James S. Jackson and the program for research on Black Americans: Contributions to psychology and the social sciences.

James S. Jackson (1944-2020) is remembered as a groundbreaking social psychologist whose career contributions in scholarship, research, and service were fundamental to the field of psychology. This article briefly outlines his career-long work and contributions. A strong believer in interdisciplinary work, his research spanned other related social science disciplines (e.g., sociology, political science), as well as health and social welfare professions (public health, social work, medicine). As the founding director of the Program for Research on Black Americans at the Institute for Social Research, James Jackson initiated and led a long-standing program with a dual focus on research and training and mentoring doctoral students, postdoctoral scholars, and early career scientists. Jackson's efforts in the development of several nationally representative surveys of the Black population in the United States (e.g., National Survey of Black Americans, National Survey of American Life) revolutionized research focusing on the lives of Black Americans. James Jackson's international influence and reputation included numerous prestigious positions within national science organizations and honors and awards for his scientific contributions. Among James S. Jackson's most enduring legacies is the vast network of current scientists, researchers, and academics who were trained under his direction and leadership. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola (Brassica napus L.) Lines.

Genomic prediction is becoming a popular plant breeding method to predict the genetic merit of lines. While some genomic prediction results have been reported in canola, none have been evaluated for blackleg disease. Here, we report genomic prediction for seedling emergence, survival rate, and internal infection), using 532 Spring and Winter canola lines. These lines were phenotyped in two replicated blackleg disease nurseries grown at Wickliffe and Green Lake, Victoria, Australia. A transcriptome genotyping-by-sequencing approach revealed 98,054 single nucleotide polymorphisms (SNPs) after quality control. We assessed various genomic prediction scenarios based on Genomic Best Linear Unbiased Prediction (GBLUP), BayesR and BayesRC, which can make use of prior quantitative trait loci information, via cross-validation. Clustering based on genomic relationships showed that Winter and Spring lines were genetically distinct, indicating limited gene flow between sets. Genetic correlations within traits between Spring and Winter lines ranged from 0.68 and 0.90 (mean = 0.76). Based on GBLUP in the whole population, moderate to high genomic prediction accuracies were achieved within environments (0.35-0.74) and were reduced across environments (0.28-0.58). Prediction accuracy within the Spring set ranged from 0.30-0.69, and from 0.19-0.71 within the Winter set. The BayesR model resulted in slightly lower accuracy to GBLUP. The proportion of genetic variance explained by known blackleg quantitative trait loci (QTL) was < 30%, indicating that there is a large reservoir of genetic variation in blackleg traits that remains to be discovered, but can be captured with genomic prediction. However, providing prior information of known QTL in the BayesRC method resulted in an increased prediction accuracy for survival and internal infection, particularly with Spring lines. Overall, these promising results indicate that genomic prediction will be a valuable tool to make use of all genetic variation to improve blackleg resistance in canola.

Accuracy of genomic breeding values in multi-breed dairy cattle populations.

BACKGROUND: Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV. METHODS: Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies. RESULTS: When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained. CONCLUSION: Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.

Cross-cultural sources of measurement error in substance use surveys.

We present an overview of the cross-cultural quality of survey reports of substance use behaviors in the United States. Empirical data from 36 published studies (1977-2003) are examined to evaluate the reliability and validity of substance use reports across cultural groups. In these studies, race/ethnicity are used as proxy indicators of respondent culture. In general, the available research suggests that, with a few exceptions, the quality of survey data on racial and ethnic disparities in substance use is often limited by differential measurement error. A conceptual paradigm is presented to consider a wide range of potential causes for these differences in measurement error that includes two dimensions: emphasis on negative vs. positive behavior patterns, and emphasis on internal vs. external causal factors. These two dimensions yield four potential models that are useful in understanding variations in substance use measurement error: the cultural deficit model, the cultural conflict model, the mainstream conformity model, and the cultural distrust model. Future research should focus on the ability of each of these alternative models to account for cultural variability in the quality of substance use reporting.