Mapping and Predicting Non-LinearBrassica rapaGrowth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation

Abstract Key message Through substitution mapping strategy, two pairs of closely linked QTLs controlling stigma exsertion rate were dissected from chromosomes 2 and 3 and the four QTLs were fine mapped. Abstract Stigma exsertion rate (SER) is an important trait affecting the outcrossing ability of male sterility lines in hybrid rice. This complex trait was controlled by multiple QTLs and affected by environment condition. Here, we dissected, respectively, two pairs of tightly linked QTLs for SER on chromosomes 2 and 3 by substitution mapping. On chromosome 2, two linkage QTLs, qSER-2a and qSER-2b, were located in the region of 1288.0 kb, and were, respectively, delimited to the intervals of 234.9 kb and 214.3 kb. On chromosome 3, two QTLs, qSER-3a and qSER-3b, were detected in the region of 3575.5 kb and were narrowed down to 319.1 kb and 637.3 kb, respectively. The additive effects of four QTLs ranged from 7.9 to 9.0%. The epistatic effect produced by the interaction of qSER-2a and qSER-2b was much greater than that of qSER-3a and qSER-3b. The open reading frames were identified within the maximum intervals of qSER-2a, qSER-2b and qSER-3a, respectively. These results revealed that there are potential QTL clusters for SER in the two regions of chromosome 2 and chromosome 3. Fine mapping of the QTLs laid a foundation for cloning of the genes of SER.

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Deconstructing and reconstructing joint hypermobility on an evo-devo perspective

Rheumatology ◽

10.1093/rheumatology/keab196 ◽

2021 ◽

Author(s):

Marco Castori

Keyword(s):

Evolutionary Biology ◽

Phenotypic Variability ◽

Morphological Variability ◽

Complex Trait ◽

Joint Hypermobility ◽

Integration Model ◽

Ehlers Danlos Syndrome ◽

Evo Devo ◽

Derivatives Of ◽

Continuous Traits

Abstract Joint hypermobility is a common characteristic in humans. Its non-casual association with various musculoskeletal complaints is known and currently defined “the spectrum”. It includes hypermobile Ehlers–Danlos syndrome (hEDS) and hypermobility spectrum disorders (HSD). hEDS is recognized by a set of descriptive criteria, while HSD is the background diagnosis for individuals not fulfilling these criteria. Little is known about the aetiopathogenesis of the spectrum. It may be interpreted as a complex trait according to the integration model. Particularly, the spectrum is common in the general population, affects morphology, presents extreme clinical variability and is characterized by marked sex bias without a clear Mendelian or hormonal explanation. Joint hypermobility and the other hEDS systemic criteria are intended as qualitative derivatives of continuous traits of normal morphological variability. The need for a minimum set of criteria for hEDS diagnosis implies a tendency to co-vary of these underlying continuous traits. In evolutionary biology, such a co-variation (i.e. integration) is driven by multiple forces, including genetic, developmental, functional and environmental/acquired interactors. The aetiopathogenesis of the spectrum may be resolved by a deeper understanding of phenotypic variability, which superimposes on normal morphological variability.

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Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different GWAS models

Scientific Reports ◽

10.1038/s41598-021-86127-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ali Muhammad ◽

Jianguo Li ◽

Weichen Hu ◽

Jinsheng Yu ◽

Shahid Ullah Khan ◽

...

Keyword(s):

Plant Height ◽

Genome Wide Association Study ◽

Flag Leaf ◽

Significant Snps ◽

Complex Trait ◽

Leaf Length ◽

Candidate Snps ◽

A Genome ◽

Multiple Environments ◽

Architectural Traits

AbstractWheat is a major food crop worldwide. The plant architecture is a complex trait mostly influenced by plant height, tiller number, and leaf morphology. Plant height plays a crucial role in lodging and thus affects yield and grain quality. In this study, a wheat population was genotyped by using Illumina iSelect 90K single nucleotide polymorphism (SNP) assay and finally 22,905 high-quality SNPs were used to perform a genome-wide association study (GWAS) for plant architectural traits employing four multi-locus GWAS (ML-GWAS) and three single-locus GWAS (SL-GWAS) models. As a result, 174 and 97 significant SNPs controlling plant architectural traits were detected by ML-GWAS and SL-GWAS methods, respectively. Among these SNP makers, 43 SNPs were consistently detected, including seven across multiple environments and 36 across multiple methods. Interestingly, five SNPs (Kukri_c34553_89, RAC875_c8121_1490, wsnp_Ex_rep_c66315_64480362, Ku_c5191_340, and tplb0049a09_1302) consistently detected across multiple environments and methods, played a role in modulating both plant height and flag leaf length. Furthermore, candidate SNPs (BS00068592_51, Kukri_c4750_452 and BS00022127_51) constantly repeated in different years and methods associated with flag leaf width and number of tillers. We also detected several SNPs (Jagger_c6772_80, RAC875_c8121_1490, BS00089954_51, Excalibur_01167_1207, and Ku_c5191_340) having common associations with more than one trait across multiple environments. By further appraising these GWAS methods, the pLARmEB and FarmCPU models outperformed in SNP detection compared to the other ML-GWAS and SL-GWAS methods, respectively. Totally, 152 candidate genes were found to be likely involved in plant growth and development. These finding will be helpful for better understanding of the genetic mechanism of architectural traits in wheat.

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Functional-structural plant modeling to support complex trait phenotyping: Case of rice early vigour and drought tolerance using ecomeristem model

2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications ◽

10.1109/pma.2012.6524845 ◽

2012 ◽

Cited By ~ 6

Author(s):

Delphine Luquet ◽

Maria Camila Rebolledo ◽

Jean-Christophe Soulie

Keyword(s):

Drought Tolerance ◽

Complex Trait ◽

Plant Modeling ◽

Early Vigour

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CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

Nucleic Acids Research ◽

10.1093/nar/gkz1026 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jianhua Wang ◽

Dandan Huang ◽

Yao Zhou ◽

Hongcheng Yao ◽

Huanhuan Liu ◽

...

Keyword(s):

Fine Mapping ◽

Genetic Variants ◽

Association Studies ◽

Complex Trait ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Genome Wide ◽

Credible Sets ◽

Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

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Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401847 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4599-4613

Author(s):

Fabio Morgante ◽

Wen Huang ◽

Peter Sørensen ◽

Christian Maltecca ◽

Trudy F. C. Mackay

Keyword(s):

Precision Agriculture ◽

Complex Traits ◽

Prediction Accuracy ◽

Cellular Response ◽

Complex Trait ◽

Sources Of Information ◽

Starvation Resistance ◽

Expression Levels ◽

Chill Coma ◽

Additional Layer

The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

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