Climate-Associated Genetic Variation and Projected Genetic Offsets for D. Don Under Future Climate Scenarios

Overview

Journal Evol Appl

Date 2025 Feb 10

PMID 39925619

Authors

Kentaro Uchiyama

Tokuko Ujino-Ihara

Katsuhiro Nakao

Jumpei Toriyama

Shoji Hashimoto

Yoshihiko Tsumura

Affiliations

Soon will be listed here.

Abstract

Revealing the spatial distribution of adaptive genetic variation is both a challenging and crucial task in evolutionary ecology, essential for understanding local adaptation within species, and in management, for predicting species responses to future climate change. This understanding is particularly important for long-lived tree species, which may not be able to migrate quickly enough to adapt to rapid climate changes and may need to rely on their standing genetic variation. In this study, we focused on , a major component of Japan's temperate forests and an important forestry species adapted to the humid environment of monsoon Asia. We extracted climate-associated genetic variation from the entire genome and evaluated its distribution and vulnerability under future climate scenarios using spatial modeling techniques. We analyzed 31,676 high-quality SNPs from 249 individuals across 22 natural populations of , covering its entire distribution range. We identified 239 candidate climate-associated SNPs and found winter temperature, summer precipitation, and winter precipitation as the most significant factors explaining the genetic variation in these SNPs. The climate-associated genetic variation deviated from non-associated (neutral) genetic variation in the opposite (the Sea of Japan and Pacific Ocean) sides of Japanese archipelago, suggesting natural selection of different climate conditions in these regions. Difference in estimated allele frequency at the climate-associated loci (genetic offset) between the present and future (2090 in the SSP5-8.5 scenario) climate conditions was predicted to be larger in three areas (not only southwestern Japan but also coastal area on the Sea of Japan side and inland area on the Pacific Ocean side in northeastern Japan). This prediction implies the discrepancy between standing genetic variation at the present and that adaptive to the future climate in these areas, which underscores the necessity for proactive management to adjust the adaptive genetic variation.

References

Li H, Durbin R . Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754-60. PMC: 2705234. DOI: 10.1093/bioinformatics/btp324. View

Kimura M, Uchiyama K, Nakao K, Moriguchi Y, San Jose-Maldia L, Tsumura Y . Evidence for cryptic northern refugia in the last glacial period in Cryptomeria japonica. Ann Bot. 2014; 114(8):1687-700. PMC: 4649686. DOI: 10.1093/aob/mcu197. View

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M, Bender D . PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81(3):559-75. PMC: 1950838. DOI: 10.1086/519795. View

Tsumura Y, Kado T, Takahashi T, Tani N, Ujino-Ihara T, Iwata H . Genome scan to detect genetic structure and adaptive genes of natural populations of Cryptomeria japonica. Genetics. 2007; 176(4):2393-403. PMC: 1950640. DOI: 10.1534/genetics.107.072652. View

Tani N, Maruyama K, Tomaru N, Uchida K, Araki M, Tsumura Y . Genetic diversity of nuclear and mitochondrial genomes in Pinus parviflora Sieb. & Zucc. (Pinaceae) populations. Heredity (Edinb). 2003; 91(5):510-8. DOI: 10.1038/sj.hdy.6800349. View

Nose M, Kurita M, Tamura M, Matsushita M, Hiraoka Y, Iki T . Effects of day length- and temperature-regulated genes on annual transcriptome dynamics in Japanese cedar (Cryptomeria japonica D. Don), a gymnosperm indeterminate species. PLoS One. 2020; 15(3):e0229843. PMC: 7062269. DOI: 10.1371/journal.pone.0229843. View

Takahashi T, Tani N, Taira H, Tsumura Y . Microsatellite markers reveal high allelic variation in natural populations of Cryptomeria japonica near refugial areas of the last glacial period. J Plant Res. 2005; 118(2):83-90. DOI: 10.1007/s10265-005-0198-2. View

Gain C, Rhone B, Cubry P, Salazar I, Forbes F, Vigouroux Y . A Quantitative Theory for Genomic Offset Statistics. Mol Biol Evol. 2023; 40(6). PMC: 10306404. DOI: 10.1093/molbev/msad140. View

Fitzpatrick M, Keller S . Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol Lett. 2014; 18(1):1-16. DOI: 10.1111/ele.12376. View

10.

Chiang F, Mazdiyasni O, Aghakouchak A . Evidence of anthropogenic impacts on global drought frequency, duration, and intensity. Nat Commun. 2021; 12(1):2754. PMC: 8115225. DOI: 10.1038/s41467-021-22314-w. View

11.

Bolger A, Lohse M, Usadel B . Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30(15):2114-20. PMC: 4103590. DOI: 10.1093/bioinformatics/btu170. View

12.

Hiura T, Yoshioka H, Matsunaga S, Saito T, Kohyama T, Kusumoto N . Diversification of terpenoid emissions proposes a geographic structure based on climate and pathogen composition in Japanese cedar. Sci Rep. 2021; 11(1):8307. PMC: 8050256. DOI: 10.1038/s41598-021-87810-x. View

13.

Peterson B, Weber J, Kay E, Fisher H, Hoekstra H . Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS One. 2012; 7(5):e37135. PMC: 3365034. DOI: 10.1371/journal.pone.0037135. View

14.

Kamvar Z, Tabima J, Grunwald N . Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ. 2014; 2:e281. PMC: 3961149. DOI: 10.7717/peerj.281. View

15.

Frichot E, Schoville S, Bouchard G, Francois O . Testing for associations between loci and environmental gradients using latent factor mixed models. Mol Biol Evol. 2013; 30(7):1687-99. PMC: 3684853. DOI: 10.1093/molbev/mst063. View

16.

Toriyama J, Hashimoto S, Osone Y, Yamashita N, Tsurita T, Shimizu T . Estimating spatial variation in the effects of climate change on the net primary production of Japanese cedar plantations based on modeled carbon dynamics. PLoS One. 2021; 16(2):e0247165. PMC: 7888599. DOI: 10.1371/journal.pone.0247165. View

17.

Caye K, Jumentier B, Lepeule J, Francois O . LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies. Mol Biol Evol. 2019; 36(4):852-860. PMC: 6659841. DOI: 10.1093/molbev/msz008. View

18.

Wang J . Fast and accurate population admixture inference from genotype data from a few microsatellites to millions of SNPs. Heredity (Edinb). 2022; 129(2):79-92. PMC: 9338324. DOI: 10.1038/s41437-022-00535-z. View

19.

Norisada M, Hara M, Yagi H, Tange T . Root temperature drives winter acclimation of shoot water relations in Cryptomeria japonica seedlings. Tree Physiol. 2005; 25(11):1447-55. DOI: 10.1093/treephys/25.11.1447. View

20.

Puritz J, Hollenbeck C, Gold J . dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms. PeerJ. 2014; 2:e431. PMC: 4060032. DOI: 10.7717/peerj.431. View