Project description:Plant architecture is central to yield and has been at the core of crop domestication and improvement. In cereals, inflorescence branching and leaf angle are important traits that contribute to planting density and yield potential. Several classical maize mutants show disruptions in both traits, suggesting a core regulatory network underlies pleiotropy between them. Here, we investigate regulatory modules that contribute to architectural pleiotropy between tassel branch number (TBN) and leaf angle (LA) in maize by defining transcriptional networks that function in lateral organ boundaries to promote development of these morphologically distinct organs. Using a set of nine mutants with specific developmental defects in one or both traits, we generated dynamic, context-specific gene regulatory maps that describe ligule and tassel branch development at the molecular level. Mutants introgressed into B73 and control plants were grown in environmentally controlled chambers and precisely-staged tassel primordia were hand-dissected at two stages: right before and after first primary branches initiated. Two stages capturing early development of the ligular region, including the shoot apical meristem, were also collected from mutants with LA defects. RNA-seq was performed on 140 samples and integrated into gene regulatory and co-expression networks, which were extended to include publicly available transcription factor occupancy maps for important developmental regulators, chromatin accessibility maps and natural variation to help prioritize novel genes and regulatory elements underlying diversity in LA and tassel branching phenotypes. We also used these transcriptional networks to guide multi-trait genome-wide association studies (GWAS) based on three years of field phenotyping TBN and LA traits in over 500 diverse maize lines. Various network-assisted GWAS approaches were used to identify polymorphisms in candidate genes that associate with these architecture traits and the pleiotropy between them. Our data provide novel insight into regulatory mechanisms controlling architectural pleiotropy that can be used for targeted crop improvement.

Project description:The data set submitted here contains the raw SNP genotyping data obtained from the analysis of 24 biparental segregating maize (Zea mays L.) populations and their respective parents. The processed and filtered data were used to construct genetic linkage maps which we used in our study of variation of recombination rate in maize. In sexually reproducing organisms, meiotic crossovers ensure the proper segregation of chromosomes and contribute to genetic diversity by shuffling allelic combinations. Such genetic reassortment is exploited in breeding to combine favorable alleles, and in genetic research to identify genetic factors underlying traits of interest via linkage or association-based approaches. Crossover numbers and distributions along chromosomes vary between species, but little is known about their intraspecies variation. In our study, we report on the variation of recombination rates between 22 European maize inbred lines that belong to the Dent and Flint gene pools. We genotyped 23 doubled-haploid populations derived from crosses between these lines with a 50k-SNP array and constructed high-density genetic maps, showing good correspondence with the maize B73 genome sequence assembly. By aligning each genetic map to the B73 sequence, we obtained the recombination rates along chromosomes specific to each population. We identified significant differences in recombination rates at the genome-wide, chromosome, and intrachromosomal levels between populations, as well as significant variation for genome-wide recombination rates among maize lines. Crossover interference analysis using a two-pathway modeling framework revealed a negative association between recombination rate and interference strength. To our knowledge, the present work provides the most comprehensive study on intraspecific variation of recombination rates and crossover interference strength in eukaryotes. Differences found in recombination rates will allow for selection of high or low recombining lines in crossing programs. Our methodology should pave the way for precise identification of genes controlling recombination rates in maize and other organisms.

Project description:Alpine goat phenotypes for quality components have been routinely recorded for many years and deposited in the Council on Dairy Cattle Breeding (CDCB) repository. The data collected were used to conduct an exploratory genome-wide association study (GWAS) from 72 female Alpine goats originating from locations throughout the U.S. Genotypes were identified with the Illumina Goat 50K single nucleotide polymorphisms (SNP) Beadchip. The analysis used a polygenic model where the dropping criteria was the Call Rate ≥ 0.95. The initial dataset was composed of ~ 60,000 rows of SNPs, 21 columns of phenotypic traits and composed of 53,384 scaffolds containing other informative data points used for genomic predictive power. Phenotypic association with the 50KBeadchip revealed 26,074 reads of candidate genes. These candidate genes segregated as separate novel SNPs and were identified as statistically significant regions for genome and chromosome level trait associations. Candidate genes associated differently for each of the following phenotypic traits: test day milk yield (13,469 candidate genes), test day protein yield (25,690 candidate genes), test day fat yield (25,690 candidate genes), percentage protein (25,690 candidate genes), percentage fat (25,690 candidate genes), and percentage lactose content (25,690 candidate genes). The outcome of this study supports elucidation of novel genes that are important for livestock species in association to key phenotypic traits. Validation towards the development of marker-based selection that provide precision breeding methods will thereby increase breeding value. Specific aims: 1) Improve on contributions to the phenotype repository, the Council on Dairy Cattle Breeding (CDCB) for milk quality traits that are economically important for goat production while developing a corresponding DNA repository for each of the animals with significant genotype-phenotype associations. 2) Develop genomic prediction tools and provide data for a better database for tools to predict phenotypic traits by initially using the high density Goat50KSNP BeadChip for the selection of more specific SNPs associated with select signatures (genes) for phenotypic traits in American Alpine goats. 3) To establish whether a low number of goat subjects (< 300 goats) will provide statistically significant (p < 0.05) predictive capabilities for desired breeding traits in American Alpine dairy goats.

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