Biology subjects

Beissinger, T.

Publications and source records attributed to Beissinger, T..

3 recordsLinked to original sources

bioRxiv2025

Genome-wide association study in European beech (Fagus sylvatica L.) for drought stress traits

Forest tree breeding is an extremely long and tedious process. To study the genetic architecture of polygenic traits in long-lived species such as forest trees, costly field experiments are implemented. Phenotypic data on traits, that are measured at maturity, are only available after a long time and juvenile-mature correlations are often unknown. Genome-wide association studies (GWAS) aim to identify loci associated with drought stress, tree physiology, growth or wood quality traits, which could be prioritized in breeding programs. Genotypic and phenotypic data were collected from approximately 100 adult beech trees per stand in five locations in the South-Eastern Romanian Carpathians along an altitudinal gradient associated with precipitation and temperature. We performed GWAS using PLINK to identify SNP markers associated with traits related to drought stress. A total of 121 markers on eight chromosomes were identified as being associated with stomatal density. Sixty-four markers are located on chromosome 10 in a region spreading from [~]4.89 to 13.67 Mb. There are five genes in this region that are thought to play a role in controlling stomatal density. All markers within this region exhibit similar allele frequencies, which are correlated with stomatal density and the altitudinal gradient of the stands. We assume this entire region is jointly involved in local adaptation to drought stress. We identified one interesting candidate SNP associated with leaf nitrogen content. Two SNP markers were identified as being significantly associated with {delta}13C as measure of intrinsic water use efficiency. Additionally, signals of significant polygenic selection for {delta}13C were observed.

genetics↗

bioRxiv2024

Experimental evolution in maize with replicated divergent selection identifies plant-height associated SNPs

Experimental evolution studies are common in agricultural research, where they are often deemed "long term selection". These are often used to perform selection mapping, which involves identifying markers which were putatively under selection based on finding signals of selection left in the genome. A challenge of previous selection mapping studies, especially in agricultural research, has been the specification of robust significance thresholds. This is in large part because long term selection studies in crops have rarely included replication. Usually, significance thresholds in long term selection experiments are based on outliers from an empirical distribution. This approach is prone to missing true positives or including false positives. Under laboratory conditions with model species, replicated selection has been shown to be a powerful tool, especially for the specification of significance thresholds. Another challenge is that commonly used single-marker based statistics may identify neutral linked loci which have hitchhiked along with regions that are actually under selection. In this study, we conducted divergent, replicated selection for short and tall plant-height in a random mating maize population under real field conditions. Selection of the 5% tallest and shortest plants was conducted for three generations. Significance thresholds were specified using the false discovery rate for selection (FDRfS) based on a window-based statistic applied on a statistic leveraging replicated selection (FSTSum). Overall, we found 3 significant regions putatively under selection. One region was located on chromosome 3 close to the plant-height genes Dwarf1 and iAA8. We applied a haplotype block analysis to further dissect the pattern of selection in significant regions of the genome. We observed patterns of strong selection in the subpopulations selected for short plant height on chromosome 3.

genetics↗

bioRxiv2020

Ghat: An R package for identifying adaptive polygenic traits

Identifying selection on polygenic complex traits in crops and livestock is important for understanding evolution and helps prioritize important characteristics for breeding. The QTL that contribute to polygenic trait variation often exhibit small or infinitesimal effects. This hinders the ability to detect QTL controlling polygenic traits because enormously high statistical power is needed for their detection. Recently, we circumvented this challenge by introducing a method to identify selection on complex traits by evaluating the relationship between genome-wide changes in allele frequency and estimates of effect-size. The approach involves calculating a composite-statistic across all markers that captures this relationship, followed by implementing a linkage disequilibrium-aware permutation test to evaluate if the observed pattern differs from that expected due to drift during evolution and population stratification. In this manuscript, we describe "Ghat", an R package developed to implement this method to test for selection on polygenic traits. We demonstrate the package by applying it to test for polygenic selection on 15 published European wheat traits including yield, biomass, quality, morphological characteristics, and disease resistance traits. Moreover, we applied Ghat to different simulated populations with different breeding history and genetic architecture. The results highlight the power of Ghat to identify selection on complex traits. The Ghat package is accessible on CRAN, the Comprehensive R Archival Network, and on GitHub.

genomics↗