Sentence examples for most quantitative traits from inspiring English sources
Evolution may reduce extinction under global warming, but few studies have explored how genetic covariation, the norm for most quantitative traits, will affect the course of evolution under rapid climatic change.
Recent advances in genotyping and sequencing technologies have enabled high-resolution mapping of quantitative trait loci in many species, revealing that most quantitative traits are highly polygenic and that segregating alleles have context-dependent effects.
Studies of genetic architecture have revealed that epistasis, i.e. interactions between loci, is a common component of most quantitative traits.
Quantitative trait loci (QTL) mapping studies have shown that most quantitative traits are affected significantly by a finite number of genes [30], which are neither evenly distributed nor equally contributing to the trait of interest.
This is because trait heterosis under complete dominance or additivity for all loci (Table S3 and Fig. S3) contradicts the commonly observed importance of additive gene actions for most quantitative traits and the observed variation in the levels and directions of heterosis.
Most quantitative traits are affected by multiple QTL, rather than single QTL as assumed here.
Motivation: In most quantitative trait locus (QTL) mapping studies, phenotypes are assumed to follow normal distributions.
Until now, most quantitative trait loci (QTLs) were identified using bi-parental linkage mapping populations.
Because multiple traits have historically been selected in concert and most quantitative trait loci have small effects, selection has incrementally altered allele frequencies throughout the genome.
To date, most quantitative trait loci (QTL) analyses have been performed on F2 crosses between outbred pig strains and have led to the identification of numerous QTL.
The biological and molecular basis of most quantitative trait variation is poorly understood and QTL mapping approaches generally result in too large numbers of candidate genes to be able to identify causal genes easily.
