Genome-Wide Association Studies of Grain Yield Components in Diverse Sorghum Germplasm

Citation: Boyles, R. E., Cooper, E. A., Myers, M. T., Brenton, Z., Rauh, B. L., Morris, G. P., & Kresovich, S. (2016). Genome-Wide Association Studies of Grain Yield Components in Diverse Sorghum Germplasm. Plant Genome, 9(2), 17. doi:10.3835/plantgenome2015.09.0091Grain yield and its primary determinants, grain number and weight, are important traits in cereal crops that have been well studied; however, the genetic basis of and interactions between these traits remain poorly understood. Characterization of grain yield per primary panicle (YPP), grain number per primary panicle (GNP), and 1000-grain weight (TGW) in sorghum [Sorghum bicolor (L.) Moench], a hardy C-4 cereal with a genome size of similar to 730 Mb, was implemented in a diversity panel containing 390 accessions. These accessions were genotyped to obtain 268,830 single-nucleotide polymorphisms (SNPs). Genome-wide association studies (GWAS) were performed to identify loci associated with each grain yield component and understand the genetic interactions between these traits. Genome-wide association studies identified associations across the genome with YPP, GNP, and TGW that were located within previously mapped sorghum QTL for panicle weight, grain yield, and seed size, respectively. There were no significant associations between GNP and TGW that were within 100 kb, much greater than the average linkage disequilibrium (LD) in sorghum. The identification of nonoverlapping loci for grain number and weight suggests these traits may be manipulated independently to increase the grain yield of sorghum. Following GWAS, genomic regions surrounding each associated SNP were mined for candidate genes. Previously published expression data indicated several TGW candidate genes, including an ethylene receptor homolog, were primarily expressed within developing seed tissues to support GWAS. Furthermore, maize (Zea mays L.) homologs of identified TGW candidates were differentially expressed within the seed between small- and large-kernel lines from a segregating maize population

Modeling multiple risks during infancy to predict quality of the caregiving environment: Contributions of a person-centered approach

► Nine risk factors for poor quality caregiving environment explored using four methods. ► Compared bivariate approach, regression, cumulative risk, latent class analysis. ► Five risk classes identified, from married low-risk to single low-income/education. ► Latent class analysis provided more intuitive and useful summary of multiple risks. The primary goal of this study was to compare several variable-centered and person-centered methods for modeling multiple risk factors during infancy to predict the quality of caregiving environments at six months of age. Nine risk factors related to family demographics and maternal psychosocial risk, assessed when children were two months old, were explored in the understudied population of children born in low-income, non-urban communities in Pennsylvania and North Carolina ( N = 1047). These risk factors were (1) single (unpartnered) parent status, (2) marital status, (3) mother's age at first child birth, (4) maternal education, (5) maternal reading ability, (6) poverty status, (7) residential crowding, (8) prenatal smoking exposure, and (9) maternal depression. We compared conclusions drawn using a bivariate approach, multiple regression analysis, the cumulative risk index, and latent class analysis (LCA). The risk classes derived using LCA provided a more intuitive summary of how multiple risks were organized within individuals as compared to the other methods. The five risk classes were: married low-risk; married low-income; cohabiting multiproblem; single low-income; and single low-income/education. The LCA findings illustrated how the association between particular family configurations and the infants’ caregiving environment quality varied across race and site. Discussion focuses on the value of person-centered models of analysis to understand complexities of prediction of multiple risk factors