential for advancing MAS.
Perennial crops represent important fresh and processed food sources worldwide, but advancements in breeding perennials are often impeded due to their very nature. The perennial crops we rely on most for food take several years to reach production maturity and require large spaces to grow, which make breeding new cultivars costly compared with most annual crops. Because breeding perennials is inefficient and expensive, they are often grown in monocultures consisting of small numbers of elite cultivars that are vegetatively propagated for decades or even centuries. This practice puts many perennial crops at risk for calamity since they remain stationary in the face of evolving pest and disease pressures. Although there is tremendous genetic diversity available to them, perennial crop breeders often struggle to generate commercially successful cultivars in a timely and cost-effective manner because of the high costs of breeding. Moreover, consumers often expect the same cultivars to be available indefinitely, and there is often little or no incentive for growers and retailers to take the risk of adopting new cultivars. While genomics studies linking DNA variants to commercially important traits have been performed in diverse perennial crops, the translation of these studies into accelerated breeding of improved cultivars has been limited. Here we explain the "perennial problem" in detail and demonstrate how modern genomics tools can significantly improve the cost effectiveness of breeding perennial crops and thereby prevent crucial food sources from succumbing to the perils of perpetual propagation.
Supplementary data are available at Bioinformatics online.
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