Abstract

An increase in the distribution of data points indicates the presence of genetic or environmental modifiers. Mapping of the genetic control of the spread of points, the uniformity, allows us to allocate genetic difference in point distribution to adjacent, cis effects or to independently segregating, trans genetic effects. Our genetic architecture-mapping experiment elucidated the “environmental context specificity” of modifiers, the number and effect size of positive and negative alleles important for uniformity in single and combined stress, and the extent of additivity in estimated allele effects in combined stress environments. We found no alleles for low uniformity in combined stress treatments in the maize mapping population we examined. The major advances in this research area since early 2011 have been in improved methods for modeling of distributions and means and detection of important loci. Double hierarchical general linear models and, more recently, a likelihood ratio formulation have been developed to better model and estimate the genetic and environmental effects in populations. These new methods have been applied to real data sets by the method authors and we now encourage additional development of the software and wider application of the methods. We also propose that simulations of genetic regulatory network models to examine differences in uniformity and systematic exploration of models using shared simulations across communities of researchers would be constructive avenues for developing further insight into the genetic mechanisms of variation control.

Highlights

  • There is useful information in the distribution of data as well as the mean (Cleasby and Nakagawa, 2011; Geiler-Samerotte et al, 2013); genetic analysis of distributions can be especially informative (Hill and Mulder, 2010; Ronnegard and Valdar, 2012)

  • We detected only one coincident cis quantitative trait locus (QTL)

  • All the uniformity alleles we found do decrease uniformity, though we could not separate the effects of the modifier and the allele in the way we color-coded our hypothetical example, as we carried out separate Levene and mean analyses and compared them by map overlay

Read more

Summary

Introduction

There is useful information in the distribution of data as well as the mean (Cleasby and Nakagawa, 2011; Geiler-Samerotte et al, 2013); genetic analysis of distributions can be especially informative (Hill and Mulder, 2010; Ronnegard and Valdar, 2012). This pattern is different than the architecture of mean plant height, which has loci with both high and low allele effects under combined stress.

Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call