scholarly journals A Nested Association Mapping Panel in Arabidopsis thaliana for Mapping and Characterizing Genetic Architecture

2020 ◽  
Vol 10 (10) ◽  
pp. 3701-3708 ◽  
Author(s):  
Marcus T. Brock ◽  
Matthew J. Rubin ◽  
Dean DellaPenna ◽  
Cynthia Weinig
Keyword(s):  
Arabidopsis Thaliana ◽  
Association Mapping ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Recurrent Parent ◽  
Linkage Maps ◽  
Nucleotide Polymorphisms ◽  
Nested Association Mapping ◽  
Mapping Panel ◽  
Genotype By Sequencing

Linkage and association mapping populations are crucial public resources that facilitate the characterization of trait genetic architecture in natural and agricultural systems. We define a large nested association mapping panel (NAM) from 14 publicly available recombinant inbred line populations (RILs) of Arabidopsis thaliana, which share a common recurrent parent (Col-0). Using a genotype-by-sequencing approach (GBS), we identified single nucleotide polymorphisms (SNPs; range 563-1525 per population) and subsequently built updated linkage maps in each of the 14 RIL sets. Simulations in individual RIL populations indicate that our GBS markers have improved power to detect small effect QTL and enhanced resolution of QTL support intervals in comparison to original linkage maps. Using these robust linkage maps, we imputed a common set of publicly available parental SNPs into each RIL linkage map, generating overlapping markers across all populations. Though ultimately depending on allele frequencies at causal loci, simulations of the NAM panel suggest that surveying between 4 to 7 of the 14 RIL populations provides high resolution of the genetic architecture of complex traits, relative to a single mapping population.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

scholarly journals The Genetic Architecture of Complex Traits in Teosinte (Zea mays ssp. parviglumis): New Evidence From Association Mapping

Genetics ◽  
2008 ◽  
Vol 180 (2) ◽  
pp. 1221-1232 ◽  
Author(s):  
Allison L. Weber ◽  
William H. Briggs ◽  
Jesse Rucker ◽  
Baltazar M. Baltazar ◽  
José de Jesús Sánchez-Gonzalez ◽  
...  
Keyword(s):  
Zea Mays ◽  
Association Mapping ◽  
Complex Traits ◽  
Genetic Architecture ◽  
New Evidence

scholarly journals Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize

PLoS Genetics ◽  
2021 ◽  
Vol 17 (10) ◽  
pp. e1009568
Author(s):  
Anju Giri ◽  
Merritt Khaipho-Burch ◽  
Edward S. Buckler ◽  
Guillaume P. Ramstein
Keyword(s):  
Genomic Prediction ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Cost Effective ◽  
Rna Expression ◽  
Haplotype Structure ◽  
Association Panel ◽  
Nested Association Mapping ◽  
Mapping Panel

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.

scholarly journals TeoNAM: a nested association mapping population for domestication and agronomic trait analysis in maize

2019 ◽  
Author(s):  
Qiuyue Chen ◽  
Chin Jian Yang ◽  
Alessandra M. York ◽  
Wei Xue ◽  
Lora L. Daskalska ◽  
...  
Keyword(s):  
Association Mapping ◽  
Complex Traits ◽  
Agronomic Traits ◽  
Mapping Population ◽  
Allelic Variation ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Nested Association Mapping ◽  
Nested Association Mapping Population ◽  
Association Mapping Population

AbstractRecombinant inbred lines (RILs) are an important resource for mapping genes controlling complex traits in many species. While RIL populations have been developed for maize, a maize RIL population with multiple teosinte inbred lines as parents has been lacking. Here, we report a teosinte nested association mapping population (TeoNAM), derived from crossing five teosinte inbreds to the maize inbred line W22. The resulting 1257 BC1S4 RILs were genotyped with 51,544 SNPs, providing a high-density genetic map with a length of 1540 cM. On average, each RIL is 15% homozygous teosinte and 8% heterozygous. We performed joint linkage mapping (JLM) and genome-wide association study (GWAS) for 22 domestication and agronomic traits. A total of 255 QTLs from JLM were identified with many of these mapping to known genes or novel candidate genes. TeoNAM is a useful resource for QTL mapping for the discovery of novel allelic variation from teosinte. TeoNAM provides the first report that PROSTRATE GROWTH1, a rice domestication gene, is also a QTL associated with tillering in teosinte and maize. We detected multiple QTLs for flowering time and other traits for which the teosinte allele contributes to a more maize-like phenotype. Such QTL could be valuable in maize improvement.

scholarly journals Dissecting adaptive traits with nested association mapping: Genetic architecture of inflorescence morphology in sorghum

2019 ◽  
Author(s):  
Marcus O. Olatoye ◽  
Sandeep R. Marla ◽  
Zhenbin Hu ◽  
Sophie Bouchet ◽  
Ramasamy Perumal ◽  
...  
Keyword(s):  
Association Mapping ◽  
Genetic Architecture ◽  
Regulatory Networks ◽  
Allelic Variation ◽  
Recombinant Inbred Lines ◽  
Branch Length ◽  
Lower Branch ◽  
Nested Association Mapping ◽  
Inflorescence Morphology ◽  
Significant Enrichment

ABSTRACTIn the cereal crop sorghum (Sorghum bicolor) inflorescence morphology variation underlies yield variation and confers adaptation across precipitation gradients, but its genetic basis is poorly understood. Here we characterized the genetic architecture of sorghum inflorescence morphology using a global nested association mapping (NAM) population (2200 recombinant inbred lines) and 198,000 phenotypic observations from multi-environment trials for four inflorescence morphology traits (upper branch length, lower branch length, rachis length, and rachis diameter). Trait correlations suggest that lower and upper branch length are under largely independent genetic control, while lower branch length and rachis diameter are pleiotropic. Joint linkage and genome-wide association mapping revealed an oligogenic architecture with 1–22 QTL per trait, each explaining 0.1%–5.0% of variation across the entire NAM population. Overall, there is a significant enrichment (2.4-fold) of QTL colocalizing with homologs of grass inflorescence genes, notably with orthologs of maize (Ramosa2) and rice (Aberrant Panicle Organization1, TAWAWA1) inflorescence regulators. In global georeferenced germplasm, allelic variation at the major inflorescence morphology QTL is significantly associated with precipitation gradients, consistent with a role for these QTL in adaptation to agroclimatic zones. The findings suggest that global inflorescence diversity in sorghum is largely controlled by oligogenic, epistatic, and pleiotropic variation in ancestral regulatory networks. This genotype-phenotype trait dissection in global germplasm provides a basis for genomics-enabled breeding of locally-adapted inflorescence morphology.

scholarly journals A high-resolution association mapping panel for the dissection of complex traits in mice

2010 ◽  
Vol 20 (2) ◽  
pp. 281-290 ◽  
Author(s):  
B. J. Bennett ◽  
C. R. Farber ◽  
L. Orozco ◽  
H. Min Kang ◽  
A. Ghazalpour ◽  
...  
Keyword(s):  
High Resolution ◽  
Association Mapping ◽  
Complex Traits ◽  
Mapping Panel

scholarly journals Modelling the genetic architecture of flowering time control in barley through nested association mapping

BMC Genomics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Andreas Maurer ◽  
Vera Draba ◽  
Yong Jiang ◽  
Florian Schnaithmann ◽  
Rajiv Sharma ◽  
...  
Keyword(s):  
Association Mapping ◽  
Flowering Time ◽  
Genetic Architecture ◽  
Nested Association Mapping ◽  
Time Control ◽  
Flowering Time Control

scholarly journals Polygenicity and epistasis underlie fitness-proximal traits in the Caenorhabditis elegans multiparental experimental evolution (CeMEE) panel

2017 ◽  
Author(s):  
Luke M. Noble ◽  
Ivo Chelo ◽  
Thiago Guzella ◽  
Bruno Afonso ◽  
David D. Riccardi ◽  
...  
Keyword(s):  
Caenorhabditis Elegans ◽  
Experimental Evolution ◽  
Complex Traits ◽  
Genetic Basis ◽  
Natural Populations ◽  
Phenotypic Variance ◽  
Nucleotide Polymorphisms ◽  
C Elegans ◽  
Mapping Panel ◽  
Trait Locus

ABSTRACTUnderstanding the genetic basis of complex traits remains a major challenge in biology. Polygenicity, phenotypic plasticity and epistasis contribute to phenotypic variance in ways that are rarely clear. This uncertainty is problematic for estimating heritability, for predicting individual phenotypes from genomic data, and for parameterizing models of phenotypic evolution. Here we report a recombinant inbred line (RIL) quantitative trait locus (QTL) mapping panel for the hermaphroditic nematode Caenorhabditis elegans, the C. elegans multiparental experimental evolution (CeMEE) panel. The CeMEE panel, comprising 507 RILs, was created by hybridization of 16 wild isolates, experimental evolution at moderate population sizes and predominant outcrossing for 140-190 generations, and inbreeding by selfing for 13-16 generations. The panel contains 22% of single nucleotide polymorphisms known to segregate in natural populations, and complements existing mapping resources for C. elegans by providing high nucleotide diversity across >95% of the genome. We apply it to study the genetic basis of two fitness components, fertility and hermaphrodite body size at time of reproduction, with high broad sense heritability in the CeMEE. While simulations show we should detect common alleles with additive effects as small as 5%, at gene-level resolution, the genetic architectures of these traits does not feature such alleles. We instead find that a significant fraction of trait variance, particularly for fertility, can be explained by sign epistasis with weak main effects. In congruence, phenotype prediction, while generally poor (r2 < 10%), requires modeling epistasis for optimal accuracy, with most variance attributed to the highly recombinant, rapidly evolving chromosome arms.

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