Genetic Dissection of Growth and Eco-Physiological Traits Associated with Altitudinal Adaptation in Sakhalin Fir (Abies sachalinensis) Based on QTL Mapping

Abstract

(1) Background: The genetic basis of local adaptation in conifers remains poorly understood because of limited research evidence and the lack of suitable genetic materials. Sakhalin fir (Abies sachalinensis) is an ideal organism for elucidating the genetic basis of local adaptation because its altitudinal adaptation has been demonstrated, and suitable materials for its linkage mapping are available. (2) Method: We constructed P336 and P236 linkage maps based on 486 and 516 single nucleotide polymorphisms, respectively, that were derived from double digest restriction site-associated DNA sequences. We measured the growth and eco-physiological traits associated with morphology, phenology, and photosynthesis, which are considered important drivers of altitudinal adaptation. (3) Results: The quantitative trait loci (QTLs) for growth traits, phenology, needle morphology, and photosynthetic traits were subsequently detected. Similar to previous studies on conifers, most traits were controlled by multiple QTLs with small or moderate effects. Notably, we detected that one QTL for the crown area might be a type-A response regulator, a nuclear protein responsible for the cytokinin-induced shoot elongation. (4) Conclusion: The QTLs detected in this study include potentially important genomic regions linked to altitudinal adaptation in Sakhalin fir.

Quantitative trait loci mapping reveals important genomic regions controlling root architecture and shoot biomass under nitrogen, phosphorus, and potassium stress in rapeseed (Brassica napus L.)

“…The genomic region associated with many traits is biologically intriguing because it may harbor important regulators. It signifies the existence of a single gene with a pleiotropic effect or closely linked loci controlling two or more traits ( Goto et al, 2021 ). The shared genomic region in this study is referred to as the QTL hotspot or QTL cluster.…”

Plethora of QTLs found inArabidopsis thalianareveals complexity of genetic variation for photosynthesis in dynamic light conditions

“…However, identification of the causal genes underlying a QTL can generate additional insights on the impact of natural genetic variation on the photosynthetic functioning (Theeuwen et al ., 2022 b ). In both A. thaliana and crops species a range of previous studies have revealed QTLs for photosynthesis, but hardly any of these QTLs have been used to identify the underlying causal gene(s) (Poormohammad Kiani et al ., 2008; Jung and Niyogi, 2009; Wang et al ., 2017; Oakley et al ., 2018 a ; Rungrat et al ., 2019; Goto et al ., 2021). In this study, the QTL contributing the most to the difference between the Ely and Col nucleotypes in high to low-light transitions is QTL-2 18,500 (Figures 2, 5 and 6).…”

“…Despite the relative absence of natural genetic variation in the core photosynthetic machinery, there is ample phenotypic variation for photosynthesis, implying there is standing genetic variation outside the core machinery. This is also true for NPQ dynamics, as quantitative trait loci (QTLs) are identified for NPQ in regions of the genome that do not include any of the known NPQ related genes (Poormohammad Kiani et al ., 2008; Jung and Niyogi, 2009; Wang et al ., 2017; Oakley et al ., 2018 a ; Rungrat et al ., 2019; Goto et al ., 2021). Unfortunately, in hardly any of these cases the causal genes have been revealed, even though identifying the genes underlying QTLs opens up novel targets for improving the dynamic responses of NPQ, as well as forming an opportunity to expand the physiological understanding of NPQ (Theeuwen et al ., 2022 b ).…”

Genotype-by-environment interaction and genetic dissection of heartwood color in Cryptomeria japonica based on multiple common gardens and quantitative trait loci mapping

“…The methodology of QTL analysis conducted in this study was fully described by Goto et al [ 38 ]. QTL analysis was conducted using hierarchical linear models with a regularized horseshoe prior distribution.…”