The relevance of dominance and functional annotations to predict agronomic traits in hybrid maize

Heterosis has been key to the development of maize breeding but describing its genetic basis has been challenging. Previous studies of heterosis have shown the contribution of within-locus complementation effects (dominance) and their differential importance across genomic regions. However, they have generally considered panels of limited genetic diversity and have shown little benefit to including dominance effects for predicting genotypic value in breeding populations. This study examined within-locus complementation and enrichment of genetic effects by functional classes in maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the United States (1,106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1,640 hybrids). We assayed three agronomic traits: days to silking (DTS), plant height (PH) and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results point to mechanisms by which heterosis arises through local complementation in proximal gene regions and suggest the relevance of dominance and gene annotations for genomic prediction in maize.

[1]  ScienceOpen Admin Genomic Prediction , 2019 .

[2]  Alain Charcosset,et al.  Genomic prediction of maize yield across European environmental conditions , 2019, Nature Genetics.

[3]  Nathan M. Springer,et al.  Dynamic Patterns of Gene Expression Additivity and Regulatory Variation throughout Maize Development. , 2019, Molecular plant.

[4]  M. Sorrells,et al.  Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid , 2019, Genetics.

[5]  C. Buell,et al.  Candidate Variants for Additive and Interactive Effects on Bioenergy Traits in Switchgrass (Panicum virgatum L.) Identified by Genome‐Wide Association Analyses , 2018, The plant genome.

[6]  Brian L Browning,et al.  A One-Penny Imputed Genome from Next-Generation Reference Panels. , 2018, American journal of human genetics.

[7]  Brian L. Browning,et al.  A one penny imputed genome from next generation reference panels , 2018, bioRxiv.

[8]  Xin Li,et al.  Genomic and environmental determinants and their interplay underlying phenotypic plasticity , 2018, Proceedings of the National Academy of Sciences.

[9]  L. Lukens,et al.  Distinct gene networks modulate floral induction of autonomous maize and photoperiod-dependent teosinte , 2018, Journal of experimental botany.

[10]  Peter J. Bradbury,et al.  Dysregulation of expression correlates with rare-allele burden and fitness loss in maize , 2018, Nature.

[11]  Jian Wang,et al.  SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data , 2017, GigaScience.

[12]  A. Charcosset,et al.  Reciprocal Genetics: Identifying QTL for General and Specific Combining Abilities in Hybrids Between Multiparental Populations from Two Maize (Zea mays L.) Heterotic Groups , 2017, Genetics.

[13]  Edward S. Buckler,et al.  Genetic Analysis of Lodging in Diverse Maize Hybrids , 2017, bioRxiv.

[14]  Rita H. Mumm,et al.  Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize , 2016, bioRxiv.

[15]  Zhe Zhang,et al.  Incorporating Gene Annotation into Genomic Prediction of Complex Phenotypes , 2017, Genetics.

[16]  遊也 石田,et al.  Augmented Implicitly Restarted Lanczos Bidiagonalization法の改良 , 2017 .

[17]  Rasool Tahmasbi,et al.  Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits , 2017, Nature Genetics.

[18]  M. Hufford,et al.  The interplay of demography and selection during maize domestication and expansion , 2017, Genome Biology.

[19]  Ning Gao,et al.  Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE) , 2017, BMC Bioinformatics.

[20]  Waqas Ahmed Malik,et al.  Stability of Single-Parent Gene Expression Complementation in Maize Hybrids upon Water Deficit Stress1[OPEN] , 2016, Plant Physiology.

[21]  M. Bohn,et al.  Genomic Prediction of Single Crosses in the Early Stages of a Maize Hybrid Breeding Pipeline , 2016, G3: Genes, Genomes, Genetics.

[22]  Daniel L. Vera,et al.  Open chromatin reveals the functional maize genome , 2016, Proceedings of the National Academy of Sciences.

[23]  Henner Simianer,et al.  Epistasis and covariance: how gene interaction translates into genomic relationship , 2016, Theoretical and Applied Genetics.

[24]  D. Akdemir,et al.  Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement , 2016, Heredity.

[25]  Timothy M. Beissinger,et al.  Recent demography drives changes in linked selection across the maize genome , 2015, Nature Plants.

[26]  Yingrui Li,et al.  Construction of the third-generation Zea mays haplotype map , 2015, bioRxiv.

[27]  S. Salvi,et al.  Yield QTLome distribution correlates with gene density in maize. , 2016, Plant science : an international journal of experimental plant biology.

[28]  Jochen C Reif,et al.  Modeling Epistasis in Genomic Selection , 2015, Genetics.

[29]  C. Jung,et al.  Flowering time regulation in crops—what did we learn from Arabidopsis? , 2015, Current opinion in biotechnology.

[30]  Peter J. Bradbury,et al.  Recombination in diverse maize is stable, predictable, and associated with genetic load , 2015, Proceedings of the National Academy of Sciences.

[31]  Katherine E. Guill,et al.  The Genomic Impacts of Drift and Selection for Hybrid Performance in Maize , 2013, Genetics.

[32]  Peter J. Bradbury,et al.  Association Mapping across Numerous Traits Reveals Patterns of Functional Variation in Maize , 2014, bioRxiv.

[33]  G. Abecasis,et al.  Rare-variant association analysis: study designs and statistical tests. , 2014, American journal of human genetics.

[34]  W. G. Hill,et al.  Influence of Gene Interaction on Complex Trait Variation with Multilocus Models , 2014, Genetics.

[35]  Eva Bauer,et al.  Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize , 2014, Genetics.

[36]  Jing Wang,et al.  CrossMap: a versatile tool for coordinate conversion between genome assemblies , 2014, Bioinform..

[37]  Robert J. Elshire,et al.  TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis Pipeline , 2014, PloS one.

[38]  H. Pospisil,et al.  Genome-wide meta-analysis of maize heterosis reveals the potential role of additive gene expression at pericentromeric loci , 2014, BMC Plant Biology.

[39]  José Crossa,et al.  A reaction norm model for genomic selection using high-dimensional genomic and environmental data , 2013, Theoretical and Applied Genetics.

[40]  Xiaohong Yang,et al.  CACTA-like transposable element in ZmCCT attenuated photoperiod sensitivity and accelerated the postdomestication spread of maize , 2013, Proceedings of the National Academy of Sciences.

[41]  Robert J. Elshire,et al.  Comprehensive genotyping of the USA national maize inbred seed bank , 2013, Genome Biology.

[42]  Nathan M. Springer,et al.  Progress toward understanding heterosis in crop plants. , 2013, Annual review of plant biology.

[43]  M. Slatkin,et al.  An Introduction to Population Genetics: Theory and Applications , 2013 .

[44]  Douglas M. Bates,et al.  Fast and Elegant Numerical Linear Algebra Using the RcppEigen Package , 2013 .

[45]  Xiang Zhou,et al.  Polygenic Modeling with Bayesian Sparse Linear Mixed Models , 2012, PLoS genetics.

[46]  C. Davis The pattern and distribution of deleterious mutations in maize , 2013 .

[47]  Steven P. Lund,et al.  Complementation contributes to transcriptome complexity in maize (Zea mays L.) hybrids relative to their inbred parents , 2012, Genome research.

[48]  Jean-Luc Jannink,et al.  Shrinkage Estimation of the Realized Relationship Matrix , 2012, G3: Genes | Genomes | Genetics.

[49]  M. Stephens,et al.  Genome-wide Efficient Mixed Model Analysis for Association Studies , 2012, Nature Genetics.

[50]  P. This,et al.  Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness , 2011, Heredity.

[51]  B. Mangin,et al.  The Genetic Basis of Heterosis: Multiparental Quantitative Trait Loci Mapping Reveals Contrasted Levels of Apparent Overdominance Among Traits of Agronomical Interest in Maize (Zea mays L.) , 2012, Genetics.

[52]  M. Stephens,et al.  Bayesian variable selection regression for genome-wide association studies and other large-scale problems , 2011, 1110.6019.

[53]  Dale R Nyholt,et al.  Association mapping. , 2011, Methods in molecular biology.

[54]  Serafim Batzoglou,et al.  Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++ , 2010, PLoS Comput. Biol..

[55]  R. Veitia,et al.  Heterosis , 2010, Plant Cell.

[56]  James A. Birchler,et al.  The gene balance hypothesis: implications for gene regulation, quantitative traits and evolution. , 2010, The New phytologist.

[57]  H. Kang,et al.  Variance component model to account for sample structure in genome-wide association studies , 2010, Nature Genetics.

[58]  M. McMullen,et al.  Genetic Properties of the Maize Nested Association Mapping Population , 2009, Science.

[59]  B. Browning,et al.  A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. , 2009, American journal of human genetics.

[60]  P. VanRaden,et al.  Efficient methods to compute genomic predictions. , 2008, Journal of dairy science.

[61]  Edward S. Buckler,et al.  TASSEL: software for association mapping of complex traits in diverse samples , 2007, Bioinform..

[62]  M. Morgante,et al.  Classical Genetic and Quantitative Trait Loci Analyses of Heterosis in a Maize Hybrid Between Two Elite Inbred Lines , 2007, Genetics.

[63]  Nathan M. Springer,et al.  Allelic variation and heterosis in maize: how do two halves make more than a whole? , 2007, Genome research.

[64]  Jianbing Yan,et al.  Epistatic interaction is an important genetic basis of grain yield and its components in maize , 2007, Molecular Breeding.

[65]  Nathan M. Springer,et al.  Cis-transcriptional Variation in Maize Inbred Lines B73 and Mo17 Leads to Additive Expression Patterns in the F1 Hybrid , 2006, Genetics.

[66]  Dan Nettleton,et al.  All possible modes of gene action are observed in a global comparison of gene expression in a maize F1 hybrid and its inbred parents. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[67]  G. Shull Duplicate genes for capsule-form inBursa bursa-pastoris , 2018, Zeitschrift für induktive Abstammungs- und Vererbungslehre.

[68]  John Doebley,et al.  Maize association population: a high-resolution platform for quantitative trait locus dissection. , 2005, The Plant journal : for cell and molecular biology.

[69]  A. Melchinger,et al.  No evidence for epistasis in hybrid and per se performance of elite european flint maize inbreds from generation means and QTL analyses , 2005 .

[70]  Lothar Reichel,et al.  Augmented Implicitly Restarted Lanczos Bidiagonalization Methods , 2005, SIAM J. Sci. Comput..

[71]  J. Reif,et al.  HETEROSIS AND HETEROTIC PATTERNS IN MAIZE , 2005 .

[72]  J. Reif,et al.  Genetic distance based on simple sequence Repeats and Heterosis in Tropical Maize Populations , 2003 .

[73]  R. Stoughton,et al.  Genetics of gene expression surveyed in maize, mouse and man , 2003, Nature.

[74]  S. Wood Thin plate regression splines , 2003 .

[75]  K. Lamkey,et al.  Absence of Epistasis for Grain Yield in Elite Maize Hybrids , 2003 .

[76]  Karl J. Friston,et al.  Variance Components , 2003 .

[77]  J. Birchler,et al.  Dosage-dependent gene regulation in multicellular eukaryotes: implications for dosage compensation, aneuploid syndromes, and quantitative traits. , 2001, Developmental biology.

[78]  J. Crow 90 years ago: the beginning of hybrid maize. , 1998, Genetics.

[79]  C. Stuber,et al.  Characterization of a Yield Quantitative Trait Locus on Chromosome Five of Maize by Fine Mapping , 1997 .

[80]  C. Cockerham,et al.  Design III with marker loci. , 1996, Genetics.

[81]  M. Lynch,et al.  Genetics and Analysis of Quantitative Traits , 1996 .

[82]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[83]  M. Kimura,et al.  An introduction to population genetics theory , 1971 .

[84]  W. G. Hill,et al.  The effect of linkage on limits to artificial selection. , 1966, Genetical research.

[85]  H. F. Robinson,et al.  Estimates of Genetic Variances and Level of Dominance in Maize. , 1964, Genetics.

[86]  Cedric A. B. Smith,et al.  Introduction to Quantitative Genetics , 1960 .

[87]  H. Grüneberg,et al.  Introduction to quantitative genetics , 1960 .

[88]  J. Crow Alternative Hypotheses of Hybrid Vigor. , 1948, Genetics.

[89]  G. Shull The composition of a field of maize , 1908 .