Association genetics of growth and adaptive traits in loblolly pine (Pinus taeda L.) using whole-exome-discovered polymorphisms

Lu, Mi; Krutovsky, Konstantin V.; Nelson, C. Dana; West, Jason B.; Reilly, Nathalie A.; Loopstra, Carol A.

doi:10.1007/s11295-017-1140-1

Cited by 35 publications

(48 citation statements)

References 67 publications

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“…Using the SNP set of the current study, we previously found SNP associations with such adaptive phenotypic traits as specific leaf area, branch angle, height, diameter, crown width, carbon isotope discrimination, and nitrogen content (Lu et al, 2017a). Using the SNP set of the current study, we previously found SNP associations with such adaptive phenotypic traits as specific leaf area, branch angle, height, diameter, crown width, carbon isotope discrimination, and nitrogen content (Lu et al, 2017a).…”

Section: F I G U R E 2 Summary Of Different Approaches Used In This Smentioning

confidence: 72%

“…We applied general linear model (GLM) method (S model), mixed linear model (MLM) method incorporating a kinship matrix (K model), the GLM incorporating the covariate to adjust for population structure (Q model) and the MLM incorporating both the kinship matrix and population structure covariate (QK model) to conduct association analyses. A corrected Bonferroni threshold 0.05/94,478 = 5.29E-7, where 94,478 was the number of haplotype blocks in the studied population (Lu et al, 2017a), was applied to screen for significant loci. However, the SSR markers were only available for 249 out of the total 328 trees.…”

Section: Environmental Associations and Outlier Analysesmentioning

confidence: 99%

“…We have discovered 2.8 million SNPs using whole exome sequencing from a clonally propagated association mapping loblolly pine population (Lu et al, 2016(Lu et al, , 2017a(Lu et al, , 2017b. We have discovered 2.8 million SNPs using whole exome sequencing from a clonally propagated association mapping loblolly pine population (Lu et al, 2016(Lu et al, , 2017a(Lu et al, , 2017b.…”

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confidence: 99%

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Detecting the genetic basis of local adaptation in loblolly pine (Pinus taedaL.) using whole exome‐wide genotyping and an integrative landscape genomics analysis approach

Loopstra

Krutovsky

2019

Ecology and Evolution

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In the Southern United States, the widely distributed loblolly pine contributes greatly to lumber and pulp production, as well as providing many important ecosystem services. Climate change may affect the productivity and range of loblolly pine. Nevertheless, we have insufficient knowledge of the adaptive potential and the genetics underlying the adaptability of loblolly pine. To address this, we tested the association of 2.8 million whole exome‐based single nucleotide polymorphisms (SNPs) with climate and geographic variables, including temperature, precipitation, latitude, longitude, and elevation data. Using an integrative landscape genomics approach by combining multiple environmental association and outlier detection analyses, we identified 611 SNPs associated with 56 climate and geographic variables. Longitude, maximum temperature of the warm months and monthly precipitation associated with most SNPs, indicating their importance and complexity in shaping the genetic variation in loblolly pine. Functions of candidate genes related to terpenoid synthesis, pathogen defense, transcription factors, and abiotic stress response. We provided evidence that environment‐associated SNPs also composed the genetic structure of adaptive phenotypic traits including height, diameter, metabolite levels, and gene transcript abundance. Our study promotes understanding of the genetic basis of local adaptation in loblolly pine and provides promising tools for selecting genotypes adapted to local environments in a changing climate.

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Section: F I G U R E 2 Summary Of Different Approaches Used In This Smentioning

confidence: 72%

Section: Environmental Associations and Outlier Analysesmentioning

confidence: 99%

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confidence: 99%

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Detecting the genetic basis of local adaptation in loblolly pine (Pinus taedaL.) using whole exome‐wide genotyping and an integrative landscape genomics analysis approach

Loopstra

Krutovsky

2019

Ecology and Evolution

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“…When K = 2, the center and eastern populations are differentiated from the western populations, suggesting that the Mississippi river is the major barrier for gene flow. This population structure is coincident with previous smaller‐scale studies in the species using SNPs and simple sequence repeats (SSRs) (Eckert et al ., ,) and also with more recent ones (Lu et al ., ), suggesting a dual Pleistocene refugia model might have shaped the genetic structure of loblolly pine.…”

Section: Resultsmentioning

confidence: 99%

Genomic architecture of complex traits in loblolly pine

et al. 2018

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Summary Dissecting the genetic and genomic architecture of complex traits is essential to understand the forces maintaining the variation in phenotypic traits of ecological and economical importance. Whole‐genome resequencing data were used to generate high‐resolution polymorphic single nucleotide polymorphism (SNP) markers and genotype individuals from common gardens across the loblolly pine (Pinus taeda) natural range. Genome‐wide associations were tested with a large phenotypic dataset comprising 409 variables including morphological traits (height, diameter, carbon isotope discrimination, pitch canker resistance), and molecular traits such as metabolites and expression of xylem development genes. Our study identified 2335 new SNP × trait associations for the species, with many SNPs located in physical clusters in the genome of the species; and the genomic location of hotspots for metabolic × genotype associations. We found a highly polygenic basis of quantitative inheritance, with significant differences in number, effects size, genomic location and frequency of alleles contributing to variation in phenotypes in the different traits. While mutation‐selection balance might be shaping the genetic variation in metabolic traits, balancing selection is more likely to shape the variation in expression of xylem development genes. Our work contributes to the study of complex traits in nonmodel plant species by identifying associations at a whole‐genome level.

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“…Various studies attempted GWAS for growth traits in forest trees, namely in Populus (Porth et al ., ; Allwright et al ., ; Du et al ., ; Fahrenkrog et al ., ), Pinus (Bartholomé et al ., ; Lu et al ., ) and Eucalyptus (Cappa et al ., ; Müller et al ., ; Resende et al ., ). Despite the considerably large number of individuals used in our study for each population and for the combined dataset, our results suggested that much larger numbers will be necessary to identify discrete regions capturing larger fractions of the genetic variance of complex traits as indicated by simulations (Spencer et al ., ; Visscher et al ., ).…”

Section: Discussionmentioning

confidence: 99%

Independent and Joint‐GWASfor growth traits inEucalyptusby assembling genome‐wide data for 3373 individuals across four breeding populations

Müller

Filho

Lima³

et al. 2018

New Phytologist

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Summary Genome‐wide association studies (GWAS) in plants typically suffer from limited statistical power. An alternative to the logistical and cost challenge of increasing sample sizes is to gain power by meta‐analysis using information from independent studies. We carried out GWAS for growth traits with six single‐marker models and regional heritability mapping (RHM) in four Eucalyptus breeding populations independently and by Joint‐GWAS, using gene and segment‐based models, with data for 3373 individuals genotyped with a communal EUChip60KSNP platform. While single‐single nucleotide polymorphism (SNP) GWAS hardly detected significant associations at high‐stringency in each population, gene‐based Joint‐GWAS revealed nine genes significantly associated with tree height. Associations detected using single‐SNP GWAS, RHM and Joint‐GWAS set‐based models explained on average 3–20% of the phenotypic variance. Whole‐genome regression, conversely, captured 64–89% of the pedigree‐based heritability in all populations. Several associations independently detected for the same SNPs in different populations provided unprecedented GWAS validation results in forest trees. Rare and common associations were discovered in eight genes involved in cell wall biosynthesis and lignification. With the increasing adoption of genomic prediction of complex phenotypes using shared SNPs and much larger tree breeding populations, Joint‐GWAS approaches should provide increasing power to pinpoint discrete associations potentially useful toward tree breeding and molecular applications.

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Association genetics of growth and adaptive traits in loblolly pine (Pinus taeda L.) using whole-exome-discovered polymorphisms

Cited by 35 publications

References 67 publications

Detecting the genetic basis of local adaptation in loblolly pine (Pinus taedaL.) using whole exome‐wide genotyping and an integrative landscape genomics analysis approach

Detecting the genetic basis of local adaptation in loblolly pine (Pinus taedaL.) using whole exome‐wide genotyping and an integrative landscape genomics analysis approach

Genomic architecture of complex traits in loblolly pine

Independent and Joint‐GWASfor growth traits inEucalyptusby assembling genome‐wide data for 3373 individuals across four breeding populations

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