In recently developed hierarchical age-period-cohort (HAPC) models, inferential questions arise: How can one assess or judge the significance of estimates of individual cohort and period effects in such models? And how does one assess the overall statistical significance of the cohort and/or the period effects? Beyond statistical significance is the question of substantive significance. This paper addresses these questions. In the context of empirical applications of linear and generalized linear mixed model specifications of HAPC models, we describe a two-step approach and set of guidelines for assessing statistical significance. The guidelines include assessments of patterns of effects and statistical tests both for the effects of individual cohorts and time periods as well as for entire sets of cohorts and periods.

Assessing the significance of cohort and period effects in hierarchical age-period-cohort models

The objective of this study was to estimate the genetic variance for within-litter variation of birth weight (BW0) using genomic (GRM) or pedigree relationship matrices (PRM) and to compare the accuracy of estimated breeding values (EBV) for within-litter variation of BW0 using GRM and PRM. The BW0 and residual variance of BW0 were modeled by the double hierarchical generalized linear model using GRM or PRM. Data came from 2 dam lines: Landrace and Large White. After editing, the data set in Landrace consisted of 748 sows with 1,938 litters and 29,430 piglets and in Large White of 989 sows with 3,320 litters and 51,818 piglets. To construct GRM, 46,466 (Landrace) and 44,826 (Large White) single nucleotide polymorphisms were used, whereas to construct PRM, 5 generations of pedigree were used. The accuracy of EBV with GRM was estimated with 8-fold cross-validation and compared to PRM. Estimated variance components were highly similar for GRM and PRM. The maternal genetic variance in residual variance of BW0 in Landrace was 0.05 with GRM and 0.06 with PRM. In Large White these were 0.04 with GRM and 0.05 with PRM. The genetic coefficient of variation (GCVSDe) was about 0.10 in both dam lines. This indicates a change of 10% in residual SD of BW0 when achieving a genetic response of 1 genetic standard deviation. The genetic correlation between birth weight and its residual variance was about 0.6 in both dam lines. The accuracies of selection for within-litter variation of birth weight were 0.35 with GRM and 0.23 with PRM in Landrace and 0.29 with GRM and 0.34 with PRM in Large White. In this case, using GRM did not significantly increase accuracies of selection. Results, however, show good opportunities to select for reduced within-litter variation of BW0. Genomic selection can increase accuracy of selection when reference populations contain at least 2,000 sows

Genetic analysis of within-litter variation in piglets' birth weight using genomic or pedigree relationship matrices.

Neurocognitive processes of decision-making in adults with ADHD – Deficits in behavior and functional brain processing, and the effects of methylphenidate

Between-individual variation in phenotypes within a population is the basis of evolution. However, evolutionary and behavioural ecologists have mainly focused on estimating between-individual variance in mean trait and neglected variation in within-individual variance, or predictability of a trait. In fact, an important assumption of mixed-effects models used to estimate between-individual variance in mean traits is that within-individual residual variance (predictability) is identical across individuals. Individual heterogeneity in the predictability of behaviours is a potentially important effect but rarely estimated and accounted for. We used 11 389 measures of docility behaviour from 1576 yellow-bellied marmots (Marmota flaviventris) to estimate between-individual variation in both mean docility and its predictability. We then implemented a double hierarchical animal model to decompose the variances of both mean trait and predictability into their environmental and genetic components. We found that individuals differed both in their docility and in their predictability of docility with a negative phenotypic covariance. We also found significant genetic variance for both mean docility and its predictability but no genetic covariance between the two. This analysis is one of the first to estimate the genetic basis of both mean trait and within-individual variance in a wild population. Our results indicate that equal within-individual variance should not be assumed. We demonstrate the evolutionary importance of the variation in the predictability of docility and illustrate potential bias in models ignoring variation in predictability. We conclude that the variability in the predictability of a trait should not be ignored, and present a coherent approach for its quantification.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jeb.13048

Genetic basis of between-individual and within-individual variance of docility.

On Hinde-Demétrio Regression Models for Overdispersed Count Data

Univ Limerick, Limerick, Ireland. Univ Warwick, Coventry CV4 7AL, W Midlands, England. London Metropolitan Univ, London, England. Rothamsted Res, Harpenden, Herts, England. Univ Glasgow, Glasgow G12 8QQ, Lanark, Scotland. Univ Nottingham, Nottingham NG7 2RD, England. Univ Bristol, Bristol BS8 1TH, Avon, England. Univ Autonoma Barcelona, E-08193 Barcelona, Spain. Daegu Haany Univ, Gyongsan, South Korea. Hongik Univ, Seoul, South Korea. Univ S Carolina, Columbia, SC 29208 USA. Hasselt Univ, Diepenbeek, Belgium. Katholieke Univ Leuven, Louvain, Belgium. City Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China. Brunel Univ, Uxbridge UB8 3PH, Middx, England. Beijing Univ Technol, Beijing, Peoples R China.

Double hierarchical generalized linear models - Discussion

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Double hierarchical generalized linear models - Discussion