Biology subjects

Steibel, J. P.

Publications and source records attributed to Steibel, J. P..

6 recordsLinked to original sources

bioRxiv2026

Genetic Modeling of Dyadic Behavioral Traits: Implications for Estimation and Interpretation of Variance Components

Studying the genomic control of dyadic social interactions is gaining traction in animal genetics. However, genetic modeling of social interactions poses several challenges, one of which is whether social interactions should be treated as dyadic traits or as aggregated traits at the individual level. In this study, we systematically compared two approaches: dyadic models using dyadic traits and marginal models using marginally aggregated traits and we derived the algebraic relationships between their variance components. In the application, we used a published dataset on post-mixing aggression in pigs, including both directed and undirected aggression records collected during the 9-hour period after mixing among 797 finishing pigs in 59 social groups, as an example to show how model choice can affect variance estimation. Results showed that dyadic models can estimate genetic effects and permanent environmental effects by exploiting repeated dyadic interaction records, thereby enabling a more complete understanding of the sources of variation underlying social interactions. In contrast, marginal models can bias the estimation and interpretation of genetic components, as the aggregated genetic variance may be confounded with other variance components due to the aggregation of dyadic traits. Marginal models may also lead to overestimation of social group and residual variance. These results can provide useful guidance for choosing appropriate modeling strategies for social interaction traits.

genetics↗

bioRxiv2025

Revealing the hidden social structure of pigs with AI assisted automated monitoring data and social network analysis

Social interactions of farm animals affect their performance, health and welfare. The recent advances in AI-automated monitoring technologies offer digital phenotypes, at low-cost, that record the animals in real-time. This proof-of-concept study addresses, for the first time, the hypothesis that applying social network analysis (SNA) on automated data could potentially facilitate the analysis of social structures of farm animals. Data was collected using automated recording systems that captured 2D camera images and videos of pigs in six pens (16-19 animals each) on a PIC breeding company farm (USA). The system provided real-time data, including ear-tag readings, elapsed time, posture (standing, lying, sitting), and XY coordinates of the shoulder and rump for each pig. Weighted SNA was performed, based on the proximity of "standing" animals, for two 3-day periods: the early growing period (first month after mixing) and the later period (60 days post-mixing). Group level degree, betweenness, and closeness centralization showed a significant increase from the early growing period to the later one (p<0.02), highlighting the pig[s] social dynamics over time. Largest clique size remained unchanged (p=0.28), but the number of maximal cliques significantly decreased from the early to late growing period (p=0.007). Individual SNA traits were stable over these periods, except for closeness centrality and clustering coefficient which significantly increased (p<0.00001). This study demonstrates that combining AI-assisted monitoring technologies with SNA offers an efficient, real-time approach to gain novel insights into animal social interactions. This approach can optimize on-farm management or breeding practices, leading to improved animal performance, health, and welfare. HighlightsO_LISocial network analysis (SNA) applied to automated monitoring data provides novel insight into social interactions of pigs. C_LIO_LIGroup level SNA centralization traits highlighted pigs social dynamics over time. C_LIO_LIThe stability of key individual SNA traits can be leveraged for breeding purposes. C_LIO_LIAI-assisted monitoring combined with SNA help optimizing management practices on commercial farms. C_LI

animal behavior and cognition↗

bioRxiv2020

Heuristic hyperparameter optimization of deep learning models for genomic prediction

There is a growing interest among quantitative geneticists and animal breeders in the use of deep learning (DL) for genomic prediction. However, the performance of DL is affected by hyperparameters that are typically manually set by users. These hyperparameters do not simply specify the architecture of the model, they are also critical for the efficacy of the optimization and model fitting process. To date, most DL approaches used for genomic prediction have concentrated on identifying suitable hyperparameters by exploring discrete options from a subset of the hyperparameter space. Enlarging the hyperparameter optimization search space with continuous hyperparameters is a daunting combinatorial problem. To deal with this problem, we propose using differential evolution (DE) to perform an efficient search of arbitrarily complex hyperparameter spaces in DL models and we apply this to the specific case of genomic prediction of livestock phenotypes. This approach was evaluated on two pig and cattle datasets with real genotypes and simulated phenotypes (N=7,539 animals and M=48,541 markers) and one real dataset (N=910 individuals and M=28,916 markers). Hyperparameters were evaluated using cross validation. We compared the predictive performance of DL models using hyperparameters optimized by DE against DL models with "best practice" hyperparameters selected from published studies and baseline DL models with randomly specified hyperparameters. Optimized models using DE showed clear improvement in predictive performance across all three datasets.DE optimized hyperparameters also resulted in DL models with less overfitting and less variation in predictive performance over repeated retraining compared to non-optimized DL models.

genetics↗

bioRxiv2019

Deciphering sex-specific genetic architectures using local Bayesian regressions

Many complex human traits exhibit differences between sexes. While numerous factors likely contribute to this phenomenon, growing evidence from genome-wide studies suggest a partial explanation: that males and females from the same population possess differing genetic architectures. Despite this, mapping gene-by-sex (GxS) interactions remains a challenge likely because the magnitude of such an interaction is typically and exceedingly small; traditional genome-wide association techniques may be underpowered to detect such events partly due to the burden of multiple test correction. Here, we developed a local Bayesian regression (LBR) method to estimate sex-specific SNP marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This enabled us to infer sex-specific effects and GxS interactions either at the single SNP level, or by aggregating the effects of multiple SNPs to make inferences at the level of small LD-based regions. Using simulations in which there was imperfect LD between SNPs and causal variants, we showed that aggregating sex-specific marker effects with LBR provides improved power and resolution to detect GxS interactions over traditional single-SNP-based tests. When using LBR to analyze traits from the UK Biobank, we detected a relatively large GxS interaction impacting bone-mineral density within ABO and replicated many previously detected large-magnitude GxS interactions impacting waist-to-hip ratio. We also discovered many new GxS interactions impacting such traits as height and BMI within regions of the genome where both male- and female-specific effects explain a small proportion of phenotypic variance (R2 < 1x10-4), but are enriched in known expression quantitative trait loci. By combining biobank-level data and techniques to estimate sex-specific SNP effects after accounting for local-LD patterns, we are providing evidence that numerous small-magnitude GxS interactions exist to influence sex differences in a variety of complex traits.\n\nAuthor SummaryMany complex human traits are known to be influenced by an impressive number of causal variants each with very small effects, posing great challenges for genome-wide association studies (GWAS). To add to this challenge, many causal variants may possess context-dependent effects such as effects that are dependent on biological sex. While GWAS are commonly performed using specific methods in which one single nucleotide polymorphism (SNP) at a time is tested for association with a trait, alternatively we utilize methods more commonly observed in the genomic prediction literature. Such methods are advantageous in that they are not burdened by multiple test correction in the same way as traditional GWAS techniques are, and can fully account for linkage-disequilibrium patterns to accurately estimate the true effects of SNP markers. Here we adapt such methods to estimate genetic effects within sexes and provide a powerful means to compare sex-specific genetic effects.

genetics↗

bioRxiv2017

Genome Wide Association Analyses Based On Broadly Different Specifications For Prior Distributions, Genomic Windows, And Estimation Methods

A popular strategy (EMMAX) for genome wide association (GWA) analysis fits all marker effects as classical random effects (i.e., Gaussian prior) by which association for the specific marker of interest is inferred by treating its effect as fixed. It seems more statistically coherent to specify all markers as sharing the same prior distribution, whether it is Gaussian, heavy-tailed (BayesA), or has variable selection specifications based on a mixture of, say, two Gaussian distributions (SSVS). Furthermore, all such GWA inference should be formally based on posterior probabilities or test statistics as we present here, rather than merely being based on point estimates. We compared these three broad categories of priors within a simulation study to investigate the effects of different degrees of skewness for quantitative trait loci (QTL) effects and numbers of QTL using 43,266 SNP marker genotypes from 922 Duroc-Pietrain F2 cross pigs. Genomic regions were based either on single SNP associations, on non-overlapping windows of various fixed sizes (0.5 to 3 Mb) or on adaptively determined windows that cluster the genome into blocks based on linkage disequilibrium (LD). We found that SSVS and BayesA lead to the best receiver operating curve properties in almost all cases. We also evaluated approximate marginal a posteriori (MAP) approaches to BayesA and SSVS as potential computationally feasible alternatives; however, MAP inferences were not promising, particularly due to their sensitivity to starting values. We determined that it is advantageous to use variable selection specifications based on adaptively constructed genomic window lengths for GWA studies.\n\nSUMMARYGenome wide association (GWA) analyses strategies have been improved by simultaneously fitting all marker effects when inferring upon any single marker effect, with the most popular distributional assumption being normality. Using data generated from 43,266 genotypes on 922 Duroc-Pietrain F2 cross pigs, we demonstrate that GWA studies could particularly benefit from more flexible heavy-tailed or variable selection distributional assumptions. Furthermore, these associations should not just be based on single markers or even genomic windows of markers of fixed physical distances (0.5 - 3.0 Mb) but based on adaptively determined genomic windows using linkage disequilibrium information.

genetics↗

bioRxiv2017

Evidence for Transcriptome-wide RNA Editing Among Sus scrofa PRE-1 SINE Elements

BackgroundRNA editing by ADAR (adenosine deaminase acting on RNA) proteins is a form of transcriptional regulation that is widespread among humans and other primates. Based on high-throughput scans used to identify putative RNA editing sites, ADAR appears to catalyze a substantial number of adenosine to inosine transitions within repetitive regions of the primate transcriptome, thereby dramatically enhancing genetic variation beyond what is encoded in the genome.\n\nResultsHere, we demonstrate the editing potential of the pig transcriptome by utilizing DNA and RNA sequence data from the same pig. We identified a total of 8550 mismatches between DNA and RNA sequences across three tissues, with 75% of these exhibiting an A-to-G (DNA to RNA) discrepancy, indicative of a canonical ADAR-catalyzed RNA editing event. When we consider only mismatches within repetitive regions of the genome, the A-to-G percentage increases to 94%, with the majority of these located within the swine specific SINE retrotransposon PRE-1. We also observe evidence of A-to-G editing within coding regions that were previously verified in primates.\n\nConclusionsThus, our high-throughput evidence suggests that pervasive RNA editing by ADAR can exist outside of the primate lineage to dramatically enhance genetic variation in pigs.

genomics↗