Genetic Selection Research Articles

Feature Selection Techniques for Enhancing Credit Card Fraud Detection Performance: A Hybrid Metaheuristic Approach Using Nature-Inspired Algorithms

Abstract: The surge in credit card usage has led to a parallel increase in fraudulent transactions, necessitating advanced detection systems. Traditional methods encounter challenges with high-dimensional and imbalanced datasets. This paper proposes a comprehensive approach integrating metaheuristic algorithms and deep learning techniques to enhance fraud detection accuracy. The first contribution, the Rock Hyrax Swarm Optimization Feature Selection (RHSOFS) algorithm, draws inspiration from the collective behaviors of rock hyrax swarms to effectively select relevant features from high-dimensional datasets. RHSOFS identifies an optimal subset of features critical for fraud detection through supervised machine learning. Complementing this, the second contribution leverages a hybrid deep learning model. Beginning by organizing transactional data and constructing a Logical Graph of Behavior Profile (LGBP) to abstract transaction details, the Modified Butterfly Optimization Algorithm (MBOA) selects important features from the dataset. The hybrid model, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), establishes rational connections between transactional characteristics, enhancing detection performance. To evaluate the approach, comparative efficiency analyses against existing methods, including Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS), are conducted. Results demonstrate the superiority of the approach in terms of both reliability and recognition rate, validated through rigorous statistical testing. The proposed hybrid approach signifies a significant advancement in credit card fraud detection systems, offering enhanced accuracy and efficiency in combating fraudulent activities amidst the growing complexity of transactional datasets.

Genetic basis and selection of glyceollin elicitation in wild soybean.

Glyceollins, a family of phytoalexins elicited in legume species, play crucial roles in environmental stress response (e.g., defending against pathogens) and human health. However, little is known about the genetic basis of glyceollin elicitation. In the present study, we employed a metabolite-based genome-wide association (mGWA) approach to identify candidate genes involved in glyceollin elicitation in genetically diverse and understudied wild soybeans subjected to soybean cyst nematode. In total, eight SNPs on chromosomes 3, 9, 13, 15, and 20 showed significant associations with glyceollin elicitation. Six genes fell into two gene clusters that encode glycosyltransferases in the phenylpropanoid pathway and were physically close to one of the significant SNPs (ss715603454) on chromosome 9. Additionally, transcription factors (TFs) genes such as MYB and WRKY were also found as promising candidate genes within close linkage to significant SNPs on chromosome 9. Notably, four significant SNPs on chromosome 9 show epistasis and a strong signal for selection. The findings describe the genetic foundation of glyceollin biosynthesis in wild soybeans; the identified genes are predicted to play a significant role in glyceollin elicitation regulation in wild soybeans. Additionally, how the epistatic interactions and selection influence glyceollin variation in natural populations deserves further investigation to elucidate the molecular mechanism of glyceollin biosynthesis.

How low can you go? Broiler breast muscle possesses a mitochondrial content of just 2%

ABSTRACT In 1977 the pectoralis major (breast) muscle mitochondrial content of domestic broiler chickens, Gallus gallus domesticus, was estimated at 4.1%. However, in the subsequent ∼45 years broilers have been further modified by sustained intensive genetic selection on feed conversion efficiency and breast muscle mass. With a view to understanding the metabolic implications of this historic selection – and also laying a foundation to better interpret current commercial performance – we quantitated the cellular bioenergetic structure of modern broilers. The breast muscle of 4 64 d old Ross308 broilers was subject to morphometric analysis based on transverse Transmission Electron Microscopy (TEM) images. This approach determined an average mitochondrial content, diameter and circularity ratio of 2.1%, 0.42 µm and 0.72, respectively. Broiler breast mitochondrial content has thus approximately halved in the last 45 years and represents one of the lowest contents recorded for the muscle of any eukaryotic species.

A Network Intrusion Detection Model Based on GA-Improved NSA

With the popularization of the Internet, network security issues have also emerged. In response to network security issues, there are certain shortcomings in current network intrusion detection technologies. To improve and optimize this technology, a network intrusion detection model based on genetic algorithm and improved negative selection algorithm is designed. The generation of detectors in the selection algorithm is replaced by genetic algorithm, and the non-self spatial distribution of detectors is optimized. This paper proposes a network intrusion detection model using a genetic algorithm-improved negative selection algorithm (GA-INSA) and an improved LeNet-5 CNN. The GA enhances detector generation and distribution in NSA while SMOTE handles class imbalance for CNN. Experiments show GA-INSA has over 9% higher accuracy than NSA, SVM and GA-BP across different data sizes. The improved LeNet-5 demonstrates superior accuracy and recall rates over by over 20% baseline LeNet-5. However, more comprehensive evaluation on public datasets, design details on architectures, and discussion around limitations are warranted. The data shows that the designed network intrusion detection model has better performance. The model can provide technical support for network intrusion detection in reality and can enrich the content of network intrusion detection technology.

Evaluation of breeding cost in the French maritime pine breeding program and perspectives for alternative strategies using molecular markers

The economic efficiency of conventional breeding strategies for forest trees based on biparental crosses is compared with that of alternative strategies based on pedigree reconstruction using molecular markers. Analyses of economic efficiency is based on comparisons of breeding scenarios corresponding to the same total investment. The first step is the description and cost evaluation of each basic operation, from crossing to genetic selection and clonal archive establishment. Breeding scenarios are then compared by stochastic sampling with a parametric genetic model (POPSIM), the comparison criteria in this case being genetic gain in the seed orchard for a given level of genetic diversity. Additionally, the economic gain resulting from the use of improved material is estimated for different levels of breeding investment. Our analysis shows that genotyping costs account for a much smaller proportion of total investment than phenotyping costs. We also show that, in comparisons of breeding scenarios corresponding to the same total investment, the three main breeding strategies (biparental crosses, polymix crosses, and open pollination) achieve similar genetic gains provided that sufficiently large numbers of parents are considered. These results open up promising perspectives for the wider integration of molecular markers into forest tree breeding strategies.

Nomogram Utilizing ABVS Radiomics and Clinical Factors for Predicting ≤ 3 Positive Axillary Lymph Nodes in HR+ /HER2- Breast Cancer with 1–2 Positive Sentinel Nodes

BackgroundIn HR+/HER2- breast cancer patients with ≤3 positive axillary lymph nodes (ALNs), genomic tests can streamline chemotherapy decisions. Current studies, centered on tumor metrics, miss broader patient insights. Automated Breast Volume Scanning (ABVS) provides advanced 3D imaging, and its potential synergy with radiomics for ALN evaluation is untapped. ObjectiveThis study sought to combine ABVS radiomics and clinical characteristics in a nomogram to predict ≤3 positive ALNs in HR+/HER2- breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs), guiding clinicians in genetic test candidate selection. MethodsWe enrolled 511 early-stage breast cancer patients: 362 from A Hospital for training and 149 from B Hospital for validation. Using LASSO logistic regression, primary features were identified. A clinical-radiomics nomogram was developed to predict the likelihood of ≤3 positive ALNs in HR+/HER2- patients with 1-2 positive SLNs. We assessed the discriminative capability of the nomogram using the ROC curve. The model's calibration was confirmed through a calibration curve, while its fit was evaluated using the Hosmer-Lemeshow (HL) test. To determine the clinical net benefits, we employed the Decision Curve Analysis (DCA). ResultsIn the training group, 81.2% patients had ≤3 metastatic ALNs, and 83.2% in the validation group. We developed a clinical-radiomics nomogram by analyzing clinical characteristics and rad-scores. Factors like positive SLNs (OR=0.077), absence of negative SLNs (OR=11.138), lymphovascular invasion (OR=0.248), and rad-score (OR=0.003) significantly correlated with ≤3 positive ALNs. The clinical-radiomics nomogram, with an AUC of 0.910 in training and 0.882 in validation, outperformed the rad-score-free clinical nomogram (AUCs of 0.796 and 0.782). Calibration curves and the HL test (P-values 0.688 and 0.691) confirmed its robustness. DCA showed the clinical-radiomics nomogram provided superior net benefits in predicting ALN burden across specific threshold probabilities. ConclusionsWe developed a clinical-radiomics nomogram that integrated radiomics from ABVS images and clinical data to predict the presence of ≤3 positive ALNs in HR+/HER2- patients with 1-2 positive SLNs, aiding oncologists in identifying candidates for genomic tests, bypassing ALND. In the era of precision medicine, combining genomic tests with SLNB refines both surgical and systemic patient treatments.

Modeling of PCR-RFLP genotyping of cattle by polymorphic markers of iNOS gene

Relevance. The study of the Bos taurus iNOS gene polymorphism and its association with resistance to bovine leukemia, as well as with breeding value in terms of milk productivity, is a topical subject of genetic selection research.The purpose of this study was to identify and map polymorphic restriction sites in 4 SNP markers (AH13-1, AH13-2, AH13-3, AH13-4) of the Bos taurus iNOS gene, followed by PCR-RFLP profiling of the encountered genotypes and modeling of the method of gene testing of cattle by the listed polymorphic markers of the analyzed locus.Methods. An effective tool for visualizing the result of computer simulation of the experiment, which contributed to predicting the applicability of the developed method of gene testing, was in silico modeling of the generated PCR-RFLP profiles of the encountered genotypes of polymorphic markers of the Bos taurus iNOS gene. At the same time, the very result of mapping the identified polymorphic restriction sites and subsequent profiling of the encountered genotypes indicated the technical feasibility of detecting polymorphic positions of 4 SNP markers of the Bos taurus iNOS gene by PCR-RFLP analysis. This, in turn, initiated the modeling of the method of gene testing of cattle by the listed polymorphic markers with the provision of successive stages of DNA isolation from whole preserved blood of cattle, PCR with oligonucleotide primers iNOS-F and iNOS-R, RFLP with selected restriction endonucleases (HinfI, Sau96I (AspS9I isoschizomer), HpyAV and MluCI (Sse9I isoschizomer), as well as performing gel electrophoresis detection with visualization of the obtained electrophoregrams in a UV transilluminator.

The occurrence of myopathies and meat abnormalities in modern broiler farming

Over the past few decades, there has been a significant increase in the production and consumption of poultry meat due to its high-quality nutritional profile, relatively low production costs, and ease of meat processing. This growing demand has led to progressive improvements in genetic selection for fast-growing broilers, triggering several spontaneous idiopathic muscle abnormalities along with an increased susceptibility to stress-induced myopathy. These muscle abnormalities have a negative impact on the quality of both fresh and processed chicken meat, primarily affecting the large breast muscle (m. pectoralis major) of broilers, which is also the most valuable part of the carcass. Frequent meat abnormalities include the occurrence of pale, soft, and exudative meat, primarily characterized by reduced water-holding capacity, while Deep Pectoral Myopathy results in a change in colour and a significant reduction in the attractive appearance of the small breast muscle (m. pectoralis minor). Myopathies labelled as "White Striping" are characterized by superficial white stripes, "Wooden Breast" is marked by pale and bulging areas of significant hardness, while "Spaghetti Meat" is described by the separation of muscle fibres resembling spaghetti pasta. These mentioned abnormalities and myopathies negatively impact the economic aspect of the poultry industry during the removal of affected parts and, furthermore, reduce the qualitative and nutritional value of the meat, posing challenges during technological processes. This paper describes recent findings on the occurrence, characteristics, and prevalence of the most common myopathies affecting modern broiler farming.

Genetic diversity and autozygosity estimates in wild european boars and domesticated pigs

BACKGROUND: Pigs are one of the most widely distributed domestic animals. The study of their genetic diversity and selection loci is of great interest both in the field of genetics and animal breeding, and in the aspect of conservation and development of breeding resources and food security.
 AIM: The aim of the presented work is to evaluate the autozygosity and the distribution of autozygosity segments (HBD) in wild boars and pigs of the main commercial breeds: Large White, Landrace and Duroc, and to search for selection loci related to adaptation to habitat conditions and selection pressure.
 MATERIALS AND METHODS: The aim of the presented work is to evaluate the autozygosity and distribution of autozygosity segments (HBD) in wild boars and domestic pigs of the main commercial breeds: Large White, Landrace and Duroc, and to search for selection loci related to adaptation to habitat conditions and selection pressure.
 RESULTS: Based on the results of the genome scan, the average autozygosity values in boars and pigs were in the range of 0.23–0.29, but in boars about 0.08 of the genome share is covered by HBD segments, presumably originating from ancestors who lived about 206 years ago; in pigs — originating from ancestors who lived about 64 years ago.
 CONCLUSIONS: Only 3 segments met the criteria for top-HBD (frequency of at least 60% and at least 10 SNPs) in boars. In Large White, Landrace and Duroc pigs, 18, 9 and 35 segments were identified, respectively. In general, the analysis of HBD segments showed that they reflect the main breeding strategies aimed at developing commercial pigs.

Short communication: Correlation of methane production, intensity, and yield with residual feed intake throughout lactation in Holstein cows

The environmental impact of dairy production can be reduced in several ways, including increasing feed efficiency and reducing methane (CH4) emissions. There is no consensus on their relationship. This study aimed at estimating the correlations between residual feed intake (RFI) and CH4 emissions expressed in g/d methane production (MeP), g/kg of fat- and protein-corrected milk methane intensity (MeI), or g/kg of DM intake methane yield (MeY) throughout lactation. We collected CH4 data using GreenFeed devices from 107 Holstein cows, as well as production and intake phenotypes. RFI was predicted from DM intake, fat- and protein-corrected milk, BW, and body condition score. Five-trait random regression models were used to estimate the individual variance components of the CH4 and production traits, which were used to calculate the correlations between RFI and CH4 traits throughout lactation. We found positive correlations of RFI with MeP and MeI ranging from 0.05 to 0.47 throughout the lactation. Correlations between RFI and MeY are low and vary from positive to negative, ranging from −0.18 to 0.17. Both MeP and MeI are favorably correlated with RFI, as is MeY during the first half of lactation. These correlations are mostly favorable for genetic selection, but the confirmation of these results is needed with genetic correlations over a larger dataset.

Evaluation of Genetic Parameters and Comparison of Stress Tolerance Traits in Different Strains of Litopenaeus vannamei.

Litopenaeus vannamei stands out globally in aquaculture for its fast growth, broad salt tolerance, disease resistance, and high protein levels. Selective breeding requires the precise estimation of the variance components and genetic parameters for important traits. This study formed lineages from 20 full sibling families of L. vannamei, with progenitors from Thailand and the USA. We then assessed the genetic resilience traits of juvenile shrimp from these families to high ammonia-N, high pH, and low salinity by performing a 96 h acute toxicity test. Mortality rates for the families under 96 h exposure to high ammonia-N, high pH, and low salinity were 19.52-92.22%, 23.33-92.22%, and 19.33-80.00%, respectively, showing significant variance in stress tolerance among families (p < 0.05). Survival heritability estimates, using threshold male and female models, were 0.44 ± 0.12 in high ammonia-N, 0.41 ± 0.12 in high pH, and 0.27 ± 0.08 in low salinity, respectively. Genetic correlations between growth and stress resistance traits varied from 0.0137 ± 0.2406 to 0.8327 ± 0.0781, and phenotypic correlations ranged from 0.0019 ± 0.0590 to 0.6959 ± 0.0107, indicating a low-to-high positive correlation significant at (p < 0.05). It was found that the survival rate of families No. 2 and No. 9 was higher under high ammonia-N and high pH stresses, while the survival rate of family No. 10 was higher under low salinity stress after comparing two selection criteria, the breeding values and phenotypic values. Thus, these three families are identified as potential breeding program candidates. Through the creation of a genetic parameter estimation model, the genetic variances across mating combinations for stress resistance traits were obtained and families with heightened stress resistance were identified, laying the groundwork for enhanced genetic selection of L. vannamei.

Estimating the Rate of Mutation to a Mutator Phenotype

A mutator is a variant in a population of organisms whose mutation rate is higher than the average mutation rate in the population. For genetic and population dynamics reasons, mutators are produced and survive with much greater frequency than anti-mutators (variants with a lower-than-average mutation rate). This strong asymmetry is a consequence of both fundamental genetics and natural selection; it can lead to a ratchet-like increase in the mutation rate. The rate at which mutators appear is, therefore, a parameter that should be of great interest to evolutionary biologists generally; for example, it can influence: (1) the survival duration of a species, especially asexual species (which are known to be short-lived), (2) the evolution of recombination, a process that can ameliorate the deleterious effects of mutator abundance, (3) the rate at which cancer appears, (4) the ability of pathogens to escape immune surveillance in their hosts, (5) the long-term fate of mitochondria, etc. In spite of its great relevance to basic and applied science, the rate of mutation to a mutator phenotype continues to be essentially unknown. The reasons for this gap in our knowledge are largely methodological; in general, a mutator phenotype cannot be observed directly, but must instead be inferred from the numbers of some neutral “marker” mutation that can be observed directly: different mutation-rate variants will produce this marker mutation at different rates. Here, we derive the expected distribution of the numbers of the marker mutants observed, accounting for the fact that some of the mutants will have been produced by a mutator phenotype that itself arose by mutation during the growth of the culture. These developments, together with previous enhancements of the Luria–Delbrück assay (by one of us, dubbed the “Jones protocol”), make possible a novel experimental protocol for estimating the rate of mutation to a mutator phenotype. Simulated experiments using biologically reasonable parameters that employ this protocol show that such experiments in the lab can give us fairly accurate estimates of the rate of mutation to a mutator phenotype. Although our ability to estimate mutation-to-mutator rates from simulated experiments is promising, we view this study as a proof-of-concept study and an important first step towards practical empirical estimation.

Analysis of nonsynonymous SNPs in candidate genes that influence bovine temperament and evaluation of their effect in Brahman cattle.

Temperament is an important production trait in cattle and multiple strategies had been developed to generate molecular markers to assist animal selection. As nonsynonymous single nucleotide polymorphisms are markers with the potential to affect gene functions, they could be useful to predict phenotypic effects. Genetic selection of less stress-responsive, temperamental animals is desirable from an economic and welfare point of view. Two nonsynonymous single nucleotide polymorphisms identified in HTR1B and SLC18A2 candidate genes for temperament were analyzed in silico to determine their effects on protein structure. Those nsSNPs allowing changes in proteins were selected for a temperament association analysis in a Brahman population. Transversion effects on protein structure were evaluated in silico for each amino acid change model, revealing structural changes in the proteins of the HTR1B and SLC18A2 genes. The selected nsSNPs were genotyped in a Brahman population (n = 138), and their genotypic effects on three temperament traits were analyzed: exit velocity, pen score, and temperament score. Only the SNP rs209984404-HTR1B (C/A) showed a significant association (P = 0.0144) with pen score. The heterozygous genotype showed a pen score value 1.17 points lower than that of the homozygous CC genotype. The results showed that in silico analysis could direct the selection of nsSNPs with the potential to change the protein. Non-synonymous single nucleotide polymorphisms causing structural changes and reduced protein stability were identified. Only rs209984404-HTR1B shows that the allele affecting protein stability was associated with the genotype linked to docility in cattle.

An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows

Genetic selection has remarkably helped U.S. dairy farms to decrease their carbon footprint by more than doubling milk production per cow over time. Despite the environmental and economic benefits of improved feed and milk production efficiency, there is a critical need to explore phenotypical variance for feed utilization to advance the long-term sustainability of dairy farms. Feed is a major expense in dairy operations, and their enteric fermentation is a major source of greenhouse gases in agriculture. The challenges to expanding the phenotypic database, especially for feed efficiency predictions, and the lack of understanding of its drivers limit its utilization. Herein, we leveraged an artificial intelligence approach with feature engineering and ensemble methods to explore the predictive power of the rumen microbiome for feed and milk production efficiency traits, as rumen microbes play a central role in physiological responses in dairy cows. The novel ensemble method allowed to further identify key microbes linked to the efficiency measures. We used a population of 454 genotyped Holstein cows in the U.S. and Canada with individually measured feed and milk production efficiency phenotypes. The study underscored that the rumen microbiome is a major driver of residual feed intake (RFI), the most robust feed efficiency measure evaluated in the study, accounting for 36% of its variation. Further analyses showed that several alpha-diversity metrics were lower in more feed-efficient cows. For RFI, [Ruminococcus] gauvreauii group was the only genus positively associated with an improved feed efficiency status while seven other taxa were associated with inefficiency. The study also highlights that the rumen microbiome is pivotal for the unexplained variance in milk fat and protein production efficiency. Estimation of the carbon footprint of these cows shows that selection for better RFI could reduce up to 5 kg of diet consumed per cow daily, potentially reducing up to 37.5% of CH4. These findings shed light that the integration of artificial intelligence approaches, microbiology, and ruminant nutrition can be a path to further advance our understanding of the rumen microbiome on nutrient requirements and lactation performance of dairy cows to support the long-term sustainability of the dairy community.

A step-by-step, multidisciplinary strategy to maximize the yield of genetic testing in pediatric patients with chronic kidney diseases.

The use of genetic testing in pediatric patients with chronic kidney diseases (CKD) has increased exponentially in the past few years, particularly with the emergence of novel sequencing techniques. However, the genetic yield remains unexpectedly low in nephrology, with an impact on diagnosis, prognosis and treatment. Moreover, the increasing diversity of genetic testing possibilities can be seen as an obstacle by clinicians, in the absence of a strong background in genetics. Here, we propose a step-by-step, multidisciplinary strategy for the diagnostic evaluation of pediatric patients with CKD, and appropriate genetic test selection to maximize the yield of genetic testing. A total of 126 pediatric patients were enrolled in a retrospective file analysis. Genetic testing techniques used included phenotype-associated next-generation panel sequencing (N = 41), Sanger and SNaPshot sequencing (N = 3) and/or whole exome sequencing (N = 2). Overall genetic yield reached 63% and genetic testing significantly impacted patient management in 70%. The distribution of kidney diseases among patients was balanced and matched previously described pediatric cohorts in terms of glomerulopathies, tubulopathies and ciliopathies. Genetic analyses led to significant treatment modifications, kidney biopsy sparing and personalized nephroprotection, as well as tailored genetic counseling. Of note, the evaluation of Human Phenotype Ontology term accuracy in the cohort showed that causal mutations were precisely identified in 85% of the patients at most. Here we suggest a step-by-step, multidisciplinary strategy to maximize the yield of genetic testing in pediatric patients with CKD. This approach optimizes patient care while avoiding unnecessary treatments or procedures.

Genetic Clustering Algorithm-Based Feature Selection and Divergent Random Forest for Multiclass Cancer Classification Using Gene Expression Data

AbstractComputational identification and classification of clinical disorders gather major importance due to the effective improvement of machine learning methodologies. Cancer identification and classification are essential clinical areas to address, where accurate classification for multiple types of cancer is still in a progressive stage. In this article, we propose a multiclass cancer classification model that categorizes the five different types of cancers using gene expression data. To perform efficient analysis of the available clinical data, we propose feature selection and classification methods. We propose a genetic clustering algorithm (GCA) for optimal feature selection from the RNA-gene expression data, consisting of 801 samples belonging to the five major classes of cancer. The proposed feature selection method reduces the 1621 gene expressions into a cluster of 21 features. The optimum feature set acts as input data to the proposed divergent random forest. Based on the features computed, the proposed classifier categorizes the data samples into 5 different classes of cancers, including breast cancer, colon cancer, kidney cancer, lung cancer, and prostate cancer. The proposed divergent random forest provided performance improvisation in terms of accuracy with 95.21%, specificity with 93%, and sensitivity with 94.29% which outperformed all the other existing multiclass classification algorithms.

Genetic influences on depression and selection into adverse life experiences

Genome-wide association studies find that a large number of genetic variants jointly influence the risk of depression, which is summarized by polygenic indices (PGIs) of depressive symptoms and major depression. But PGIs by design remain agnostic about the causal mechanisms linking genes to depression. Meanwhile, the role of adverse life experiences in shaping depression risk is well-documented, including via gene-environment correlation. Building on theoretical work on dynamic and contingent genetic selection, we suggest that genetic influences may lead to differential selection into negative life experiences, forging gene-environment correlations that manifest in various permutations of depressive behaviors and environmental adversities. We also examine the extent to which apparent genetic influences may reflect spurious associations due to factors such as indirect genetic effects. Using data from two large surveys of middle-aged and older US adults, we investigate to what extent a PGI of depression predicts the risk of 27 different adversities. Further, to glean insights about the kinds of processes that might lead to gene-environment correlation, we augment these analyses with data from an original preregistered survey to measure cultural understandings of the behavioral dependence of various adversities. We find that the PGI predicts the risk of majority of adversities, net of class background and prior depression, and that the selection risk is greater for adversities typically perceived as being dependent on peoples’ own behaviors. Taken together, our findings suggest that the PGI of depression largely picks up the risk of behaviorally-influenced adversities, but to a lesser degree also captures other environmental influences. The results invite further exploration into the behavioral and interactional processes that lie along the pathways intervening between genetic differences and wellbeing.

Disruption of the bacterial OLE RNP complex impairs growth on alternative carbon sources.

Ornate, large, extremophilic (OLE) RNAs comprise a class of large noncoding RNAs in bacteria whose members form a membrane-associated ribonucleoprotein (RNP) complex. This complex facilitates cellular adaptation to diverse stresses such as exposure to cold, short-chain alcohols, and elevated Mg2+ concentrations. Here, we report additional phenotypes exhibited by Halalkalibacterium halodurans (formerly called Bacillus halodurans) strains lacking functional OLE RNP complexes. Genetic disruption of the complex causes restricted growth compared to wild-type cells when cultured in minimal media (MM) wherein glucose is replaced with alternative carbon/energy sources. Genetic suppressor selections conducted in glutamate MM yielded isolates that carry mutations in or near genes relevant to Mn2+ homeostasis (ykoY and mntB), phosphate homeostasis (phoR), and putative multidrug resistance (bmrCD). These functional links between OLE RNA, carbon/energy management, and other fundamental processes including protein secretion are consistent with the hypothesis that the OLE RNP complex is a major contributor to cellular adaptation to unfavorable growth conditions.

An integrated QTL mapping and transcriptome sequencing provides further molecular insights and candidate genes for stem strength in rapeseed (Brassica napus L.).

We detected the major QTL- qSR.A07, which regulated stem strength and was fine-mapped to 490 kb. BnaA07G0302800ZS and BnaA07G0305700ZS as the candidate functional genes were identified at qSR.A07 locus. The stem's mechanical properties reflect its ability to resist lodging. In rapeseed (Brassica napus L.), although stem lodging negatively affects yield and generates harvesting difficulties, the molecular regulation of stem strength remains elusive. Hence, this study aimed to unravel the main loci and molecular mechanisms governing rapeseed stem strength. A mapping population consisting of 267 RILs (recombinant inbred lines) was developed from the crossed between ZS11 (high stem strength) and 4D122 (low stem strength), and two mechanical properties of stems including stem breaking strength and stem rind penetrometer resistance were phenotyped in four different environments. Four pleiotropic QTLs that were stable in at least two environments were detected. qSR.A07, the major one, was fine-mapped to a 490kb interval between markers SA7-2711 and SA7-2760 on chromosome 7. It displayed epistatic interaction with qRPR.A09-2. Comparative transcriptome sequencing and analysis unveiled methionine/S-adenosylmethionine cycle (Met/SAM cycle), cytoskeleton organization, sulfur metabolism and phenylpropanoid biosynthesis as the main pathways associated with high stem strength. Further, we identified two candidate genes, BnaA07G0302800ZS and BnaA07G0305700ZS, at qSR.A07 locus. Gene sequence alignment identified a number of InDels, SNPs and amino acid variants in sequences of these genes between ZS11 and 4D122. Finally, based on these genetic variants, we developed three SNP markers of these genes to facilitate future genetic selection and functional studies. These findings offer important genetic resources for the molecular-assisted breeding of novel rapeseed stem lodging-resistant varieties.

Novel phenotypes of feeding and social behaviour and their relationship with individual rabbit growth and feed efficiency

Due to the lack of a recording system for individual consumption of group-housed rabbits, published studies about feeding behaviour are based on information recorded at the group- and not at the individual level and periods covering only a few days or, in some cases, only part of a day. Such information could be used to inform rabbit management systems but cannot be used for genetic selection. We aimed to generate and use information from a novel automated feeder for group-housed rabbits to identify new phenotypes for individual animals that could be incorporated into breeding programs to improve feed efficiency and social behaviour under different feeding regimens. At 39 d of age, rabbits from 15 batches were placed in cages and fed ad libitum to become used to the electronic feeder. From 42 to 58–59 d, one group of 1 086 rabbits was fed ad libitum (AL), while another group of 1 134 rabbits was fed on a restricted feeding schedule (R) by limiting the feeding time to the period between 1800 and 0600 h of the following day. We implemented a reliable multivariate method to remove anomalous feeding behaviour records. We then defined novel traits for feeding behaviour that apply to both types of feeding regimes, and for social behaviour that indicates an animal’s rank within the cage hierarchy. We based these traits on feeder records and a biologically sound definition of a meal. Finally, we estimated the phenotypic correlations of those traits with growth and feed efficiency traits. Our findings demonstrate that variables about resource distribution among cage mates and an animal's priority for feed access were found to be good indicators of an animal's dominant or subordinate status within the cage. Based on results obtained in R animals (results were similar in AL animals), the most efficient animals were those that ate less frequently (phenotypic correlation with feed conversion ratio, rho = 0.6), and consumed smaller amounts per meal (rho = 0.7), spent less time at the feeder (rho = 0.4), and appeared to be subordinate, as they did not have priority access to the feeder (rho = −0.3), and had the smallest share of resources (range of rho = 0.2–0.6). We conclude that quantifying feeding and social behaviour traits can enhance the understanding of the mechanisms through which individuals exert their effects on the performance of their cage mates.