RT Journal Article
SR Electronic
T1 Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 435685
DO 10.1101/435685
A1 Campbell, Malachy
A1 Momen, Mehdi
A1 Walia, Harkamal
A1 Morota, Gota
YR 2019
UL http://biorxiv.org/content/early/2019/01/31/435685.abstract
AB Understanding the genetic basis of dynamic plant phenotypes has largely been limited due to lack of space and labor resources needed to record dynamic traits, often destructively, for a large number of genotypes. However, the recent advent of image-based phenotyping platforms has provided the plant science community with an effective means to non-destructively evaluate morphological, developmental, and physiological processes at regular, frequent intervals for a large number of plants throughout development. The statistical frameworks typically used for genetic analyses (e.g. genome-wide association mapping, linkage mapping, and genomic prediction) in plant breeding and genetics are not particularly amenable for repeated measurements. Random regression (RR) models are routinely used in animal breeding for the genetic analysis of longitudinal traits, and provide a robust framework for modeling traits trajectories and performing genetic analysis simultaneously. We recently used a RR approach for genomic prediction of shoot growth trajectories in rice using 33,674 SNPs. In this study, we have extended this approach for genetic inference by leveraging genomic breeding values derived from RR models for rice shoot growth during early vegetative development. This approach provides improvements over a conventional single time point analyses for discovering loci associated with shoot growth trajectories. The RR approach uncovers persistent, as well as time-specific, transient quantitative trait loci. This methodology can be widely applied to understand the genetic architecture of other complex polygenic traits with repeated measurements.Core Ideas:Random regression models are an appealing framework for GWAS of longitudinal traitsThis approach provides improvements over a conventional single time point analyses for GWASWe identify QTL with transient and persistent effects on shoot growth in riceAbbreviationsBLUPbest-linear unbiased predictionGEBVsGenomic estimated breeding valuesGWASgenome-wide association studyPSAprojected shoot areaQTLquantitative trait lociRDP1rice diversity panel 1DATdays after transplantRRrandom regressionSMRsingle marker regressionSNPsingle nucleotide polymorphismTPsingle time point