Identifying Genetic Variation Associated With Environmental Variation and Drought-tolerance Phenotypes in Ponderosa Pine

Abstract

Background Genotype-to-environment (G2E) association analysis coupled with genotype-to-phenotype (G2P) association analysis promises exciting advances towards discovering genes responsible for local adaptation. We combine G2E and G2P analysis with gene annotation in Pinus ponderosa (ponderosa pine), an ecologically and economically important conifer that lacks a sequenced genome, to identify genetic variants and gene functions that may be associated with local adaptation to drought. Results We identified SNP markers in 223 genotypes from across the Sierra Nevada by aligning GBS sequence fragments to the reference genome of Pinus taeda (loblolly pine). Focusing on SNPs in or near coding regions, we found 1458 associated with 5 largely-uncorrelated climate variables, with the largest number (1151) associated with April 1st snow pack. We also planted seeds from a subset of these trees in the greenhouse, subjected half of the seedlings to a drought treatment, and measured phenotypes thought to be associated with drought tolerance, including root length and stomatal density. 817 SNPs were associated with the control-condition values of six traits, while 1154 were associated with responsiveness of these traits to drought. Conclusions While no individual SNPs were associated with both the environmental variables and the measured traits, several categories of genes were associated with both, particularly those involved in cell wall formation, biotic and abiotic stress responses, and ubiquitination. However, functions of many of the associated genes have not yet been determined due to the lack of gene annotation information for trees and future studies are needed.

Field Trials to Assess the Growth, Survival, and Stomatal Densities of Five Mexican Pine Species and Their Hybrids under Common Plantation Conditions

“…Stomatal density was the highest in the drought-tolerant P. engelmannii [26,30] (Table 7). This finding is consistent with those of other studies that have reported a higher stomatal density and/or number of stomatal rows in P. ponderosa [47] and in some Mediterranean pines under drought conditions [48][49][50]. According to Afas et al [51] and Shu [47], a higher stomatal density could enable increased leaf gas exchange during short, favorable periods and greater control of water loss and gas exchange under drought stress in harsh dry conditions.…”

“…This finding is consistent with those of other studies that have reported a higher stomatal density and/or number of stomatal rows in P. ponderosa [47] and in some Mediterranean pines under drought conditions [48][49][50]. According to Afas et al [51] and Shu [47], a higher stomatal density could enable increased leaf gas exchange during short, favorable periods and greater control of water loss and gas exchange under drought stress in harsh dry conditions. Stomatal density depends on different environmental factors, such as water stress [52] and changes in ambient CO 2 concentration [53].…”

Heritability of plastic trait changes in drought‐exposed ponderosa pine seedlings

“…That study calculated “repeatability,” which represents the upper limit of narrow‐sense heritability, and found low but significant repeatability for resistance (0.07) and resilience (0.21) when all trees were lumped together, and higher repeatability within provenances (a maximum of 0.39). We treated our families of ponderosa pine as a single “population,” which is supported by landscape genetic data for the trees from the Chico orchard suggesting high connectivity via pollen movement and isolation by distance rather than multiple genetic clusters (Shu, 2020). This yielded heritability estimates ranging from 0.0723 to 0.2913 for raw trait values and 0.1526 to 0.6547 for trait plasticity in response to drought.…”

“…The five variables were mean climatic water deficit (CWD, a measure of evaporative demand exceeding soil moisture); mean minimum winter (December-February) temperature (TMIN); mean maximum summer (June-August) temperature (TMAX); mean monthly winter precipitation (PPTW); and mean April 1 snowpack (PCK4). We chose these variables because they showed low correlation with one another and were associated with genetic variation in the adult trees (Shu, 2020). To test whether trait values or their responsiveness to drought were related to maternal home climate, we ran linear regressions in R with home climate variables as the predictors and each of the raw trait values and drought responses as the responses.…”

Identifying genetic variation associated with environmental gradients and drought‐tolerance phenotypes in ponderosa pine

“…Furthermore, the P. taeda reference genome was successfully used to design probes for sequence capture in P. contorta (Suren et al., 2016; Yeaman et al., 2016), a distant relative. Based on preliminary analyses, we selected the Stacks v.2.2 pipeline (Rochette & Catchen, 2017) with this reference genome (https://treegenesdb.org/FTP/Genomes/Pita/) for SNP calling (Shu, 2020). Each step in the Stacks reference pipeline was performed internally in Stacks algorithms except alignment with BWA v.0.7.17 (Li & Durbin, 2009) and the Samtools v.1.9 (Li, 2011) step used to get read position.…”