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PubMed2006

Use of routinely collected amniotic fluid for whole-genome expression analysis of polygenic disorders.

BACKGROUND: Neural tube defects related to polygenic disorders are the second most common birth defects in the world, but no molecular biologic tests are available to analyze the genes involved in the pathomechanism of these disorders. We explored the use of routinely collected amniotic fluid to characterize the differential gene expression profiles of polygenic disorders. METHODS: We used oligonucleotide microarrays to analyze amniotic fluid samples obtained from pregnant women carrying fetuses with neural tube defects diagnosed during ultrasound examination. The control samples were obtained from pregnant women who underwent routine genetic amniocentesis because of advanced maternal age (>35 years). We also investigated specific folate-related genes because maternal periconceptional folic acid supplementation has been found to have a protective effect with respect to neural tube defects. RESULTS: Fetal mRNA from amniocytes was successfully isolated, amplified, labeled, and hybridized to whole-genome transcript arrays. We detected differential gene expression profiles between cases and controls. Highlighted genes such as SLA, LST1, and BENE might be important in the development of neural tube defects. None of the specific folate-related genes were in the top 100 associated transcripts. CONCLUSIONS: This pilot study demonstrated that a routinely collected amount of amniotic fluid (as small as 6 mL) can provide sufficient RNA to successfully hybridize to expression arrays. Analysis of the differences in fetal gene expressions might help us decipher the complex genetic background of polygenic disorders.

Amniotic Fluid↗
PubMed2026

Methods for modeling gene-environment interplay using polygenic risk scores.

Polygenic risk scores (PRS) are increasingly recognized as pivotal tools for quantifying disease risk through the aggregation of multiple genetic variants. As sample sizes in genome-wide association studies (GWAS) continue to expand and PRS become more powerful, they are set to play a key role in translational research and personalized medicine. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in diverse contexts. This interplay manifests in two forms: PRS-by-environment interaction (PRS × E) and gene-environment correlation (rGE). However, despite the growing application and importance of PRS, there are limited guidelines for performing PRS × E interaction analyses while controlling for rGE, which can lead to inconsistencies across studies and misinterpretation of results. Here we provide a review of different methods for performing PRSxE interaction in various epidemiological study designs, propose recommendations for best-practice, and discuss future challenges.

Gene-Environment Interaction↗
PubMed2003

Candidate gene case-control studies.

Two main approaches to the identification of genes are involved in polygenic diseases. Use of family studies has generally been the preferred approach up until recently, but this is only feasible if the genetic component of the disease is relatively strong and DNA samples are available from other family members. Population case-control studies are useful both as an alternative and an adjunct to family studies. These can involve either whole genome scanning or candidate gene approaches. While whole genome scanning is likely to be widely used in the future once more information on genome-wide single nucleotide polymorphism distributions is available, at present, candidate gene studies are more feasible. When performing candidate gene case-control studies factors such as study design, methods for recruitment of case and controls, selection of candidate genes, functional significance of polymorphisms chosen for study and statistical analysis require close attention to ensure that only genuine associations are detected. Some examples of the successful use of candidate gene case-control studies are discussed and, to illustrate some potential problems in the design and interpretation of association studies, some specific examples of association studies on cancer are considered.

Case-Control Studies↗
PubMed2003

Minor physical anomalies in schizophrenic patients and normal controls.

The aim of the study is to investigate the rate and topographical pattern of minor physical anomalies in schizophrenic patients and normal subjects and determine their value in predicting the patient-control status. Seventy-six schizophrenic inpatients (43 men, 33 women) and 82 normal control subjects (42 men, 40 women) were examined for minor physical anomalies on the Waldrop scale. Schizophrenics showed a higher rate for almost all examined anomalies, the differences reaching statistical significance for six of them: fine electric hair, epicanthus, high/steepled palate, tongue with smooth/rough spots, third toe the second, and big gap between I and II toes. They have significantly higher values for 5 out of 6 body regions and for the total anomalies score. Anomalies in schizophrenics show higher prevalence in the craniofacial complex than the periphery, but the periphery is also considerably stigmatized. Seven anomalies distinguish patients from controls, classifying correctly 81.6% of the patients and 82.9% of the controls. Some anomalies show an almost equal rate in the schizophrenics and the controls, while the rate of others is more than 10 times greater in the patients (odds ratios range: 1.0 to 10.9). Viewed within the multifactorial-polygenic threshold model of liability to a disease, minor physical anomalies might reflect a type of neurodevelopmental risk factor, which by interaction with other genetic or environmental factors could result in passing a threshold and producing symptoms of the disorder, at least in one subpopulation of schizophrenics.

Adolescent↗
PubMed2004

Common genetic effects on variation in impulsivity and activity in mice.

Impulsivity is a complex psychological construct that impacts on behavioral predispositions in the normal range and has been shown to have a genetic element through the examination of hereditary patterns of abnormal conditions such as attention deficit/hyperactivity disorder and obsessive compulsive disorder. In this study, we took advantage of the isogenic nature of inbred strains of mice to determine the contribution of genes to impulsive behaviors by examining the performance of four separate mouse strains in a novel murine delayed-reinforcement paradigm, during which the animals had to choose between rewards that were relatively small but available immediately and larger but progressively delayed rewards. To control for maternal effects, all the mice were cross-fostered to a common strain immediately after birth. Under these conditions, we found significant differences between the strains on behaviors indexing impulsive choice and on independent measures of locomotor activity, which subsequent heritability analysis showed could be related, in part, to genetic effects. Moreover, the two aspects of behavior were found to co-vary, with the more active animals also displaying more impulsive behavior. This was not attributable to mundane confounds related to individual task requirements but instead indicated the existence of common genetic factors influencing variation in both impulsivity and locomotor activity. The data are discussed in terms of the coexistence of impulsivity and hyperactivity, interactions between environmental and genetic effects, and possible candidate genes.

Animals↗
PubMed2004

Joint mapping of quantitative trait Loci for multiple binary characters.

Joint mapping for multiple quantitative traits has shed new light on genetic mapping by pinpointing pleiotropic effects and close linkage. Joint mapping also can improve statistical power of QTL detection. However, such a joint mapping procedure has not been available for discrete traits. Most disease resistance traits are measured as one or more discrete characters. These discrete characters are often correlated. Joint mapping for multiple binary disease traits may provide an opportunity to explore pleiotropic effects and increase the statistical power of detecting disease loci. We develop a maximum-likelihood method for mapping multiple binary traits. We postulate a set of multivariate normal disease liabilities, each contributing to the phenotypic variance of one disease trait. The underlying liabilities are linked to the binary phenotypes through some underlying thresholds. The new method actually maps loci for the variation of multivariate normal liabilities. As a result, we are able to take advantage of existing methods of joint mapping for quantitative traits. We treat the multivariate liabilities as missing values so that an expectation-maximization (EM) algorithm can be applied here. We also extend the method to joint mapping for both discrete and continuous traits. Efficiency of the method is demonstrated using simulated data. We also apply the new method to a set of real data and detect several loci responsible for blast resistance in rice.

Algorithms↗
PubMed2006

Relaxed significance criteria for linkage analysis.

Linkage analysis involves performing significance tests at many loci located throughout the genome. Traditional criteria for declaring a linkage statistically significant have been formulated with the goal of controlling the rate at which any single false positive occurs, called the genomewise error rate (GWER). As complex traits have become the focus of linkage analysis, it is increasingly common to expect that a number of loci are truly linked to the trait. This is especially true in mapping quantitative trait loci (QTL), where sometimes dozens of QTL may exist. Therefore, alternatives to the strict goal of preventing any single false positive have recently been explored, such as the false discovery rate (FDR) criterion. Here, we characterize some of the challenges that arise when defining relaxed significance criteria that allow for at least one false positive linkage to occur. In particular, we show that the FDR suffers from several problems when applied to linkage analysis of a single trait. We therefore conclude that the general applicability of FDR for declaring significant linkages in the analysis of a single trait is dubious. Instead, we propose a significance criterion that is more relaxed than the traditional GWER, but does not appear to suffer from the problems of the FDR. A generalized version of the GWER is proposed, called GWERk, that allows one to provide a more liberal balance between true positives and false positives at no additional cost in computation or assumptions.

Algorithms↗
PubMed2006

Mapping quantitative trait loci for longitudinal traits in line crosses.

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.

Chromosome Mapping↗
PubMed2006

Genetic and environmental effects on complex traits in mice.

The interaction between genotype and environment is recognized as an important source of experimental variation when complex traits are measured in the mouse, but the magnitude of that interaction has not often been measured. From a study of 2448 genetically heterogeneous mice, we report the heritability of 88 complex traits that include models of human disease (asthma, type 2 diabetes mellitus, obesity, and anxiety) as well as immunological, biochemical, and hematological phenotypes. We show that environmental and physiological covariates are involved in an unexpectedly large number of significant interactions with genetic background. The 15 covariates we examined have a significant effect on behavioral and physiological tests, although they rarely explain >10% of the variation. We found that interaction effects are more frequent and larger than the main effects: half of the interactions explained >20% of the variance and in nine cases exceeded 50%. Our results indicate that assays of gene function using mouse models should take into account interactions between gene and environment.

Animals↗
PubMed2000

Distribution of body weight, blood insulin and lipid levels in the SMXA recombinant inbred strains and the QTL analysis.

In the SMXA recombinant inbred (RI) strains, we measured body weight, blood insulin and lipid (triglyceride, total cholesterol and phospholipid) levels in each strain. In the five traits, mean values of substrains varied remarkably and showed a continuous spectrum of distribution, suggesting control by multiple genes at distinct loci for each trait. We also screened for quantitative trait loci (QTLs) involved in the five traits. Suggestive QTLs for body weight (Chromosomes 1 and 6), insulin (Chromosomes 1, 3, 10 and 17), triglyceride (Chromosomes 4 and 11) and phospholipid (Chromosome 18) levels were detected. The SMXA RI strains are unique tools for analyzing genetic factors that influence body weight, blood insulin and lipids levels.

Animals↗
PubMed1998

Introduction to the post-Human Genome Project era, a target for interactions between polygenic and/or multiphenotypical components in cancer control in South America.

Epidemiological studies have suggested that the propensity to develop malignancy involves a complex mix of genetic and environmental determinants, however both older and innovative techniques display unresolved fundamental questions regarding etiology. Current barriers to achieving the potential benefit from this understanding are: 1) incomplete background on the various environmental and genetic factors involved in the carcinogenesis mechanism; 2) difficulties in accurately differentiating specific molecular subtypes and measuring the effective cellular exposure dose; and 3) difficulties in determining the multifactorial interaction between genetic and environmental factors. To extrapolate Human Genome Project research findings to the Post-Human Genome Project era, South America provides a large population and large-pedigree families, thus including genetically heterogeneous and less heterogeneous groups. An initial strategy might be to trace high risk populations and the respective exposures to which they are susceptible, such as: 1) migration, identifying rural migrant populations; 2) inherent susceptibility, studying "long term homogeneous populations" or large families living in similar rural environments; and 3) dissection of gene-environmental interaction

Environmental Exposure↗
PubMed2002

Bioinformatics and approaches to identifying polygenic susceptibility traits.

The role of genetic factors in periodontal disease is now well recognized, although details for the genetic mechanisms of the disease and implications for therapy can be as obscure as they are for other human traits. This paper addresses the role that the analysis of genome-wide data might play in helping to understand the molecular determinants of periodontal risk. Very few human diseases are not polygenic, in that an individual's susceptibility depends on his or her constitution at many genetic loci, each of which may have a small effect. Not only do these loci interact, but also their actions and interactions depend on nongenetic factors. Much of the statistical machinery to handle this complexity was developed in the plant and animal breeding context, where crosses between inbred lines selected for trait differences could be conducted. Human polygenic studies began with studies on large pedigrees, but have expanded to include case-control analyses of random samples of individuals who differ in disease status, and studies of marker transmissions within nuclear families. In the area of characterizing the genetic architecture of complex traits, the relatively new field of bioinformatics is distinguished from the more mature fields of statistical genetics or genetic epidemiology by its focus on genome-wide data. The very dense sets of genetic markers now available, particularly those at single nucleotide positions (SNPs), have meant that it is possible to seek linkages or associations between chromosomal position and disease from the whole genome in a single study. Apart from the obvious problems of scale, there are real issues involved with multiple testing and recognizing interactions. Current thinking tends to focus on relatively conserved "haplotype blocks" instead of single genetic markers, although there is no consensus on the utility of this emphasis.

Dental Informatics↗
PubMed2006

Rethinking target discovery in polygenic diseases.

Despite an extraordinary investment in R&D the yield of successful new drugs has been disproportionately low in recent years, suggesting that the whole process of drug development requires rethinking and reform. Most analyses on this issue focus on molecular target discovery considerations. Target identification is characterized by a surplus of potential targets, but there is a translational bottleneck primarily due to limitations of currently employed target validation platforms. Meanwhile, the clinical entities, to which treatments are directed, are also highly complex in terms of pathophysiologic mechanisms and manifestations. In the present study we discuss the limitations of current molecular target discovery approaches mainly in regard to selectivity and efficacy. We also describe the constraints imposed on drug development by the current diagnostic constructs and the tendency towards dissecting the complex clinical phenotypes to component intermediate phenotypes. Finally, we describe how the reconsideration of molecular and clinical targets in polygenic diseases may lead to new strategies of pharmacological intervention directed against component dysfunctions, rather than the whole complex phenotype. Such strategies involve the combination of single ligands that act selectively on multiple molecules involved in a particular disease, or the employment of "multi-targeted" drugs, i.e. single drug molecules that hit selectively multiple receptors sharing common binding sites.

Animals↗
PubMed2024

Increased Genetic Risk for β-Cell Failure Is Associated With β-Cell Function Decline in People With Prediabetes.

Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the association of T2D pPS with diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (β-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin, or placebo arms. Associations were tested with general linear models and Cox regression with adjustment for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher β-cell pPS was associated with lower insulinogenic index and corrected insulin response at 1-year follow-up with adjustment for baseline measures (effect per pPS SD -0.04, P = 9.6 × 10-7, and -8.45 μU/mg, P = 5.6 × 10-6, respectively) and with increased diabetes incidence with adjustment for BMI at nominal significance (hazard ratio 1.10 per SD, P = 0.035). The liver/lipid pPS was associated with reduced 1-year baseline-adjusted triglyceride levels (effect per SD -4.37, P = 0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the β-cell cluster pPS had worsening in measures of β-cell function.

Humans↗
PubMed1999

"Quasi-REML" correlation estimates between production and health traits in the presence of selection and confounding: a simulation study.

Performance of the "quasi-REML" method for estimating correlations between a continuous trait and a categorical trait, and between two categorical traits, was studied with Monte Carlo simulations. Three continuous, correlated traits were simulated for identical populations and three scenarios with either no selection, selection for one moderately heritable trait (Trait 1, h2 = .25), and selection for the same trait plus confounding between sires and management groups. The "true" environmental correlations between Traits 2 (h2 = .10) and 3 (h2 = .05) were always of the same absolute size (.20), but further data scenarios were generated by setting the sign of environmental correlation to either positive or negative. Observations for Traits 2 and 3 were then reassigned to binomial categories to simulate health or reproductive traits with incidences of 15 and 5%, respectively. Genetic correlations (r(g12), r(g13), and r(g23) and environmental correlations (r(e12), r(e13), and r(e23)) were estimated for the underlying continuous scale (REML) and the visible categorical scales ("quasi-REML") with linear multiple-trait sire and animal models. Contrary to theory, practically all "quasi-REML" genetic correlations were underestimated to some extent with the sire and animal models. Selection inflated this negative bias for sire model estimates, and the sign of r(e23) noticeably affected r(g23) estimates for the animal model, with greater bias and SD for estimates when the "true" r(e23) was positive. Transformed "quasi-REML" environmental correlations between a continuous and a categorical trait were estimated with good efficiency and little bias, and corresponding correlations between two categorical traits were systematically overestimated. Confounding between sires and contemporary groups negatively affected all correlation estimates on the underlying and the visible scales, especially for sire model "quasi-REML" estimates of genetic correlation. Selection, data structure, and the (co)variance structure influences how well correlations involving categorical traits are estimated with "quasi-REML" methods.

Animal Husbandry↗
PubMed2001

A whole-genome scan for quantitative trait loci affecting teat number in pigs.

A whole-genome scan was conducted using 132 microsatellite markers to identify chromosomal regions that have an effect on teat number. For this purpose, an experimental cross between Chinese Meishan pigs and five commercial Dutch pig lines was used. Linkage analyses were performed using interval mapping by regression under line cross models including a test for imprinting effects. The whole-genome scan revealed highly significant evidence for three quantitative trait loci (QTL) affecting teat number, of which two were imprinted. Paternally expressed (i.e., maternally imprinted) QTL were found on chromosomes 2 and 12. A Mendelian expressed QTL was found on chromosome 10. The estimated additive effects showed that, for the QTL on chromosomes 10 and 12, the Meishan allele had a positive effect on teat number, but, for the QTL on chromosome 2, the Meishan allele had a negative effect on teat number. This study shows that imprinting may play an important role in the expression of teat number.

Animals↗