Pleiotropy drives repeatability in the genetic basis of adaptation

Studies of trait-mapping and local adaptation often identify signatures of genetically parallel evolution, where different species evolve similar phenotypes using the same genes. Such patterns appear incongruent with current estimations of quantitative trait architecture. With hundreds or thousands or genes contributing to a trait, why would selection make repeated use of the same genes? Here, we use individual-based simulations to explore a two-patch model with quantitative, pleiotropic traits to understand the parameters which may lead to repeated use of a particular locus during independent bouts of adaptation. We find that repeatability can be driven by increased phenotypic effect size, a reduction in trait dimensionality and a reduction in mutational correlations at a particular locus relative to other loci in the genome, and that these patterns are magnified by increased migration between demes. These results suggest that evolutionary convergence can arise from multiple characteristics of a locus, and provide a framework for the interpretation of quantitative signatures of convergence in empirical studies.

UAS for Forest Inventory Traits: A Review

Traits are notoriously challenging to measure at a desirably large spatial extent with traditional field methods, which limits the discoveries that forest ecologists can make with these data. There is a ripe opportunity for uncrewed aerial systems (UAS) to contribute to ecology through forest inventory trait mapping. UAS can help overcome the challenge of scale by collecting data at a larger spatial extent with comparable resolution. With the proliferation of large-scale spatially explicit analyses, using UAS for forest trait mapping is synergistic with the direction that the field of forest ecology is headed, and thus an essential method for forest ecology toolkits. Here we provide evidence that forest traits are increasingly used as the metrics of focus in forest ecology, review what forest inventory traits and attributes can be derived from UAS-based data, and dive into a case example of how researchers derive a particular trait, carbon stock, from UAS-based data. Our results highlight the underutilization and infancy of UAS in forest ecology. From our review of the carbon stock literature, we found a different method of calculating carbon stock from UAS data in every paper, each with their own hurdles and caveats in estimating plant-based carbon stock. UAS can push forest ecology and the concomitant field of spatial ecology into a future with better temporal and spatial resolution of data collected on an evermore affordable budget.

Quantitative trait locus mapping combined with variant and transcriptome analyses identifies a cluster of gene candidates underlying the variation in leaf wax between upland and lowland switchgrass ecotypes

Key message Mapping combined with expression and variant analyses in switchgrass, a crop with complex genetics, identified a cluster of candidate genes for leaf wax in a fast-evolving region of chromosome 7K. Abstract Switchgrass (Panicum virgatum L.) is a promising warm-season candidate energy crop. It occurs in two ecotypes, upland and lowland, which vary in a number of phenotypic traits, including leaf glaucousness. To initiate trait mapping, two F2 mapping populations were developed by crossing two different F1 sibs derived from a cross between the tetraploid lowland genotype AP13 and the tetraploid upland genotype VS16, and high-density linkage maps were generated. Quantitative trait locus (QTL) analyses of visually scored leaf glaucousness and of hydrophobicity of the abaxial leaf surface measured using a drop shape analyzer identified highly significant colocalizing QTL on chromosome 7K (Chr07K). Using a multipronged approach, we identified a cluster of genes including Pavir.7KG077009, which encodes a Type III polyketide synthase-like protein, and Pavir.7KG013754 and Pavir.7KG030500, two highly similar genes that encode putative acyl-acyl carrier protein (ACP) thioesterases, as strong candidates underlying the QTL. The lack of homoeologs for any of the three genes on Chr07N, the relatively low level of identity with other switchgrass KCS proteins and thioesterases, as well as the organization of the surrounding region suggest that Pavir.7KG077009 and Pavir.7KG013754/Pavir.7KG030500 were duplicated into a fast-evolving chromosome region, which led to their neofunctionalization. Furthermore, sequence analyses showed all three genes to be absent in the two upland compared to the two lowland accessions analyzed. This study provides an example of and practical guide for trait mapping and candidate gene identification in a complex genetic system by combining QTL mapping, transcriptomics and variant analysis.

Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics

Background Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. Results To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1x coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1x genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8x, 0.6x, and 0.4x coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4x coverage sequencing) to 99.2% (for 1x coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 0.90, which was comparable to the imputation r2 from 0.4x coverage sequencing, while the mean imputation r2 from 1x sequencing data was 0.96. Conclusions These results indicate that low-pass sequencing to a depth above 0.4x coverage attains higher power for association studies when compared to the PMRA and should be considered as a competitive alternative to genotyping arrays for trait mapping in pharmacogenetics.

Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics

Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1× coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1× genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8×, 0.6×, and 0.4× coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4× coverage sequencing) to 99.2% (for 1× coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 90%, which was comparable to the imputation r2 from 0.4× coverage sequencing, while the mean imputation r2 from 1× sequencing data was 96%. These results indicate that low-pass sequencing to a depth above 0.4× coverage attains higher power for trait mapping when compared to the PMRA.

Imputation of canine genotype array data using 365 whole-genome sequences improves power of genome-wide association studies

Genomic resources for the domestic dog have improved with the widespread adoption of a 173k SNP array platform and updated reference genome. SNP arrays of this density are sufficient for detecting genetic associations within breeds but are underpowered for finding associations across multiple breeds or in mixed-breed dogs, where linkage disequilibrium rapidly decays between markers, even though such studies would hold particular promise for mapping complex diseases and traits. Here we introduce an imputation reference panel, consisting of 365 diverse, whole-genome sequenced dogs and wolves, which increases the number of markers that can be queried in genome-wide association studies approximately 130-fold. Using previously genotyped dogs, we show the utility of this reference panel in identifying novel associations and fine-mapping for canine body size and blood phenotypes, even when causal loci are not in strong linkage disequilibrium with any single array marker. This reference panel resource will improve future genome-wide association studies for canine complex diseases and other phenotypes.Author SummaryComplex traits are controlled by more than one gene and as such are difficult to map. For complex trait mapping in the domestic dog, researchers use the current array of 173,000 variants, with only minimal success. Here, we use a method called imputation to increase the number of variants – from 173,000 to 24 million – that can be queried in canine association studies. We use sequence data from the whole genomes of 365 dogs and wolves to accurately predict variants, in a separate cohort of dogs, that are not present on the array. Using dog body size, we show that the increase in variants results in an increase in mapping power, through the identification of new associations and the narrowing of regions of interest. This imputation panel is particularly important because of its usefulness in improving complex trait mapping in the dog, which has significant implications for discovery of variants in humans with similar diseases.