Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing

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.