- Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences
- Umeå University
Hall, David; Hallingback, Henrik R.; Wu, Harry X.
Mapping the genetic architecture of forest tree traits is important in order to understand the evolutionary forces that have shaped these traits and to facilitate the development of genomic-based breeding strategies. We examined the number, size, and distribution of allelic effects influencing eight types of traits using 30 published mapping studies (linkage and association mapping) in forest trees. The sizes of allelic effects, measured as the phenotypic variance explained, generally showed a severely right-skewed distribution. We estimated the numbers of underlying causal effects (nqtl) for different trait categories by improving a method previously developed by Otto and Jones (Genetics 156:2093-2107, 2000). Estimates of nqtl based on association mapping studies were generally higher (median at 643) than those based on linkage mapping (median at 33). Comparisons with simulated linkage and association mapping data suggested that the lower nqtl estimates for the linkage mapping studies could partly be explained by fewer causal loci segregating within the full-sib family populations normally used, but also by the cosegregation of causal loci due to limited recombination. Disease resistance estimates based on linkage mapping studies had the lowest median of four underlying effects, while growth traits based on association mapping had about 580 effects. Theoretically, the capture of 50% of the genetic variation would thus require a population size of about 200 for disease resistance in linkage mapping, while growth traits in association mapping would require about 25,000. The adequacy and reliability of the improved method was successfully verified by applying it to the simulated data.
Association mapping; Linkage (QTL) mapping; Linkage disequilibrium; QTL number estimate; Size of QTL effect
Tree Genetics and Genomes
2016, Volume: 12, number: 6, article number: 110
SLU Plant Protection Network
Bioinformatics (Computational Biology)