Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing
Data files
Feb 12, 2024 version files 5.12 GB
Abstract
Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model wild populations. Specifically, large-sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine-spined sticklebacks (Pungitius pungitius) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half-sib/full-sib design allowed precise estimation of genetic variance components, and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome-partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogenous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large-sized populations.
README: Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing
This repository contains all data and code necessary to reproduce the analyses and figures performed in the manuscript.
Directories
root directory: this directory contains the R codes necessary to format the data and produce the "DataAndGRMsHelsinki.RData" input file (see below), construct the GRMs, run the animal models, perform the correction from Legarra et al. 2015 and perform QTL mapping.
"data": this directory contains the raw genotypic (genotypeTable & genotypeMap) and phenotypic (complete_pheno_data.txt) data for the article. It also contains a summarized .RData file ("DataAndGRMsHelsinki.RData") containing the ready-to-use formated data combining both genotype and phenotype data.
"figures&tables": this directory contains all R codes to reproduce the figures from main text and supplementary material. Code for Fig.1 and Fig.6 are embeded in their corresponding analysis script in the root directory.
"results": this directory contains all necessary outputs from the animal models, chromosome partitioning, QTL mapping and simulation studies.
Sub-directories
4a. "Animal Models": this directory contains all the outputs from the MCMCglmm animal models used to produce Fig. 1.
4b. "Chromosome Partitioning": this directory contains all raw material used to run the chromosome partitioning analyses with GCTA and outputs to produce Fig. 4-5.
4c. "QTL mapping": this directory contains all raw data and source codes used to run the QTL mapping analyses.
4d. "Simulations": this directory contains all raw material obtained from the Nemo simulations and all animal models outputs ran on the simulated datasets and used as input for Fig. 2 and Fig. 3
Description of R scripts
"00_prepare_data_final.R": this is the script used to format the data for downstream analyses
"01_make_GRMs_final.R": this script is used to estimate the Genomic Relationship Matrices
"02_run_animal_models_final.R": script used to run the MCMCglmm animal models
"03_Legarra_corrections_and_Figure1_final.R": script to apply the correction from Legarra et al. on the estimated variance components, and to produce Fig.1
"04_QTL_mapping_and_Figure6_final.R": script to run the singlemapping fourway QTL analyses and producing the corresponding Figure 6.