Modeling relationships between calving traits: a comparison between standard and recursive mixed models

<p>Abstract</p> <p>Background</p> <p>The use of structural equation models for the analysis of recursive and simultaneous relationships between phenotypes has become more popular recently. The aim of this paper is to illustrate how these models can be applied in animal breeding to achieve parameterizations of different levels of complexity and, more specifically, to model phenotypic recursion between three calving traits: gestation length (GL), calving difficulty (CD) and stillbirth (SB). All recursive models considered here postulate heterogeneous recursive relationships between GL and liabilities to CD and SB, and between liability to CD and liability to SB, depending on categories of GL phenotype.</p> <p>Methods</p> <p>Four models were compared in terms of goodness of fit and predictive ability: 1) standard mixed model (SMM), a model with unstructured (co)variance matrices; 2) recursive mixed model 1 (RMM1), assuming that residual correlations are due to the recursive relationships between phenotypes; 3) RMM2, assuming that correlations between residuals and contemporary groups are due to recursive relationships between phenotypes; and 4) RMM3, postulating that the correlations between genetic effects, contemporary groups and residuals are due to recursive relationships between phenotypes.</p> <p>Results</p> <p>For all the RMM considered, the estimates of the structural coefficients were similar. Results revealed a nonlinear relationship between GL and the liabilities both to CD and to SB, and a linear relationship between the liabilities to CD and SB.</p> <p>Differences in terms of goodness of fit and predictive ability of the models considered were negligible, suggesting that RMM3 is plausible.</p> <p>Conclusions</p> <p>The applications examined in this study suggest the plausibility of a nonlinear recursive effect from GL onto CD and SB. Also, the fact that the most restrictive model RMM3, which assumes that the only cause of correlation is phenotypic recursion, performs as well as the others indicates that the phenotypic recursion may be an important cause of the observed patterns of genetic and environmental correlations.</p

Unravelling the Gut Microbiota of Cow’s Milk–Allergic Infants, Their Mothers, and Their Grandmothers

The gut microbiome constitutes a highly complex ecosystem in which bacteria are the most prominent components. Around 70% of primary colonization of the gut microbiota is maternal in origin [1], and the first 1000 days of life are crucial for the development of the intestinal microbiota [2]. Despite its early formation, the gut microbiota is highly dynamic and dependent on host-associated confounding factors such as age, diet, antibiotics, lifestyle, and environmental conditions [3,4]. Alterations in gut microbiota have been described in people with different types of allergy, including cow’s milk allergy (CMA)This work was supported by Instituto de Salud Carlos III (PI17/01087) and Fundación Sociedad Española de Alergia e Inmunología Clínica (FSEAIC_2016). It was cofunded by the European Regional Development Fund “Investing in your future” for the Thematic Network and Co-operative Research Centers ARADyAL RD16/0006/0015 and RD16/0006/0026. It was additionally supported by the Ministry of Science, Innovation in Spain (PCI2018-092930), cofunded by the European program ERA HDHL - Nutrition & the Epigenome, project Dietary Intervention in Food Allergy: Microbiome, Epigenetic and Metabolomic interactions (DIFAMEM). DR and EZ-V acknowledge funding from the Spanish Ministry of Science, Innovation and Universities (RTI2018-095166-B-I00). CU acknowledges funding from the Spanish Ministry of Economy (SAF2017-90083-R). TCB-T thanks CEUInternational Doctoral School (CEINDO) for his fellowship

A multilayered post-GWAS assessment on genetic susceptibility to pancreatic cancer

Funder: Fundación Científica Asociación Española Contra el Cáncer (ES)Funder: Cancer Focus Northern Ireland and Department for Employment and LearningFunder: Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USAAbstract: Background: Pancreatic cancer (PC) is a complex disease in which both non-genetic and genetic factors interplay. To date, 40 GWAS hits have been associated with PC risk in individuals of European descent, explaining 4.1% of the phenotypic variance. Methods: We complemented a new conventional PC GWAS (1D) with genome spatial autocorrelation analysis (2D) permitting to prioritize low frequency variants not detected by GWAS. These were further expanded via Hi-C map (3D) interactions to gain additional insight into the inherited basis of PC. In silico functional analysis of public genomic information allowed prioritization of potentially relevant candidate variants. Results: We identified several new variants located in genes for which there is experimental evidence of their implication in the biology and function of pancreatic acinar cells. Among them is a novel independent variant in NR5A2 (rs3790840) with a meta-analysis p value = 5.91E−06 in 1D approach and a Local Moran’s Index (LMI) = 7.76 in 2D approach. We also identified a multi-hit region in CASC8—a lncRNA associated with pancreatic carcinogenesis—with a lowest p value = 6.91E−05. Importantly, two new PC loci were identified both by 2D and 3D approaches: SIAH3 (LMI = 18.24), CTRB2/BCAR1 (LMI = 6.03), in addition to a chromatin interacting region in XBP1—a major regulator of the ER stress and unfolded protein responses in acinar cells—identified by 3D; all of them with a strong in silico functional support. Conclusions: This multi-step strategy, combined with an in-depth in silico functional analysis, offers a comprehensive approach to advance the study of PC genetic susceptibility and could be applied to other diseases

An application of change-point recursive models to the relationship between litter size and number of stillborns in pigs

[EN] We developed and implemented change-point recursive models and compared them with a linear recursive model and a standard mixed model (SMM), in the scope of the relationship between litter size (LS) and number of stillborns (NSB) in pigs. The proposed approach allows us to estimate the point of change in multiple-segment modeling of a nonlinear relationship between phenotypes. We applied the procedure to a data set provided by a commercial Large White selection nucleus. The data file consisted of LS and NSB records of 4,462 parities. The results of the analysis clearly identified the location of the change points between different structural regression coefficients. The magnitude of these coefficients increased with LS, indicating an increasing incidence of LS on the NSB ratio. However, posterior distributions of correlations were similar across subpopulations (defined by the change points on LS), except for those between residuals. The heritability estimates of NSB did not present differences between recursive models. Nevertheless, these heritabilities were greater than those obtained for SMM (0.05) with a posterior probability of 85%. These results suggest a nonlinear relationship between LS and NSB, which supports the adequacy of a change-point recursive model for its analysis. Furthermore, the results from model comparisons support the use of recursive models. However, the adequacy of the different recursive models depended on the criteria used: the linear recursive model was preferred on account of its smallest deviance value, whereas nonlinear recursive models provided a better fit and predictive ability based on the cross-validation approach.Financial support was provided by the IRTA, Lleida, Spain (grant 0502-21102).Ibáñez-Escriche, N.; López De Maturana, E.; Noguera, JL.; Varona, L. (2010). 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A comparison between different survival and threshold models with an application to piglet preweaning survival in a dry-cured ham-producing crossbred line

Different approaches for predicting genetic merit of piglet preweaning survival were compared using proportional hazard, threshold (TM), and sequential threshold (STM) models. Data were from 13,924 crossbred piglets (1,347 litters), born from 2000 to 2006, and originated by mating 189 Large White C21 Gorzagri boars to 328 Large White-derived crossbred sows. A frailty proportional hazard model was fitted assuming 2 different baseline hazard functions (Cox and Weibull time-dependent model) and including sire and nursed litter as random effects. The TM and STM included the same effects as considered in the proportional hazard model. Model fitting was evaluated in terms of goodness of fit and predictive ability. The goodness-of-fit was evaluated using the local weighted regression and the mean squared error, whereas the predictive ability was assessed by using a cross-validation procedure. Estimated sire variances for piglet preweaning mortality were low, and heritability ranged from 0.04 to 0.06. All 4 models led to similar ranking of sires. Results suggest that STM may be preferred to the other models for genetic evaluation of piglet preweaning survival, both for its better predictive ability and its easier interpretation. Further, STM is computationally less demanding than survival models and allows for estimating different variance components from birth up to weaning. © 2010 American Society of Animal Science