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PubMed2005

Locomotion and the pollen hoarding behavioral syndrome of the honeybee (Apis mellifera L.).

Honeybees selected for the colony level phenotype of storing large quantities of pollen (pollen hoarding) in the nest exhibit greater walking activity than those selected against pollen hoarding. In this study, we use a simple walking assay to demonstrate that walking activity increases with the proportion of high pollen-hoarding alleles in pure and backcrossed strains of bees (high-strain bees > offspring generated from a high backcross > offspring generated from a low backcross > low-strain bees). The trait is heritable but is not associated with markers linked to three quantitative trait loci (QTL) mapped for their effects on pollen hoarding with demonstrated pleiotropic effects on pollen and nectar foraging and learning behavior. However, locomotion in non-selected bees is correlated with responsiveness to sucrose, a trait that correlates with foraging and learning behavior. We propose that pollen-hoarding behavior involves a syndrome of behavioral traits with complex genetic and regulatory architectures that span sensory sensitivity, foraging behavior, and learning. We propose that locomotor activity is the component of this syndrome and reflects the early maturation of the bees that become pollen foragers.

Animals↗
PubMed2005

A novel lipoprotein-mediated mechanism controlling sexual attractiveness in a colorful songbird.

Sexually selected traits like complex vocalizations or vibrant colors communicate reliable information about mate quality when they are costly to display. Although several general condition-dependent mechanisms underlying the acquisition of mating advertisements have been identified, we rarely know the precise physiological and molecular challenges that animals must meet to develop their sexual ornaments. The flashy pigment-based colors commonly displayed by birds are ideal candidates for investigating the pathways and demands of sexual-signal expression, because we know the biochemical currency with which the trait is produced. Carotenoid colors in birds, for example, are derived from pigments that are acquired from the diet and assimilated into feathers and bare parts. In previous work, we showed that variation in the sexually attractive red carotenoid-colored beak of male zebra finches (Taeniopygia guttata) was predicted not by the amount of food or pigments ingested, but by the levels of carotenoids that birds circulated in blood. Here we elucidate a novel physiological mechanism by which birds are able to accumulate high levels of carotenoids in the body and develop a colorful bill. Carotenoids are transported through the bloodstream bound to lipoproteins. We assayed a critical component of lipoprotein particles-cholesterol-and found that males with higher cholesterol levels circulated more carotenoids and displayed redder beaks. Experimental supplementation of dietary cholesterol elevated carotenoid levels in the blood and beak hue. Experimental reductions in blood cholesterol, using the human lipid-lowering agent atorvastatin, diminished blood carotenoids and faded the beak; carotenoid and cholesterol levels were restored, however, by subsequent addition of dietary cholesterol. These results suggest that the production of circulating lipoproteins critically regulates the development of a colorful sexually selected trait in zebra finches.

Animals↗
PubMed2004

Quantitative genetic analyses of complex behaviours in Drosophila.

Behaviours are exceptionally complex quantitative traits. Sensitivity to environmental variation and genetic background, the presence of sexual dimorphism, and the widespread functional pleiotropy that is inherent in behavioural phenotypes pose daunting challenges for unravelling their underlying genetics. Drosophila melanogaster provides an attractive system for elucidating the unifying principles of the genetic architectures that drive behaviours, as genetically identical individuals can be reared rapidly in controlled environments and extensive publicly accessible genetic resources are available. Recent advances in quantitative genetic and functional genomic approaches now enable the extensive characterization of complex genetic networks that mediate behaviours in this important model organism.

Animals↗
PubMed2026

A Landscape of Drosophila melanogaster Disease Models: From Genetic Platforms to Cross-Disease Mechanisms and Translational Research.

Modeling human diseases using the fruit fly (Drosophila melanogaster) has established itself as a cornerstone of functional genomics and preclinical medicine. Despite its anatomical simplicity, the Drosophila genome shares remarkable functional conservation with human disease-related genes, enabling the study of complex physiological traits through accessible tissue models. Furthermore, beyond individual disease models, we propose a framework demonstrating how these diseases converge at common molecular centers, such as the breakdown of protein homeostasis, mitochondrial dysfunction, chronic inflammation, and organ-to-organ communication. Finally, we discuss strategies for integrating the Drosophila platform into drug development pipelines and establishing standards to enhance inter-laboratory reproducibility. Overall, this review highlights the enduring value of fruit flies as a model system, particularly when combined with AI-omics approaches to transform complex biological datasets into actionable therapeutic strategies.

Drosophila↗
PubMed2003

Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease.

Multiple sclerosis (MS) and other T cell-mediated autoimmune diseases develop in individuals carrying a complex susceptibility trait, probably following exposure to various environmental triggers. Owing to the presumed weak influence of single genes on disease predisposition and the recognized genetic heterogeneity of autoimmune disorders in humans, candidate gene searches in MS have been difficult. In an attempt to identify molecular markers indicative of disease status rather than susceptibility genes for MS, we show that gene expression profiling of peripheral blood mononuclear cells by cDNA microarrays can distinguish MS patients from healthy controls. Our findings support the concept that the activation of autoreactive T cells is of primary importance for this complex organ-specific disorder and prompt further investigations on gene expression in peripheral blood cells aimed at characterizing disease phenotypes.

Adult↗
PubMed2001

Meta-analysis for model-free methods.

The intricate nature of complex genetic traits dictates that novel methodologies be developed and utilized to achieve better power, better accuracy, and more favorable balance between type I and type II errors than could be achieved by the traditional methods as they are used in mapping Mendelian traits. Meta-analysis provides one such method for synthesizing information from multiple studies. This has the advantage of being able to pool relatively weak signals from individual studies into a collectively stronger evidence of genetic effects, while at the same time providing a quantitative framework for modeling variability among studies. The traditional lod score measures significance level of a linkage effect in an individual study, and its additive property make it a natural candidate for combining results across independent studies. To incorporate the within-study variation of the linkage effect into the pooled overall measure of genetic effect, the effect sizes (such as the proportion of genes shared identical-by-descent, IBD) should be pooled directly across studies. Traditional regression models and mixed effects models can be used to estimate the overall genetic effect size and its variance, and to test heterogeneity among studies. Our simulation studies show that designing studies with moderate power and pooling their results via meta-analysis may be more cost-effective than large dedicated studies. We believe that, as a newly emerging methodology, the meta-analysis approach has the potential to become an integral part of our toolbox that will expedite the search for complex human disease genes.

Alleles↗
PubMed2024

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans.

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

Humans↗
PubMed2005

Techniques for the identification of genes involved in psychiatric disorders.

OBJECTIVE: Most psychiatric disorders are complex genetic traits involving both genetic and environmental risk factors. This paper aims to review the gene identification strategies being applied by molecular geneticists in their efforts to elucidate the genetic and molecular basis of psychiatric disorders. Future strategies will also be canvassed. METHOD: The psychiatric genetic literature was reviewed to identify current strategies applied to gene identification, with examples provided where available. The future strategies and applications that will arise from genome projects, including the International Haplotype Mapping Project, are also discussed. RESULTS: Many advances in the techniques of gene discovery, and the increasing resources available, are rapidly being adopted by researchers and applied to the complex problem of identifying susceptibility genes for mental illnesses. Perhaps the single most important advance to date is the Human Genome Project and all that has stemmed from the vast quantity of information that this endeavour has provided. With these technological advances and the massive increase of publicly available genetic resources, several genes have recently been implicated in the susceptibility to psychiatric illnesses including schizophrenia and depression. After many years of fruitless endeavours, these recent reports indicate that the labours of researchers in psychiatric genetics are beginning to show exciting results. CONCLUSIONS: Identification of these susceptibility genes holds great promise, with the unravelling of the molecular and biochemical basis of some conditions now being a more realistic and tangible goal. The increasing number of genes being identified augers well for the future treatment of psychiatric disorders. The genes identified, and the pathways of genes and proteins that they implicate, will provide potential novel targets for new therapeutic drugs. Psychiatric genetics appears to be poised for significant advances in our knowledge and understanding of the molecular genetic basis of mental illness.

Chromosome Mapping↗
PubMed2006

Clinical assessment of pathological personality traits.

OBJECTIVE: Research has raised questions about the ability of clinicians to make reliable diagnostic judgments about personality. The aim of this study was to assess the reliability and validity of the dimensional diagnosis of pathological personality traits with the Shedler-Westen Assessment Procedure-200 (SWAP-200) Q sort. METHOD: Two clinician/judges independently described 24 outpatients using the SWAP-200, based on a systematic clinical interview. Treating clinicians described the patients using the SWAP-200 based on their knowledge of the patients over the course of treatment while they were blind to interview data. RESULTS: Interrater reliability was high. Convergent and discriminant validity between interviewers and clinicians was also strong. A procedure recently developed for providing precise estimates of construct validity with contrast analysis applied to correlational data documented strong evidence of validity. CONCLUSIONS: Clinicians and independent interviewers can reliably assess complex personality traits associated with personality pathology using the SWAP-200.

Adult↗
PubMed1999

The genetic mediation of individual differences in sensitivity to pain and its inhibition.

The underlying bases of the considerable interindividual variability in pain-related traits are starting to be revealed. Although the relative importance of genes versus experience in human pain perception remains unclear, rodent populations display large and heritable differences in both nociceptive and analgesic sensitivity. The identification and characterization of particularly divergent populations provides a powerful initial step in the genetic analysis of pain, because these models can be exploited to identify genes contributing to the behavior-level variability. Ultimately, DNA sequence differences representing the differential alleles at pain-relevant genes can be identified. Thus, by using a combination of "top-down" and "bottom-up" strategies, we are now able to genetically dissect even complex biological traits like pain. The present review summarizes the current progress toward these ends in both humans and rodents.

Analgesics↗
PubMed2001

Characterization of publicly available SNPs in the Korean population.

Single-nucleotide polymorphisms (SNPs) are the most abundant form of genetic variations and have a great potential for mapping studies of complex genetic traits. Currently a great deal of effort is invested in the identification of SNPs, and a large volume of data is already available through public databases (NCBI, NCI, WICGR, HGBASE). For an association mapping study, SNP allele frequencies in the population are critical. As an initial step toward construction of an SNP database of the Korean population, we have determined the allele frequencies of 300 cSNPs selected from the public database in 24 individuals. Among the tested markers, approximately 23% did not show polymorphism in the population. The results suggest that the ethnic and population based differences should be considered in the selection of SNPs for the study of complex diseases with association mapping methods.

Alleles↗
PubMed2006

Complex adaptive system models and the genetic analysis of plasma HDL-cholesterol concentration.

Despite remarkable advances in diagnosis and therapy, ischemic heart disease (IHD) remains a leading cause of morbidity and mortality in industrialized countries. Recent efforts to estimate the influence of genetic variation on IHD risk have focused on predicting individual plasma high-density lipoprotein cholesterol (HDL-C) concentration. Plasma HDL-C concentration (mg/dl), a quantitative risk factor for IHD, has a complex multifactorial etiology that involves the actions of many genes. Single gene variations may be necessary but are not individually sufficient to predict a statistically significant increase in risk of disease. The complexity of phenotype-genotype-environment relationships involved in determining plasma HDL-C concentration has challenged commonly held assumptions about genetic causation and has led to the question of which combination of variations, in which subset of genes, in which environmental strata of a particular population significantly improves our ability to predict high or low risk phenotypes. We document the limitations of inferences from genetic research based on commonly accepted biological models, consider how evidence for real-world dynamical interactions between HDL-C determinants challenges the simplifying assumptions implicit in traditional linear statistical genetic models, and conclude by considering research options for evaluating the utility of genetic information in predicting traits with complex etiologies.

Adaptation, Physiological↗
PubMed2004

Coordinated genetic regulation of growth and lignin revealed by quantitative trait locus analysis of cDNA microarray data in an interspecific backcross of eucalyptus.

Phenotypic, genotypic, and transcript level (microarray) data from an interspecific backcross population of Eucalyptus grandis and Eucalyptus globulus were integrated to dissect the genetic and metabolic network underlying growth variation. Transcript abundance, measured for 2,608 genes in the differentiating xylem of a 91 (E. grandis x E. globulus) x E. grandis backcross progeny was correlated with diameter variation, revealing coordinated down-regulation of genes encoding enzymes of the lignin biosynthesis and associated methylation pathways in fast growing individuals. Lignin analysis of wood samples confirmed the content and quality predicted by the transcript levels measured on the microarrays. Quantitative trait locus (QTL) analysis of transcript levels of lignin-related genes showed that their mRNA abundance is regulated by two genetic loci, demonstrating coordinated genetic control over lignin biosynthesis. These two loci colocalize with QTLs for growth, suggesting that the same genomic regions are regulating growth, and lignin content and composition in the progeny. Genetic mapping of the lignin genes revealed that most of the key biosynthetic genes do not colocalize with growth and transcript level QTLs, with the exception of the locus encoding the enzyme S-adenosylmethionine synthase. This study illustrates the power of integrating quantitative analysis of gene expression data and genetic map information to discover genetic and metabolic networks regulating complex biological traits. (Sequence data for this article have been deposited with the EMBL/GenBank data libraries under accession numbers CB 967505 to CB 968059; CD 667988 to CD 670002; CD 670004; CD 670097; CD 670101 to CD 670112; and CD 670114 to CD 670137.)

Base Sequence↗
PubMed1997

Mapping quantitative trait loci for fear-like behaviors in mice.

Two mouse models developed for screening anxiolytic drugs were selected for genetic analysis, namely "wall-seeking" tendency in an open field ("thigmotaxis") and the light-to-dark transition (LD) paradigm, a conflict test. These tests measure differences in naturalistic tendencies of mice to explore a novel environment and to avoid a bright light or the center of an open field. In an F2 intercross of two strains of mice (A/J and C57BL/6J) that differ markedly in these behaviors, we estimated a broad sense heritability ranging from 0.3 to 0.59. With this intercross (n = 518), we have mapped several quantitative trait loci (QTL) for these behaviors by performing a genome-wide search. A significant QTL on chromosome 10 (near D10Mit237; LOD of 9.3) that affects LD behavior was identified, and suggestive QTL (LOD > 2.8) were mapped to chromosomes 6, 15, 19, and X. For center time behaviors, QTL were identified on chromosome 1 (LOD of 7.7 and 4.0 for the initial 5-min epoch and the first trial average of the next two 5-min epochs, respectively), and suggestive QTL (LOD > 2.8) were mapped to chromosomes 6 and 14. These QTL individually explain from 2.3 to 8.4% of the phenotypic variance. Collectively, the multiple independent QTL explain from 3.5 to 26.5% of the F2 population's phenotypic variance, depending on the trait. The complexity and heterogeneity of the genetic factors underlying these fear-like behaviors are illustrated by the lack of shared QTL between paradigms and by mapping different QTL for repeated trials of behavior. The identification of QTL affecting individual differences in fear-like behavior may lead to the identification of new gene products and pathways that modulate behavior, providing targets for rational drug design.

Animals↗
PubMed2000

Genetics of systemic autoimmunity in mouse models of lupus.

Systemic lupus erythematosus (SLE) is inherited as a complex polygenic trait, involving genetic, environmental and stochastic factors. Although definition of these etiologic processes has been elusive, solid progress has been made toward elucidating the genetic basis for susceptibility. Herein, we summarize our genome wide mapping effort that has defined loci for component phenotypes for lupus-prone NZB, NZW, MRL-Fas(lpr) and BXSB strains. With this framework in place, identification of the specific genetic alterations and mechanisms is now proceeding through the generation of interval congenic lines, precise mapping and screening of candidate genes. In addition to this approach, transgenic and gene knockout studies have begun to identify genes that can induce or modify autoimmunity in nonautoimmune and lupus-prone background mice, including studies by us and others on Th1 and Th2 cytokine genes in lupus. It is apparent that a diversity of genes and mechanisms can independently or in combination promote systemic autoimmunity in mice. This complexity, which is also observed in human lupus, emphasizes the importance of using experimental and less complex mouse models to define these processes, a tactic that has already yielded new insights. With current technologies and the anticipated definition of mammalian genomes, identification of genes predisposing to lupus and elucidation of processes critical for disease pathogenesis appear within grasp.

Animals↗
PubMed2006

Population isolates in South Tyrol and their value for genetic dissection of complex diseases.

The study of genetic isolates is a promising approach for the study of complex genetic traits. The small and constant population size, lack of migration, and multiple relationships between individuals in the isolate population could reduce the genetic diversity, and lead to increased levels of linkage disequilibrium (LD). We studied the extent of LD on Xq13 in six population isolates from South Tyrol in the Eastern Italian Alps. We found different levels of LD in our study samples, probably reflecting their degrees of isolation and their demographic histories. The highest values were obtained in Val Gardena (ranking among the highest levels of LD in Europe) and in Stelvio, which qualified as a microisolate according to historical information, and biodemographic and genealogical criteria. Phylogenetic analysis revealed that the two Ladin-speaking populations are genetically distant from each other, and from their German-speaking neighbours, and are characterized by a smaller effective population size than the neighbouring valleys. These peculiar characteristics suggest that South Tyrol could be a unique resource for the study of complex diseases, showing all the characteristics of isolated populations with the advantage of including, in a fairly homogeneous environment, two genetically differentiated sub-populations. This could allow investigators to gain an insight into the contribution of genetic heterogeneity in complex diseases.

Adult↗
PubMed1996

Production of congenic mouse strains carrying genomic intervals containing SLE-susceptibility genes derived from the SLE-prone NZM2410 strain.

Systemic lupus erythematosus is inherited as a complex polygenic trait. Four genomic intervals containing major SLE-susceptibility loci were previously identified by interval mapping in the NZM2410 mouse model. In this paper, we utilized a marker-assisted selection protocol to produce four congenic mouse strains, each carrying an NZM2410-derived SLE-susceptibility interval on a C57BL/6-resistant background. Each strain carries only one susceptibility allele derived from this polygenic model and consequently can be used to characterize the specific component phenotypes contributed by individual SLE-susceptibility genes. We illustrate the efficacy of this approach with phenotypic data for one of our congenic strains, B6.NZMH2(z). Our results indicate that this single genomic interval from Chromosome (Chr) 17 of NZM2410 can mediate increased levels of IgG autoantibodies specific for chromatin and that, similar to results obtained in our original genetic cross, B6.NZMH2(z/b) heterozygotes are more prone than B6.NZMH2(z) homozygotes to the development of humoral autoimmunity to nuclear antigens. These results illustrate the feasibility of using congenic strains to dissect the complex pathogenic mechanisms that mediate polygenic SLE. These congenic strains will be valuable tools in the genetic analysis of SLE susceptibility. In future studies, these congenic strains will be interbred to produce bi- and tri-congenic strains in order to assess the role of genetic interactions in the expression of specific components of SLE pathogenesis. They will also be instrumental to the positional cloning and identification of the genes responsible for SLE susceptibility, via the production of congenic recombinants.

Animals↗
PubMed2025

A SuperLearner-based pipeline for the development of DNA methylation-derived predictors of phenotypic traits.

BACKGROUND: DNA methylation (DNAm) provides a window to characterize the impacts of environmental exposures and the biological aging process. Epigenetic clocks are often trained on DNAm using penalized regression of CpG sites, but recent evidence suggests potential benefits of training epigenetic predictors on principal components. METHODOLOGY/FINDINGS: We developed a pipeline to simultaneously train three epigenetic predictors; a traditional CpG Clock, a PCA Clock, and a SuperLearner PCA Clock (SL PCA). We gathered publicly available DNAm datasets to generate i) a novel childhood epigenetic clock, ii) a reconstructed Hannum adult blood clock, and iii) as a proof of concept, a predictor of polybrominated biphenyl exposure using the three developmental methodologies. We used correlation coefficients and median absolute error to assess fit between predicted and observed measures, as well as agreement between duplicates. The SL PCA clocks improved fit with observed phenotypes relative to the PCA clocks or CpG clocks across several datasets. We found evidence for higher agreement between duplicate samples run on alternate DNAm arrays when using SL PCA clocks relative to traditional methods. Analyses examining associations between relevant exposures and epigenetic age acceleration (EAA) produced more precise effect estimates when using predictions derived from SL PCA clocks. CONCLUSIONS: We introduce a novel method for the development of DNAm-based predictors that combines the improved reliability conferred by training on principal components with advanced ensemble-based machine learning. Coupling SuperLearner with PCA in the predictor development process may be especially relevant for studies with longitudinal designs utilizing multiple array types, as well as for the development of predictors of more complex phenotypic traits.

DNA Methylation↗