Landscape genomics analysis of Achyranthes bidentata reveal adaptive genetic variations are driven by environmental variations relating to ecological habit

Abstract

Knowledge on adaptive genetic variation in response to environmental variation is the key to understanding the adaptive evolution potential of species. China's warm‐temperate zone is an important climatic zone, but only a few landscape genomics studies have been conducted to understand the adaptive evolution of regional vegetation. In this study, natural populations of Achyranthes bidentata Blume were sampled in China's warm‐temperate zone to infer its adaptive evolution using landscape genomics methods. Four SCoT primers were used to investigate the adaptive evolution of A. bidentata in response to environmental variation across the warm‐temperate zone of China. A total of 126 individuals from fifteen natural populations were successfully scored, and 202 unambiguous fragments were obtained. Twenty‐three outlier loci were identified, eighteen outlier loci were significantly associated with environmental variables. Redundancy analytical results suggested that four environmental variables related to temperature and precipitation remarkably influenced the distribution of loci. The results provide empirical evidence that molecular markers with bias toward candidate functional genes might be suitable for landscape genomics studies. Temperature and precipitation jointly drive the adaptive evolution of A. bidentata. The key driving environmental factors identified in this study are mostly related to the ecological habit of A. bidentata. The species personality, i.e., ecological habit, seems to play an important role in the adaptive differentiation on A. bidentata.

Genome sequencing and population genomics modeling provide insights into the local adaptation of weeping forsythia

“…Additional important questions are whether, when and how natural selection introduced by environmental variables will affect population genetic structure. The genetic structure of a species is affected by many factors 53,54 . The identification of population genetic structure at the genome level can help provide a comprehensive understanding of the effects of these factors on population genetic structure.…”

“…Although the TREEMIX results showed gene flow between the EG and NWG, NG, and SG groups, the current amount of gene flow is not sufficient to eliminate the genetic differentiation caused by ecological isolation. In previous population genetics studies, we have paid more attention to the effects of genetic drift and gene flow on population genetic structure due to the use of neutral markers 53,54 . However, natural selection is also an important force shaping the population genetic structure of species 17 .…”

Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics

“…Here, the results of LFMM analysis showed that Sr6, Bio6, Bio4, and Wvp1 were associated with the highest numbers of EAL, which suggested that these environmental variables play a major role on the genetic differentiation of P. stenoptera genome. Recent landscape genomic studies have proposed a hypothesis that environmental variables related to ecological habitats of species play key roles in driving adaptive differentiation of species genome [ 2 , 14 , 15 ]. Among the four environmental variables associated with the largest number of adaptive loci, Sr6 was associated with the ecological habitat of “prefered light”, and Bio6 and Wvp1 were associated with the ecological habitat of “warm and humid environment”.…”

“…All individuals were genotyped using SCoT markers. Despite its inherent defects lacking DNA sequence information, the SCoT marker has been used for landscape genomic studies because it has the advantages of non-requirement for genomic information, high repeatability, and high throughput [ 2 , 14 , 15 ]. After preliminary screening of the polymorphism and reproducibility of all SCoT primers [ 25 ], nine primers (SCoT5, SCoT9, SCoT16, SCoT18, SCoT25, SCoT27, SCoT30, SCoT31, and SCoT35) were selected for polymerase chain reactions (PCRs).…”

“…Are there common reasons behind these differences? Several recent landscape genomic studies have proposed a hypothesis that environmental variables related to ecological habitats play key roles in driving species adaptive evolution; in other words, the genes associated with these environmental variables will undergo adaptive differentiation [ 2 , 14 , 15 ]. More landscape genomic research is needed to test whether this hypothesis applies to other species as well.…”