scholarly journals Identification of genetic markers and wood properties that predict wood biorefinery potential in aspen bioenergy feedstock (Populus tremula).

2021 ◽  
Author(s):  
Sacha Escamez ◽  
Mikko Luomaranta ◽  
Niklas Mahler ◽  
Madhavi Latha Gandla ◽  
Kathryn M Robinson ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Genetic Markers ◽  
Genome Wide Association Study ◽  
Growth Traits ◽  
Wood Properties ◽  
Yield Traits ◽  
Bioenergy Feedstock ◽  
European Aspen ◽  
Wood Traits ◽  
Wood Chemical Composition

Wood represents the majority of the biomass on lands, and it constitutes a renewable source of biofuels and other bioproducts. However, wood is recalcitrant to bioconversion, meaning that feedstocks must be improved. We investigated the properties of wood that affect bioconversion, as well as the underlying genetics, to help identify superior biorefinery tree feedstocks. We recorded as many as 65 wood-related and growth traits in a population of European aspen natural genotypes. These traits included three growth and field performance traits, 20 traits for wood chemical composition, 17 traits for wood anatomy and structure, and 25 wood saccharification traits as indicators of bioconversion potential. We used statistical modelling to determine which wood traits best predict bioconversion yield traits. This way, we identified a core set of wood properties that predict bioprocessing traits. Several of these predictor traits showed high broad-sense heritability, suggesting potential for genetic improvement of feedstocks. Finally, we performed genome-wide association study (GWAS) to identify genetic markers for yield traits or for wood traits that predict yield. GWAS revealed only a few genetic markers for saccharification yield traits, but many more SNPs were associated with wood chemical composition traits, including predictors traits for saccharification. Among them, 16 genetic markers associated specifically with lignin chemical composition were situated in and around two genes which had not previously been associated with lignin. Our approach allowed linking aspen wood bioprocessing yield to wood properties and the underlying genetics, including the discovery of two new potential regulator genes for wood chemical composition.

Growth and wood properties of genetically improved loblolly pine: propagation type comparison and genetic parameters

2014 ◽  
Vol 44 (3) ◽  
pp. 263-272 ◽  
Author(s):  
Finto Antony ◽  
Laurence R. Schimleck ◽  
Lewis Jordan ◽  
Benjamin Hornsby ◽  
Joseph Dahlen ◽  
...  
Keyword(s):  
Wood Density ◽  
Loblolly Pine ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Tree Height ◽  
Wood Properties ◽  
Genetically Improved ◽  
Growth Properties ◽  
Wood Traits ◽  
Clonal Lines

The use of clonal varieties in forestry offers great potential to improve growth traits (quantity) and wood properties (quality) of loblolly pine (Pinus taeda L.). Loblolly pine trees established via somatic embryogenesis (clones), full-sib zygotic crosses, and half-sib zygotic open-pollinated families were sampled to identify variation in growth and wood properties among and within clonal lines and zygotic controls. Increment cores 5 mm in diameter were collected at age 4 from a total of 2615 trees. Growth properties (diameter at 1.4 m and total tree height) and wood properties (whole-core density, latewood and earlywood density, and latewood percent) were measured for each tree sampled in the study. Overall, growth properties were better for full-sib seedling than for clonal lines, whereas wood density was higher for clonal lines than full-sib and open-pollinated seedlings. However, there were clonal lines with better growth and higher wood density. Clonal repeatability of both growth and wood properties across sampled sites and genetic correlations between growth and wood traits were determined, with higher repeatability observed for wood traits compared with growth traits. Significant genetic correlations were observed for tree height and wood properties, whereas weak correlations were observed for diameter and wood properties.

scholarly journals Inheritance and Correlation Analysis of Pulpwood Properties, Wood Density, and Growth Traits of Slash Pine

Forests ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 493 ◽  
Author(s):  
Yanjie Li ◽  
Xianyin Ding ◽  
Jingmin Jiang ◽  
Qifu Luan
Keyword(s):  
Wood Density ◽  
Growth Traits ◽  
Lignin Content ◽  
Pinus Elliottii ◽  
Wood Properties ◽  
Slash Pine ◽  
Wood Traits ◽  
Breeding Selection ◽  
The Relationship

Slash pine (Pinus elliottii) is the most important and a fast-growing material that is used for industrial timber and pulp production. A breeding program of slash pine that aims to improve wood properties has been employed for the past decade. This study analysed the genetics and correlation of growth traits and wood properties of a total of 1059 individual plants from 49 families of P. elliottii. Heritability, family ranking, genetic gain, and the relationship between these traits were estimated. The results showed that there was a significant negative genetic correlation between the holocellulose and lignin content. The heritabilities of these four traits were ranked from 0.18 to 0.32. The chemical wood traits did not show a strong correlation with diameter at breast height (DBH) and wood density. However, it is still possible to combine wood traits for selection. It was suggested that the genetic breeding selection could improve the growth and quality of P. elliottii.

scholarly journals Integration of a single-step genome-wide association study with a multi-tissue transcriptome analysis provides novel insights into the genetic basis of wool and weight traits in sheep

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Bingru Zhao ◽  
Hanpeng Luo ◽  
Xixia Huang ◽  
Chen Wei ◽  
Jiang Di ◽  
...  
Keyword(s):  
Association Study ◽  
Candidate Genes ◽  
Genetic Improvement ◽  
Genome Wide Association Study ◽  
Growth Traits ◽  
Fibre Diameter ◽  
Single Step ◽  
Genome Wide Association ◽  
Six Traits ◽  
Genome Wide

Abstract Background Genetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive. To improve our understanding of these mechanisms, we conducted a weighted single-step genome-wide association study (WssGWAS) and then integrated the results with large-scale transcriptome data for five wool traits and one growth trait in Merino sheep: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW), and live weight (LW). Results Our dataset comprised 7135 individuals with phenotype data, among which 1217 had high-density (HD) genotype data (n = 372,534). The genotypes of 707 of these animals were imputed from the Illumina Ovine single nucleotide polymorphism (SNP) 54 BeadChip to the HD Array. The heritability of these traits ranged from 0.05 (CVFD) to 0.36 (MFD), and between-trait genetic correlations ranged from − 0.44 (CN vs. LW) to 0.77 (GFW vs. LW). By integrating the GWAS signals with RNA-seq data from 500 samples (representing 87 tissue types from 16 animals), we detected tissues that were relevant to each of the six traits, e.g. liver, muscle and the gastrointestinal (GI) tract were the most relevant tissues for LW, and leukocytes and macrophages were the most relevant cells for CN. For the six traits, 54 quantitative trait loci (QTL) were identified covering 81 candidate genes on 21 ovine autosomes. Multiple candidate genes showed strong tissue-specific expression, e.g. BNC1 (associated with MFD) and CHRNB1 (LW) were specifically expressed in skin and muscle, respectively. By conducting phenome-wide association studies (PheWAS) in humans, we found that orthologues of several of these candidate genes were significantly (FDR < 0.05) associated with similar traits in humans, e.g. BNC1 was significantly associated with MFD in sheep and with hair colour in humans, and CHRNB1 was significantly associated with LW in sheep and with body mass index in humans. Conclusions Our findings provide novel insights into the biological and genetic mechanisms underlying wool and growth traits, and thus will contribute to the genetic improvement and gene mapping of complex traits in sheep.

scholarly journals Weighted Single-Step GWAS Identified Candidate Genes Associated with Growth Traits in a Duroc Pig Population

Genes ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 117
Author(s):  
Donglin Ruan ◽  
Zhanwei Zhuang ◽  
Rongrong Ding ◽  
Yibin Qiu ◽  
Shenping Zhou ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Bone Growth ◽  
Genome Wide Association Study ◽  
Growth Traits ◽  
Average Daily Gain ◽  
Snp Array ◽  
Single Step ◽  
Economic Traits ◽  
Association Analyses ◽  
Genealogical Information

Growth traits are important economic traits of pigs that are controlled by several major genes and multiple minor genes. To better understand the genetic architecture of growth traits, we performed a weighted single-step genome-wide association study (wssGWAS) to identify genomic regions and candidate genes that are associated with days to 100 kg (AGE), average daily gain (ADG), backfat thickness (BF) and lean meat percentage (LMP) in a Duroc pig population. In this study, 3945 individuals with phenotypic and genealogical information, of which 2084 pigs were genotyped with a 50 K single-nucleotide polymorphism (SNP) array, were used for association analyses. We found that the most significant regions explained 2.56–3.07% of genetic variance for four traits, and the detected significant regions (>1%) explained 17.07%, 18.59%, 23.87% and 21.94% for four traits. Finally, 21 genes that have been reported to be associated with metabolism, bone growth, and fat deposition were treated as candidate genes for growth traits in pigs. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses implied that the identified genes took part in bone formation, the immune system, and digestion. In conclusion, such full use of phenotypic, genotypic, and genealogical information will accelerate the genetic improvement of growth traits in pigs.

scholarly journals Ideas in Genomic Selection that Transformed Plant Molecular Breeding: A Review

2020 ◽  
Author(s):  
Matthew McGowan ◽  
Zhiwu Zhang ◽  
Jiabo Wang ◽  
Haixiao Dong ◽  
Xiaolei Liu ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Markers ◽  
Random Effects ◽  
Genome Wide Association Study ◽  
Single Step ◽  
Hybrid Breeding ◽  
Multiple Trait ◽  
Underlying Distribution ◽  
Genome Wide ◽  
Before And After

Estimation of breeding values through Best Linear Unbiased Prediction (BLUP) using pedigree-based kinship and Marker-Assisted Selection (MAS) are the two fundamental breeding methods used before and after the introduction of genetic markers, respectively. The emergence of high-density genome-wide markers has led to the development of two parallel series of approaches inspired by BLUP and MAS, which are collectively referred to as Genomic Selection (GS). The first series of GS methods alters pedigree-based BLUP by replacing pedigree-based kinship with marker-based kinship in a variety of ways, including weighting markers by their effects in genome-wide association study (GWAS), joining both pedigree and marker-based kinship together in a single-step BLUP, and substituting individuals with groups in a compressed BLUP. The second series of GS methods estimates the effects for all genetic markers simultaneously. For the second series methods, the marker effects are summed together regardless of their individual significance. Instead of fitting individuals as random effects like in the BLUP series, the second series fits markers as random effects. Differing assumptions regarding the underlying distribution of these marker effects have resulted in the development of many Bayesian-based GS methods. This review highlights critical concept developments for both of these series and explores ongoing GS developments in machine learning, multiple trait selection, and adaptation for hybrid breeding. Furthermore, considering the increasing use and variety of GS methods in plant breeding programs, this review addresses important concerns for future GS development and application, such as the use of GWAS-assisted GS, the long-term effectiveness of GS methods, and the valid assessment of prediction accuracy.

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