Publications by authors named "Maria Cinta Romay"

4 Publications

  • Page 1 of 1

The genetic architecture of the maize progenitor, teosinte, and how it was altered during maize domestication.

PLoS Genet 2020 05 14;16(5):e1008791. Epub 2020 May 14.

Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

The genetics of domestication has been extensively studied ever since the rediscovery of Mendel's law of inheritance and much has been learned about the genetic control of trait differences between crops and their ancestors. Here, we ask how domestication has altered genetic architecture by comparing the genetic architecture of 18 domestication traits in maize and its ancestor teosinte using matched populations. We observed a strongly reduced number of QTL for domestication traits in maize relative to teosinte, which is consistent with the previously reported depletion of additive variance by selection during domestication. We also observed more dominance in maize than teosinte, likely a consequence of selective removal of additive variants. We observed that large effect QTL have low minor allele frequency (MAF) in both maize and teosinte. Regions of the genome that are strongly differentiated between teosinte and maize (high FST) explain less quantitative variation in maize than teosinte, suggesting that, in these regions, allelic variants were brought to (or near) fixation during domestication. We also observed that genomic regions of high recombination explain a disproportionately large proportion of heritable variance both before and after domestication. Finally, we observed that about 75% of the additive variance in both teosinte and maize is "missing" in the sense that it cannot be ascribed to detectable QTL and only 25% of variance maps to specific QTL. This latter result suggests that morphological evolution during domestication is largely attributable to very large numbers of QTL of very small effect.
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http://dx.doi.org/10.1371/journal.pgen.1008791DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266358PMC
May 2020

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

BMC Res Notes 2020 Feb 12;13(1):71. Epub 2020 Feb 12.

Texas A&M University, College Station, TX, 77843, USA.

Objectives: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017.

Data Description: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.
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http://dx.doi.org/10.1186/s13104-020-4922-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017475PMC
February 2020

The genetic architecture of teosinte catalyzed and constrained maize domestication.

Proc Natl Acad Sci U S A 2019 03 6;116(12):5643-5652. Epub 2019 Mar 6.

Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706;

The process of evolution under domestication has been studied using phylogenetics, population genetics-genomics, quantitative trait locus (QTL) mapping, gene expression assays, and archaeology. Here, we apply an evolutionary quantitative genetic approach to understand the constraints imposed by the genetic architecture of trait variation in teosinte, the wild ancestor of maize, and the consequences of domestication on genetic architecture. Using modern teosinte and maize landrace populations as proxies for the ancestor and domesticate, respectively, we estimated heritabilities, additive and dominance genetic variances, genetic-by-environment variances, genetic correlations, and genetic covariances for 18 domestication-related traits using realized genomic relationships estimated from genome-wide markers. We found a reduction in heritabilities across most traits, and the reduction is stronger in reproductive traits (size and numbers of grains and ears) than vegetative traits. We observed larger depletion in additive genetic variance than dominance genetic variance. Selection intensities during domestication were weak for all traits, with reproductive traits showing the highest values. For 17 of 18 traits, neutral divergence is rejected, suggesting they were targets of selection during domestication. Yield (total grain weight) per plant is the sole trait that selection does not appear to have improved in maize relative to teosinte. From a multivariate evolution perspective, we identified a strong, nonneutral divergence between teosinte and maize landrace genetic variance-covariance matrices (G-matrices). While the structure of G-matrix in teosinte posed considerable genetic constraint on early domestication, the maize landrace G-matrix indicates that the degree of constraint is more unfavorable for further evolution along the same trajectory.
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http://dx.doi.org/10.1073/pnas.1820997116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6431195PMC
March 2019

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets.

BMC Res Notes 2018 Jul 9;11(1):452. Epub 2018 Jul 9.

University of Nebraska, Lincoln, NE, 68583, USA.

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available.

Data Description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.
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http://dx.doi.org/10.1186/s13104-018-3508-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6038255PMC
July 2018