DOI: 10.5061/dryad.b2rbnzsp9
title: Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing

description: 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.
description: # 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 1. 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. 2. "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. 3. "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. 4. "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.