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Multi-trait multi-environment (MTME) models were fitted to eucalyptus breeding trials data to assess residual variance structure, genetic stability and adaptability. To do so, 215 eucalyptus clones were evaluated in a randomized complete block design with 30 replicates and one plant per plot in four environments. At 36 months of age, tree diameter at breast height (DBH) and pilodyn penetration (PP) were measured. Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic, with the best MTME model selected using Bayesian information criterion. The harmonic mean of the relative performance of the genotypic values (HMRPGV) was used to determine stability and adaptability. Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy. In addition, genotype-by-environment interaction was complex, and there was low genetic correlation between DBH and PP. Rank correlation between the clones selected by the MTME models was high for DBH but low for PP. The HMRPGV facilitated clone selection through simultaneous evaluation of stability, adaptability, and productivity. Thus, our results suggest that heteroscedastic MTME model / HMRPGV can be efficiently applied in the genetic evaluation and selection of eucalyptus clones.

Bipolar disorder (BD) is an overarching diagnostic class defined by the presence of at least one prior manic episode (BD I) or both a prior hypomanic episode and a prior depressive episode (BD II). Traditionally, BD II has been conceptualized as a less severe presentation of BD I, however, extant literature to investigate this claim has been mixed. We apply genomic structural equation modeling (Genomic SEM) to investigate divergent genetic pathways across BD's two major subtypes using the most recent GWAS summary statistics from the PGC. We begin by identifying divergences in genetic correlations across 98 external traits using a Bonferroni-corrected threshold. We also use a theoretically informed follow-up model to examine the extent to which the genetic variance in each subtype is explained by schizophrenia and major depression. Lastly, transcriptome-wide SEM (T-SEM) was used to identify neuronal gene expression patterns associated with BD subtypes. BD II was characterized by significantly larger genetic overlap across non-psychiatric medical and internalizing traits (e.g. heart disease, neuroticism, insomnia), while stronger associations for BD I were absent. Consistent with these findings, follow-up modeling revealed a substantial major depression component for BD II. T-SEM results revealed 35 unique genes associated with shared risk across BD subtypes. Divergent patterns of genetic relationships across external traits provide support for the distinction of the bipolar subtypes. However, our results also challenge the illness severity conceptualization of BD given stronger genetic overlap across BD II and a range of clinically relevant traits and disorders.

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate an exploratory multivariate method in order to identify a set of Single Nucleotide Polymorphisms (SNPs) covarying with a set of neuroimaging phenotypes derived from functional Magnetic Resonance Imaging (fMRI). Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics. Here, we propose to transpose this idea to the DNA vs. imaging context. However, in very high-dimensional settings like in imaging genetics studies, such multivariate methods may encounter overfitting issues. Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes computed from reading and speech comprehension contrast maps. We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure. Univariate selection appears to be necessary to reduce the dimensionality. However, the significant association uncovered by this two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful genetic associations calls for a multivariate approach.

The author provides a personal perspective on Nick Martin’s contributions to behavioral genetics and his role in the workshops on statistical genetics held annually in Boulder. Highlighted are Prof. Martin’s seminal work on multivariate behavioral genetics, his career-long commitment to the value of the study of twins, and his enthusiastic support of the didactic mission of the ‘Boulder workshops’. These contributions and activities continue unabated as we celebrate Prof. Martin’s 70th birthday.

This work aimed to estimate the genetic diversity in accessions of Opuntia ficus-indica collected in 13 regions of the semiarid region of Bahia. A total of 65 accessions were evaluated in a randomized complete block design, with three replications, at the Rio Seco experimental station belonging to the State University of Feira de Santana, Amélia Rodrigues-BA. Characterization of the accessions was made through the evaluation of 17 descriptors, namely 11 agronomic and six bromatological. The average Euclidean distance was used to estimate the genetic diversity among accessions. The shortest distances were obtained for accessions from the same collection site while the largest were observed in accessions 54 and 62 (10.32 DE) and 63 and 3 (10.22 DE). The analysis of canonical variables indicated cladodes total number (CTN), plant width (PW), CL, plant height (PH), ether extract (EE), and dry weight (DW) for discard as they presented the lowest contribution of the data set variation. Principal component analysis and K-means method were used to establish the clusters, and the formation of four groups was indicated. The first two principal components captured 52.5% of the total variation present in accessions. The descriptors with the greatest contribution to the variation observed in O. ficus-indica were total cladode photosynthetic area (TCPA), cladode area (CA), and cladodes width (CW). There is divergence between cactus forage accessions collected in the semiarid region of Bahia. This information will allow the use of these materials for the formation of segregating populations in the genetic improvement program of the State University of Feira de Santana. The accessions of groups III and IV should be explored by the forage cactus breeding program, as they presented greater productive potential.

Utilizing selection indices is an effective strategy for the simultaneous evaluation of multiple traits in kale breeding programs. This approach allows for the selection of kale genotypes that exhibit enhanced productivity and adaptability by combining desirable attributes for the crop. In this study, we employed a mixed model approach in combination with various selection indices to estimate selection gains and recommend the most suitable index for kale breeding. The experiment was conducted at the Center of Development and Technology Transfer, Federal University of Lavras, Ijaci, MG. Thirty-four experimental genotypes were assessed in a randomized block design with three replicates, featuring four plants per plot. We evaluated several traits, including total leaf yield, number of leaves, average leaf mass, number of sprouts and chlorophyll content. Data analysis was performed at both the plot average level and the average quantity of the five harvests. Statistical analysis of mixed models confirmed the presence of genetic variability among kale genotypes. We examined the Smith and Hazel, Mulamba and Mock, Z-index, and FAI-BLUP indices. Smith and Hazel, Mulamba and Mock, as well as Z-index, were found unsuitable for leafy kale selection in breeding programs. The FAI-BLUP index demonstrated superior performance, aligning with the specific objectives of the kale breeding program and offering desirable gains. Therefore, we recommend the use of the FAI-BLUP index in kale breeding programs.

Aggressive Behavior (AGG) and Rule-Breaking Behavior (RB) are two of the eight CBCL syndromes. The phenotypic correlation between AGG and RB ranges from.48 to.76, and varies depending on the rater and the sex of the child. Prevalence of AGG and RB (i.e., T > or = 67) is in the range of 6%-7% in both boys and girls. Fifty percent to 60% of the children who are deviant on AGG are also deviant on RB and vice versa. Why so many children show problem behavior in the clinical range for both syndromes is unclear. This co-occurrence could be due to genetic factors influencing both traits, to environmental factors influencing both traits, or to both. The purpose of this study is to use a genetically informative sample to estimate genetic and environmental influences on AGG and RB and to investigate the etiology of the co-occurrence of both behaviors. We do this using multiple informants to take into account underlying sources of parental agreement and disagreement in ratings of their offspring. To this end, mother and father ratings of AGG and RB were collected by using the Child Behavior Checklist in a large sample of 12-year-old twins. Parental agreement is represented by an interparent correlation in the range of .53-.76, depending on phenotype (AGG or RB) and sex of the child. Genetic influences account for 79% and 69% of the individual differences in RB and AGG behavior (defined as AGG and RB on which both parents do agree) in boys. In girls 56% and 72% of the variance in RB and AGG are accounted for by genetic factors. Shared environmental influences are significant for RB in girls only, explaining 23% of the total variance. Eighty percent of the covariance between AGG and RB, similarly assessed by both parents, can be explained by genetic influences. So, co-occurrence in AGG and RB is mainly caused by a common set of genes. Parental disagreement seems to be a combination of so-called rater bias and of parental specific views.

In order to investigate the practices of goat breeding and establish a classification of goat herds, a survey was conducted with 106 goat breeders in the semi-arid region of Laghouat. Two indigenous breeds were encountered in this survey: the Arabia and the Mekatia; the Arabia being found as the dominant. The results show that breeders choose the breeding goats with a goal of generating more income in cash from the sale of products. The goat milk marketing chain appears to be weak. The milk produced is primary used for home consumption. A multivariate analysis categorized the goat farming of Laghouat region into three groups corresponding to three different farming systems: cluster 1 (pastoral system), cluster 2 (mixed crop-livestock system) and cluster 3 (small herds in zero grazing system).The in-depth study of the goals and contexts of goat farming in Laghouat will allow policy makers to design strategies for sustainable development of goat breeding in the region.

Univariate and multivariate analyses of the genetic and environmental contributions to variance in adipose tissue and adipose tissue distribution were carried out in early adolescents. Stature, weight, body mass index (BMI), and five subcutaneous skinfolds were measured at half-yearly intervals in 105 MZ and DZ twin pairs from 10 to 14 years. The most parsimonious model, which provided an adequate explanation for variation in the BMI, five skinfolds, and the T/E ratio, included additive genetic and specific environmental factors. Multivariate analyses of the genetic architecture of subcutaneous fat indicated a general skinfold genetic factor, an extremity skinfold genetic factor, and skinfold specific genetic factors. This implies that all skinfolds are under control of the same set of genes, that a different set of genes partly controls extremity skinfolds, and that other genes have a small skinfold specific impact. Environmental contributions included a general skinfold environmental factor and skinfold specific environmental factors. BMI is under control of the same set of genes as skinfolds and shows high genetic correlations with trunk skinfolds, which implies that nearly the same genes may influence trunk skinfolds and the BMI. All models were fairly consistent across the age range.