Reduced animal model for marker assisted selection using best linear unbiased prediction

International audienc

Frailty Index and incident mortality, hospitalization and institutionalization in Alzheimer's disease: data from the ICTUS study.

BACKGROUND: The identification of an objective evaluation of frailty capable of predicting adverse outcomes in Alzheimer's disease is increasingly discussed. The purpose of this study was to investigate whether the Frailty Index (FI) predicts hospitalization, institutionalization, and mortality in Alzheimer's disease patients. METHODS: A prospective multicenter cohort study (follow-up = 2 years) that included 1,191 participants with Alzheimer's disease was carried out. The outcomes of interest were incident hospitalization, institutionalization, and mortality. The FI was calculated as the ratio of actual to thirty potential deficits, that is, deficits presented by the participant divided by 30. Severity of dementia was assessed using the Clinical Dementia Rating score. Cox proportional hazard models were performed. RESULTS: Mean age of the study sample was 76.2 (SD = 7.6) years. A quadratic relationship of the FI with age was reported at baseline (R 2 = .045, p < .001). The FI showed a statistically significant association with mortality (age- and gender-adjusted hazard ratio [HR] = 1.019, 95% confidence interval [CI] = 1.002-1.037, p = .031) and hospitalization (age- and gender-adjusted HR = 1.017, 95% CI = 1.006-1.029, p = .004) and a borderline significance with institutionalization. When the Clinical Dementia Rating score was simultaneously included in the age- and gender-adjusted models, the FI confirmed its predictive capacity for hospitalization (HR = 1.019, 95% CI = 1.006-1.032, p = .004), whereas the Clinical Dementia Rating score was the strongest predictor for mortality (HR = 1.922, 95% CI = 1.256-2.941, p = .003) and institutionalization (HR = 1.955, 95%CI = 1.427-2.679, p < .001). CONCLUSIONS: The FI is a robust predictor of adverse outcomes even after the stage of the underlying dementia is considered. Future work should evaluate the clinical implementation of the FI in the assessment of demented individuals in order to improve the personalization of care

Estimación bayesiana de componentes de (co) varianza en Brangus argentino para caracteres de res mediante el algoritmo FCG

19-26Data on 2273 Brangus young bulls and heifers were used to estimate heritabilities (h2) and genetics and environmental correlations for ultrasound carcass measures. Records were from the genetic evaluation program of Asociación Argentina de Brangus. Traits measured were rib-eye area (AOB), marbling (MB), back-fat thickness (GD), and hip-fat thickness (GC). Average ages of measure were 641 days in males and 685 in females. The genetic and environmental dispersion parameters were estimated by a conjugate Bayesian algorithm (FCG). Estimates of h2 were 0,22, 0,16, 0,12, and 0,21, for AOB, GD, CC, and MB, respectively. In general, estimates of genetic and environmental correlations were close to the average published values. Even tough estimates of h2 were below the average of published estimates for beef cattle, the additive genetic variation found in the current study would lead to a moderate response to selection - using predictions of breeding value that are calculated with the estimate parameters

Beyond genomic selection: the animal model strikes back (one generation)!

Genome inheritance is by segments of DNA rather than by independent loci. We introduce the ancestral regression (AR) as a recursive system of simultaneous equations, with grandparental path coefficients as novel parameters. The information given by the pedigree in the AR is complementary with that provided by a dense set of genomic markers, such that the resulting linear function of grandparental BV is uncorrelated to the average of parental BV in the absence of inbreeding. AR is then connected to segmental inheritance by a causal multivariate Gaussian density for BV. The resulting covariance structure (Σ) is Markovian, meaning that conditional on the BV of parents and grandparents, the BV of the animal is independent of everything else. Thus, an algorithm is presented to invert the resulting covariance structure, with a computing effort that is linear in the number of animals as in the case of the inverse additive relationship matrix.Instituto de Genética Veterinari

Estimación bayesiana de componentes de (co) varianza en Brangus argentino para caracteres de res mediante el algoritmo FCG

19-26Data on 2273 Brangus young bulls and heifers were used to estimate heritabilities (h2) and genetics and environmental correlations for ultrasound carcass measures. Records were from the genetic evaluation program of Asociación Argentina de Brangus. Traits measured were rib-eye area (AOB), marbling (MB), back-fat thickness (GD), and hip-fat thickness (GC). Average ages of measure were 641 days in males and 685 in females. The genetic and environmental dispersion parameters were estimated by a conjugate Bayesian algorithm (FCG). Estimates of h2 were 0,22, 0,16, 0,12, and 0,21, for AOB, GD, CC, and MB, respectively. In general, estimates of genetic and environmental correlations were close to the average published values. Even tough estimates of h2 were below the average of published estimates for beef cattle, the additive genetic variation found in the current study would lead to a moderate response to selection - using predictions of breeding value that are calculated with the estimate parameters

Welfare Effects of a Non-Contributory Old Age Pension: Experimental Evidence for Ekiti State, Nigeria

Many countries in the developing world have implemented non-contributory old-age pensions. Evidence of the impact of such policies on the elderly in Sub-Saharan Africa is scarce, however. In this paper, we provide the first evidence from a randomized evaluation of an unconditional, non-contributory pension scheme targeted at the elderly in Ekiti State, Nigeria. Our findings show that treated beneficiaries self-reported better quality of life, more stable mental health, and better general health. We also provide evidence of spillover effects on labor outcomes and on household expenditure patterns as well as support for demand-side interventions aimed at improving the welfare of elderly poor citizens and other household members.Centro de Estudios Distributivos, Laborales y Sociale

Effect of a 1-Year Nutritional Blend Supplementation on Plasma p-tau181 and GFAP Levels among Community-Dwelling Older Adults: A Secondary Analysis of the Nolan Trial

BACKGROUND: Observational studies and some randomized controlled trials have suggested that nutritional supplementation could be a possible intervention pathway to prevent cognitive decline and Alzheimer's disease (AD). As measuring amyloid-β and tau pathophysiology by positron emission tomography (PET) or cerebrospinal fluid (CSF) analyses may be perceived as complex, plasma versions of such biomarkers have emerged as more accessible alternatives with comparable capacity of predicting cognitive impairment. OBJECTIVES: This study aimed to evaluate the effect of a 1-year intervention with a nutritional blend on plasma p-tau181 and glial fibrillary acidic protein (GFAP) levels in community-dwelling older adults. Effects were further assessed in exploratory analyses within sub-cohorts stratified according to p-tau status (with the third tertile considered as high: ≥15.1 pg/ mL) and to apolipoprotein E (APOE) ε4 allele status. METHODS: A total of 289 participants ≥70 years (56.4% female, mean age 78.1 years, SD=4.7) of the randomized, double-blind, multicenter, placebo-controlled Nolan trial had their plasma p-tau181 assessed, and daily took either a nutritional blend (composed of thiamin, riboflavin, niacin, pantothenic acid, pyridoxine, biotin, folic acid, cobalamin, vitamin E, vitamin C, vitamin D, choline, selenium, citrulline, eicosapentaenoic acid - EPA, and docosahexaenoic acid - DHA) or placebo for 1 year. RESULTS: After 1-year, both groups presented a significant increase in plasma p-tau181 and GFAP values, with no effect of the intervention (p-tau181 between-group difference: 0.27pg/mL, 95%CI: -0.95, 1.48; p=0.665; GFAP between-group difference: -3.28 pg/mL, 95%CI: -17.25, 10.69; p=0.644). P-tau-and APOE ε4-stratified analyses provided similar findings. CONCLUSIONS: In community-dwelling older adults, we observed an increase in plasma p-tau181 and GFAP levels that was not different between the supplementation groups after one year

Beyond genomic selection: the animal model strikes back (one generation)!

Genome inheritance is by segments of DNA rather than by independent loci. We introduce the ancestral regression (AR) as a recursive system of simultaneous equations, with grandparental path coefficients as novel parameters. The information given by the pedigree in the AR is complementary with that provided by a dense set of genomic markers, such that the resulting linear function of grandparental BV is uncorrelated to the average of parental BV in the absence of inbreeding. AR is then connected to segmental inheritance by a causal multivariate Gaussian density for BV. The resulting covariance structure (Σ) is Markovian, meaning that conditional on the BV of parents and grandparents, the BV of the animal is independent of everything else. Thus, an algorithm is presented to invert the resulting covariance structure, with a computing effort that is linear in the number of animals as in the case of the inverse additive relationship matrix.Instituto de Genética Veterinari

Meta-analysis of genome-wide association from genomic prediction models

Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.Fil: Bernal Rubio, Yeni Liliana. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gualdron Duarte, Jose Luis. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bates, R. O.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; ArgentinaFil: Ernst, C. W.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; ArgentinaFil: Nonneman, D.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Rohrer, G. A.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: King, A.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Shackelford, S. D.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Wheeler, T. L.. United States Department of Agriculture. Agricultural Research Service; Estados UnidosFil: Cantet, Rodolfo Juan Carlos. Michigan State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Steibel, J. P.. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal; Argentina. Michigan State University; Estados Unido