Biology subjects

Kelly, J. K.

Publications and source records attributed to Kelly, J. K..

4 recordsLinked to original sources

bioRxiv2018

Extreme copy number variation at a tRNA ligase affecting phenology and fitness in yellow monkeyflowers

Copy number variation (CNV) is a major part of the genetic diversity segregating within populations, but remains poorly understood relative to single nucleotide variation. Here, we report on a tRNA ligase gene (Migut.N02091; RLG1a) exhibiting unprecedented, and fitness-relevant, CNV within an annual population of the yellow monkeyflower Mimulus guttatus. RLG1a variation was associated with multiple traits in pooled population sequencing (PoolSeq) scans of phenotypic and phenological cohorts. Resequencing of inbred lines revealed intermediate frequency three-copy variants of RLG1a (trip+; 5/35 = 14%), and trip+ lines exhibited elevated RLG1a expression under multiple conditions. trip+ carriers, in addition to being over-represented in late-flowering and large-flowered PoolSeq populations, flowered later under stressful conditions in a greenhouse experiment (P < 0.05). In wild population samples, we discovered an additional rare RLG1a variant (high+) that carries 250-300 copies of RLG1a totaling [~]5.7Mb (20-40% of a chromosome). In the progeny of a high+ carrier, Mendelian segregation of diagnostic alleles and qPCR-based copy counts indicate that high+ is a single tandem array unlinked from the single copy RLG1a locus. In the wild, high+ carriers had highest fitness in two particularly dry and/or hot years (2015 and 2017; both p < 0.01), while single copy individuals were twice as fecund as either CNV type in a lush year (2016: p < 0.005). Our results demonstrate fluctuating selection on CNVs affecting phenological traits in a wild population, suggest that plant tRNA ligases mediate stress-responsive life-history traits, and introduce a novel system for investigating the molecular mechanisms of gene amplification.

evolutionary biology↗

bioRxiv2018

An examination of the evolve-and-resequence method using Drosophila simulans

We develop a set of analytical and simulation tools for Evolve-and-Resequence (E&R) experiments and apply them to a new study of rapid evolution in Drosophila simulans. Likelihood based test statistics applied to pooled population sequencing data suggest parallel evolution of 138 polymorphisms (SNPs) across the genome. This number is reduced by orders of magnitude from previous studies (thousands or tens of thousands), owing to differences in both experimental design and statistical analysis. Whole genome simulations calibrated from several Drosophila genetic datasets support the contention that the observed genome-wide response could be generated by as few as 30 loci under strong directional selection, with a corresponding hitch-hiking effect. Finally, the SNPs that showed strong parallel evolution in the novel laboratory environment exhibit an (initial) allele frequency spectrum indicative of balancing in nature. These loci also exhibit elevated differentiation among natural populations of D. simulans.

evolutionary biology↗

bioRxiv2018

A synthesis of mapping experiments reveals extensive genomic structural diversity in the Mimulus guttatus species complex

Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. In this paper, we tackle this challenge by developing a novel statistical approach to comparative genetic mapping. The procedure couples a Hidden Markov Model with a Genetic Algorithm to detect large-scale structural variation using low-level sequencing data from multiple genetic mapping populations. We demonstrate the method using five distinct crosses within the flowering plant genus Mimulus. The synthesis of data from these experiments is first used to correct numerous errors (misplaced sequences) in the M. guttatus reference genome. Second, we confirm and/or detect eight large inversions polymorphic within the M. guttatus species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.\n\nAUTHOR SUMMARYGenome sequences have proved to be a critical experimental resource for genetic research in many species. However, in some species there is considerable variation in genomic organization, making a single reference genome sequence inadequate. This variation can cause issues in interpreting genomic signals, such as those coming from trait mapping. We introduce a new statistical method and computational tools that use linkage information to reorganize a single reference genome to 1) repair genome assembly errors, and 2) identify variation between individuals or populations of the same species. Using this method we can create a new genome order that improves upon the reference genome. We apply this method to five crosses among plants in the Mimulus guttatus species complex. In this system we detect eight large chromosomal inversions and improve the resolution of a trait mapping study. This work highlights the utility of our method, and indicates how others studying diverse species might use them to improve their own research.

genetics↗

bioRxiv2017

The genomic architecture of flowering time varies across space and time in Mimulus guttatus

The degree to which genomic architecture varies across space and time is central to the evolution of genomes in response to natural selection. Bulked-segregant mapping combined with pooled sequencing provides an efficient method to estimate the effect of genetic variants on quantitative traits. We develop a novel likelihood framework to identify segregating variation within multiple populations and generations while accommodating estimation error on a sample- and SNP-specific basis. We use this method to map loci for flowering time within natural populations of Mimulus guttatus, collecting the early and late flowering plants from each of three neighboring populations and two consecutive generations. We find appreciable variation in genetic effects on flowering time across both time and space; the greatest differences evident between populations. Structural variants, such as inversions, and genes from multiple flowering time pathways exhibit the strongest associations with flowering time. It is also clear that genotype-by-environment interactions are an important influence on flowering time variation.

genomics↗