Summary
Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 ...October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP‐LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP‐LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de‐regressed EBV was slightly small (i.e. 0.87%–18.75%). The present study also compared the performance of five genomic prediction models and two cross‐validation methods. The five genomic models predicted EBV and de‐regressed EBV of the ten traits similarly well. Of the two cross‐validation methods, leave‐one‐out cross‐validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle.
Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory tract infection in young children and the second leading cause of infant death worldwide. While global circulation has ...been extensively studied for respiratory viruses such as seasonal influenza, and more recently also in great detail for SARS-CoV-2, a lack of global multi-annual sampling of complete RSV genomes limits our understanding of RSV molecular epidemiology. Here, we capitalise on the genomic surveillance by the INFORM-RSV study and apply phylodynamic approaches to uncover how selection and neutral epidemiological processes shape RSV diversity. Using complete viral genome sequences, we show similar patterns of site-specific diversifying selection among RSVA and RSVB and recover the imprint of non-neutral epidemic processes on their genealogies. Using a phylogeographic approach, we provide evidence for air travel governing the global patterns of RSVA and RSVB spread, which results in a considerable degree of phylogenetic mixing across countries. Our findings highlight the potential of systematic global RSV genomic surveillance for transforming our understanding of global RSV spread.
Describing the early life associations between infectious disease episodes and growth, cognitive development, and vaccine response in the first 2 years of life is one of the primary goals of the ...Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) cohort study. To collect high-resolution data during a critical early period of development, field staff visit each study participant at their house twice weekly from birth to 2 years of age to collect daily reported illness and treatment data from caregivers. Detailed infectious disease histories will not only allow us to relate the overall burden of infectious disease with the primary outcomes of the study, but will also allow us to describe the ages at which infectious diseases have the greatest effect on child health. In addition, twice-weekly visits allow for sample collection when diarrhea episodes are identified. This article describes the methods used to collect illness and treatment history data and discusses the a priori definitions of diarrhea and acute lower respiratory illness episodes.
Several methods have recently been developed to identify regions of the genome that have been exposed to strong selection. However, recent theoretical and empirical work suggests that polygenic ...models are required to identify the genomic regions that are more moderately responding to ongoing selection on complex traits. We examine the effects of multi-trait selection on the genome of a population of US registered Angus beef cattle born over a 50-year period representing approximately 10 generations of selection. We present results from the application of a quantitative genetic model, called Birth Date Selection Mapping, to identify signatures of recent ongoing selection.
We show that US Angus cattle have been systematically selected to alter their mean additive genetic merit for most of the 16 production traits routinely recorded by breeders. Using Birth Date Selection Mapping, we estimate the time-dependency of allele frequency for 44,817 SNP loci using genomic best linear unbiased prediction, generalized least squares, and BayesCπ analyses. Finally, we reconstruct the primary phenotypes that have historically been exposed to selection from a genome-wide analysis of the 16 production traits and gene ontology enrichment analysis.
We demonstrate that Birth Date Selection Mapping utilizing mixed models corrects for time-dependent pedigree sampling effects that lead to spurious SNP associations and reveals genomic signatures of ongoing selection on complex traits. Because multiple traits have historically been selected in concert and most quantitative trait loci have small effects, selection has incrementally altered allele frequencies throughout the genome. Two quantitative trait loci of large effect were not the most strongly selected of the loci due to their antagonistic pleiotropic effects on strongly selected phenotypes. Birth Date Selection Mapping may readily be extended to temporally-stratified human or model organism populations.
More epidemiological data are needed on risk and protective factors for child development. In The Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequences ...for Child Health and Development (MAL-ED) cohort study, we assessed child development in a harmonious manner across 8 sites in Bangladesh, Brazil, India, Nepal, Pakistan, Peru, South Africa, and Tanzania. From birth to 24 months, development and language acquisition were assessed via the Bayley Scales of Infant and Toddler Development and a modified MacArthur Communicative Development Inventory. Other measures were infant temperament, the child's environment, maternal psychological adjustment, and maternal reasoning abilities. We developed standard operating procedures and used multiple techniques to ensure appropriate adaptation and quality assurance across the sites. Test adaptation required significant time and human resources but is essential for data quality; funders should support this step in future studies. At the end of this study, we will have a portfolio of culturally adapted instruments for child development studies with examination of psychometric properties of each tool used.
The Bill and Melinda Gates Foundation's Healthy Birth, Growth and Development knowledge integration project aims to improve the overall health and well‐being of children across the world. The project ...aims to integrate information from multiple child growth studies to allow health professionals and policy makers to make informed decisions about interventions in lower and middle income countries. To achieve this goal, we must first understand the conditions that impact on the growth and development of children, and this requires sensible models for characterising different growth patterns. The contribution of this paper is to provide a quantitative comparison of the predictive abilities of various statistical growth modelling techniques based on a novel leave‐one‐out validation approach. The majority of existing studies have used raw growth data for modelling, but we show that fitting models to standardised data provide more accurate estimation and prediction. Our work is illustrated with an example from a study into child development in a middle income country in South America.
Genomic selection using single nucleotide polymorphism (SNP) chips to genotype breeding candidates has become pervasive in the dairy industry. Though the cost of SNP genotyping has dropped ...substantially in the past 10 yr, use of high-density (HD) SNP chips is still relatively expensive in practice. However, low-density (LD) SNP chips offer a cost-effective alternative and thus are gaining interest. LD SNPs are either chosen based on their map locations (e.g., evenly spaced) or selected according to their effects on quantitative traits of interest. Genomic prediction using LD SNP genotypes, however, can suffer from loss of genomic information, leading to decreased prediction accuracy. Preferably, moderate (MD) or HD SNP genotypes are imputed from LD genotypes and used in genomic prediction, yet strategies for selecting LD SNPs for imputation-mediated genomic prediction has not been addressed adequately. Consequently, we evaluated 2 fundamental strategies for selecting LD SNPs for imputation-mediated, multiple-trait genomic prediction, using Holstein animals (n = 11,106), each genotyped with 77,326 SNP (GGPHD or 80K chip). The quantitative traits included predicted transmitting abilities for milk yield, fat yield, and daughter pregnancy rate. Briefly, LD SNPs were selected according to their effects estimated using either single-trait Bayesian regression (STBR) models or multiple-trait Bayesian regression (MTBR) models. The STBR method selected 2K SNPs specific to each trait and then pooled them into a common panel. Because there were overlapping SNPs among these 3 traits, it allowed inclusion of an additional subset of map-optimal, informative SNPs, totaling 7K LD SNPs. The MTBR method selected 7K SNPs that were informative for all the traits, leaving no space for map-optimal SNPs. For each of the 2 sets, 7K SNP genotypes were imputed to 80K SNP genotypes and the latter were used to compute genomic-estimated breeding values. There were 2 salient features with the proposed strategies: 1) LD SNPs included those associated with the quantitative traits, and genotypes of these SNPs were not subject to imputation errors; and 2) genomic predictions were made based on imputed 80K genotypes, and loss of genomic information was not relevant. The results showed that the STBR method, which included a subset of map-optimal SNPs, had slightly better imputation accuracy and gave higher genomic prediction accuracy for milk yield and fat yield than the MTBR method. Nevertheless, both strategies performed well in terms of imputation accuracy (99.64 to 99.87%) and genomic prediction accuracy (97.66 to 98.09%) in this Holstein population.
Objective
In the Weight Loss Maintenance (WLM) Trial, a personal contact (PC) intervention sustained greater weight loss relative to a self‐directed (SD) group over 30 months. This study investigated ...the effects of continued intervention over an additional 30 months and overall weight change across the entire WLM Trial.
Methods
WLM had 3 phases. Phase 1 was a 6‐month weight loss program. In Phase 2, those who lost ≥4 kg were randomized to a 30‐month maintenance trial. In Phase 3, PC participants (n = 196, three sites) were re‐randomized to no further intervention (PC‐Control) or continued intervention (PC‐Active) for 30 more months; 218 SD participants were also followed.
Results
During Phase 3, weight increased 1.0 kg in PC‐Active and 0.5 kg in PC‐Control (mean difference 0.6 kg; 95% CI:−1.4 to 2.7; P = 0.54). Mean weight change over the entire study was −3.2 kg in those originally assigned to PC (PC‐Combined) and −1.6 kg in SD (mean difference −1.6 kg; 95% CI:−3.0 to −0.1; P = 0.04).
Conclusions
After 30 months of the PC maintenance intervention, continuation for another 30 months provided no additional benefit. However, across the entire study, weight loss was slightly greater in those originally assigned to PC.
Children in low-income countries experience multiple illness symptoms in early childhood. Breastfeeding is protective against diarrhea and respiratory infections, and these illnesses are thought to ...be risk factors of one another, but these relationships have not been explored simultaneously. In the eight-site MAL-ED study, 1,731 infants were enrolled near birth and followed for 2 years. We collected symptoms and diet information through twice-weekly household visits. Poisson regression was used to determine if recent illness history was associated with incidence of diarrhea or acute lower respiratory infections (ALRI), accounting for exclusive breastfeeding. Recent diarrhea was associated with higher risk of incident diarrhea after the first 6 months of life (relative risk RR 1.10, 95% confidence interval CI 1.04, 1.16) and with higher risk of incident ALRI in the 3- to 5-month period (RR 1.23, 95% CI 1.03, 1.47). Fever was a consistent risk factor for both diarrhea and ALRI. Exclusive breastfeeding 0-6 months was protective against diarrhea (0-2 months: RR 0.39, 95% CI 0.32, 0.49; 3-5 months: RR 0.83, 95% CI 0.75, 0.93) and ALRI (3-5 months: RR 0.81, 95% CI 0.68, 0.98). Children with recent illness who were exclusively breastfed were half as likely as those not exclusively breastfed to experience diarrhea in the first 3 months of life. Recent illness was associated with greater risk of new illness, causing illnesses to cluster within children, indicating that specific illness-prevention programs may have benefits for preventing other childhood illnesses. The results also underscore the importance of exclusive breastfeeding in the first 6 months of life for disease prevention.
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