Population genomics reveals a fine-scale recombination landscape for genetic improvement of cotton

Recombination breaks up ancestral linkage disequilibrium, creates combinations of alleles, affects the efficiency of natural selection, and plays a major role in crop domestication and improvement. However, there is little knowledge regarding the variation in the population-scaled recombination rate in cotton. We constructed recombination maps and characterized the difference in the genomic landscape of the population-scaled recombination rate between Gossypium hirsutum and G. arboreum and sub-genomes based on the 381 sequenced G. hirsutum and 215 G. arboreum accessions. Comparative genomics identified large structural variations and syntenic genes in the recombination regions, suggesting that recombination was related to structural variation and occurred preferentially in the distal chromosomal regions. Correlation analysis indicated that recombination was only slightly affected by geographical distribution and breeding period. A genome-wide association study (GWAS) was performed with 15 agronomic traits using 267 cotton accessions and identified 163 quantitative trait loci (QTL) and an important candidate gene (Ghir_COL2) for early maturity traits. Comparative analysis of recombination and a GWAS revealed that the QTL of fibre quality traits tended to be more common in high-recombination regions than were those of yield and early maturity traits. These results provide insights into the population-scaled recombination landscape, suggesting that recombination contributed to the domestication and improvement of cotton, which provides a useful reference for studying recombination in other species.

LDJump: Estimating variable recombination rates from population genetic data

As recombination plays an important role in evolution, its estimation and the identification of hotspot positions is of considerable interest. We propose a novel approach for estimating population recombination rates based on genotyping or sequence data that involves a sequential multiscale change point estimator. Our method also permits demography to be taken into account. It uses several summary statistics within a regression model fitted on suitable scenarios. Our proposed method is accurate, computationally fast, and provides a parsimonious solution by ensuring a type I error control against too many changes in the recombination rate. An application to human genome data suggests a good congruence between our estimated and experimentally identified hotspots. Our method is implemented in the R‐package LDJump, which is freely available at https://github.com/PhHermann/LDJump.

Genome-wide patterns of recombination, linkage disequilibrium and nucleotide diversity from pooled resequencing and single nucleotide polymorphism genotyping unlock the evolutionary history of Eucalyptus grandis

We used high-density single nucleotide polymorphism (SNP) data and whole-genome pooled resequencing to examine the landscape of population recombination (ρ) and nucleotide diversity (ϴw ), assess the extent of linkage disequilibrium (r(2) ) and build the highest density linkage maps for Eucalyptus. At the genome-wide level, linkage disequilibrium (LD) decayed within c. 4-6 kb, slower than previously reported from candidate gene studies, but showing considerable variation from absence to complete LD up to 50 kb. A sharp decrease in the estimate of ρ was seen when going from short to genome-wide inter-SNP distances, highlighting the dependence of this parameter on the scale of observation adopted. Recombination was correlated with nucleotide diversity, gene density and distance from the centromere, with hotspots of recombination enriched for genes involved in chemical reactions and pathways of the normal metabolic processes. The high nucleotide diversity (ϴw = 0.022) of E. grandis revealed that mutation is more important than recombination in shaping its genomic diversity (ρ/ϴw = 0.645). Chromosome-wide ancestral recombination graphs allowed us to date the split of E. grandis (1.7-4.8 million yr ago) and identify a scenario for the recent demographic history of the species. Our results have considerable practical importance to Genome Wide Association Studies (GWAS), while indicating bright prospects for genomic prediction of complex phenotypes in eucalypt breeding.

A fast estimate for the population recombination rate based on regression

Recombination is a fundamental evolutionary force. Therefore the population recombination rate ρ plays an important role in the analysis of population genetic data; however, it is notoriously difficult to estimate. This difficulty applies both to the accuracy of commonly used estimates and to the computational efforts required to obtain them. Some particularly popular methods are based on approximations to the likelihood. They require considerably less computational efforts than the full-likelihood method with not much less accuracy. Nevertheless, the computation of these approximate estimates can still be very time consuming, in particular when the sample size is large. Although auxiliary quantities for composite likelihood estimates can be computed in advance and stored in tables, these tables need to be recomputed if either the sample size or the mutation rate θ changes. Here we introduce a new method based on regression combined with boosting as a model selection technique. For large samples, it requires much less computational effort than other approximate methods, while providing similar levels of accuracy. Notably, for a sample of hundreds or thousands of individuals, the estimate of ρ using regression can be obtained on a single personal computer within a couple of minutes while other methods may need a couple of days or months (or even years). When the sample size is smaller (n ≤ 50), our new method remains computational efficient but produces biased estimates. We expect the new estimates to be helpful when analyzing large samples and/or many loci with possibly different mutation rates.