Showing papers by "Michael A. Province published in 2001"
TL;DR: It is concluded that most early cardiovascular events in a population occur in families with a positive family history of cardiovascular disease, a validated and relatively inexpensive tool for family-based preventive medicine and medical research.
Abstract: Detailed medical family history data have been proposed to be effective in identifying high-risk families for targeted intervention. With use of a validated and standardized quantitative family risk score (FRS), the degree of familial aggregation of coronary heart disease (CHD), stroke, hypertension, and diabetes was obtained from 122,155 Utah families and 6,578 Texas families in the large, population-based Health Family Tree Study, and 1,442 families in the NHLBI Family Heart Study in Massachusetts, Minnesota, North Carolina, and Utah. Utah families with a positive family history of CHD (FRS > or =0.5) represented only 14% of the general population but accounted for 72% of persons with early CHD (men before age 55 years, women before age 65 years) and 48% of CHD at all ages. For strokes, 11% of families with FRS > or =0.5 accounted for 86% of early strokes ( 5,000 families sampled each year in Utah for 14 years demonstrated a gradual decrease in the frequency of a strong positive family history of CHD (-26%/decade) and stroke (-15%/decade) that paralleled a decrease in incidence rates (r = 0.86, p <0.001 for CHD; r = 0.66, p <0.01 for stroke). Because of the collaboration of schools, health departments, and medical schools, the Health Family Tree Study proved to be a highly cost-efficient method for identifying 17,064 CHD-prone families and 13,106 stroke-prone families (at a cost of about $27 per high-risk family) in whom well-established preventive measures can be encouraged. We conclude that most early cardiovascular events in a population occur in families with a positive family history of cardiovascular disease. Family history collection is a validated and relatively inexpensive tool for family-based preventive medicine and medical research.
286 citations
TL;DR: The combined effect of linoleic and linolenic acids was stronger than the individual effects of either fatty acid and had synergistic effects on the prevalence odds ratio of CAD.
234 citations
TL;DR: This first genome-wide scan for abdominal fat assessed by computed tomography indicates that there may be several loci determining the propensity to store fat in the abdominal depot and that some of these loci may influence the development of diabetes in obese subjects.
Abstract: To identify chromosomal regions harboring genes influencing the propensity to store fat in the abdominal area, a genome-wide scan for abdominal fat was performed in the Quebec Family Study. Cross-sectional areas of the amount of abdominal total fat (ATF) and abdominal visceral fat (AVF) were assessed from a computed tomography scan taken at L4-L5 in 521 adult subjects. Abdominal subcutaneous fat (ASF) was obtained by computing the difference between ATF and AVF. The abdominal fat phenotypes were adjusted for age and sex effects as well as for total amount of body fat (kilogram of fat mass) measured by underwater weighing, and the adjusted phenotypes were used in linkage analyses. A total of 293 microsatellite markers spanning the 22 autosomal chromosomes were typed. The average intermarker distance was 11.9 cM. A maximum of 271 sib-pairs were available for single-point (SIBPAL) and 156 families for multipoint variance components (SEGPATH) linkage analyses. The strongest evidence of linkage was found on chromosome 12q24.3 between marker D12S2078 and ASF (logarithm of odds [LOD] = 2.88). Another marker (D12S1045) located within 2 cM of D12S2078 also provided evidence of sib-pair linkage with ASF (P = 0.019), ATF (P = 0.015), and AVF (P = 0.0007). Other regions with highly suggestive evidence (P or =1.75) of multipoint linkage and evidence (P < 0.05) of single-point linkage, all for ASF, included chromosomes 1p11.2, 4q32.1, 9q22.1, 12q22-q23, and 17q21.1. Three of these loci (1p11.2, 9q22.1, and 17q21.1) are close to genes involved in the regulation of sex steroid levels, whereas two others (4q32.1 and 17q21.1) are in the proximity of genes involved in the regulation of food intake. This first genome-wide scan for abdominal fat assessed by computed tomography indicates that there may be several loci determining the propensity to store fat in the abdominal depot and that some of these loci may influence the development of diabetes in obese subjects.
156 citations
TL;DR: In this article, the authors evaluated 1,672 participants in the Hypertension Genetic Epidemiology Network Study to investigate the relations of overweight and level of obesity to LV mass and prevalences of LV hypertrophy, abnormal cardiac output, and peripheral resistance detected using different indexations for body size.
Abstract: The impact of different methods of indexation of left ventricular (LV) mass and systemic hemodynamic variables on prevalences and correlates of cardiovascular abnormalities in relation to level of obesity in populations remains unclear. We evaluated 1,672 participants in the Hypertension Genetic Epidemiology Network Study to investigate the relations of overweight and level of obesity to LV mass and prevalences of LV hypertrophy, abnormal cardiac output, and peripheral resistance detected using different indexations for body size. In our study population, 1,577 subjects were clinically healthy nondiabetic hypertensive and 95 were normotensive normal-weight nondiabetic reference subjects. Fat-free mass (FFM) did not differ between the reference group and the normal-weight hypertensive subjects, and increased with overweight. In hypertensive subjects, LV mass and cardiac output increased and total peripheral resistance decreased with overweight. Indexation of LV mass for FFM or body surface area (BSA) resulted in no difference or even lower prevalence of LV hypertrophy in severely obese compared with normal-weight hypertensive subjects. In contrast, indexation of LV mass for height(2.7) identified an increased prevalence of LV hypertrophy with overweight and obesity. Absolute cardiac output increased and total peripheral resistance decreased with overweight. Prevalence of elevated cardiac output indexed for height(1.83) increased and for elevated total peripheral resistance-height(1.83) index decreased with greater overweight, whereas opposite trends were seen when cardiac output and total peripheral resistance were indexed for BSA or FFM. Thus, in hypertensive subjects, FFM increases with overweight and is directly related to LV mass, stroke volume, and cardiac output, and inversely related to total peripheral resistance. Indexations of LV mass and systemic hemodynamics for FFM or BSA obscured associations of LV hypertrophy and abnormal cardiac and total peripheral resistance indexes with overweight, whereas LV mass/height(2,7), cardiac output/height(1.83), and total peripheral resistance-height(1.83) detected significant preclinical cardiovascular abnormalities with obesity.
128 citations
TL;DR: The results suggest that the submaximal working capacities of sedentary subjects and their responses to endurance training are influenced by familial/genetic factors with a significant contribution of maternal inheritance.
Abstract: PERUSSE. L., J. GAGNON, M. A. PROVINCE, D. C. RAO, J. H. WILMORE, A. S. LEON, C. BOUCHARD, and J. S. SKINNER. Familial aggregation of submaximal aerobic performance in the HERITAGE Family study. Med, Sci Sports Exerc., Vol. 33, No. 4, 2001, pp. 597-604, Purpose: This study examines the contribution of genetic factors to submaximal aerobic performance phenotypes measured before and after 20 wk of endurance training. Methods: Submaximal oxygen consumption (VO 2 ) at three power outputs, 50 W (VO 2 50W), 60% (VO 2 60%) and 80% (VO 2 80%) of VO 2max and power outputs at 60% (PO60%) and 80% (PO80%) of VO 2max were measured during cycle ergometer exercise tests in 483 subjects from 99 white families participating in the HERITAGE Family study. The baseline phenotypes were adjusted for the effects of age, sex, and body mass using stepwise multiple regression procedures. The response phenotypes, computed as the difference (Δ) between the posttraining and baseline measures, were adjusted for age sex, and the baseline value, Results: All submaximal exercise phenotypes measured at baseline and in response to training were characterized by a significant familial resemblance. Maximal heritabilities of the baseline phenotypes range from 48% to 74% with significant spouse, sibling, and parent-offspring correlations. The hypothesis of maternal inheritance where mother-offspring and sibling correlations were forced to be equal was found to fit the data for VO 2 60%. VO 2 80% and PO80%. For the response phenotypes, the maximal heritabilities tended to be lower (23-57%) with a significant maternal inheritance for ΔVO 2 60%, ΔPO60%, and ΔPO80%, Conclusion: These results suggest that the submaximal working capacities of sedentary subjects and their responses to endurance training are influenced by familial/genetic factors with a significant contribution of maternal inheritance.
89 citations
TL;DR: Full genome scans were performed for quantitative lipid measurements in 622 African American and 649 white sibling pairs not taking lipid-lowering medications who were ascertained through the Hypertension Genetic Epidemiology Network (HyperGEN).
Abstract: Full genome scans were performed for quantitative lipid measurements in 622 African American and 649 white sibling pairs not taking lipid-lowering medications who were ascertained through the Hypertension Genetic Epidemiology Network (HyperGEN) of the National Heart, Lung, and Blood Institute (NHLBI) Family Blood Pressure Program. Genotypes for 391 markers spaced roughly equally throughout the genome were typed by the NHLBI Mammalian Genotyping Service. Each of the phenotypes was adjusted for covariates within sex and race and then subjected to variance components linkage analysis, which was performed separately within race by using race-specific marker allele frequencies from additional random samples. The highest lod score detected was 2.77 for logarithmically transformed triglyceride (TG) on chromosome 20 (at 28.6 cM) in the African American sibling pairs. The highest score detected in the white sibling pairs was 2.74 for high density lipoprotein cholesterol on chromosome 5 (at 48.2 cM). Although no scores >3.0 were obtained, positive scores were found in several regions that have been reported in other genome scans in the literature. For example, a score of 1.91 for TG was found on chromosome 15 (at 28.8 cM) in white sibling pairs. This score overlaps the positive findings for TG in 2 other genome scans.
78 citations
TL;DR: Development incorporating linkage analysis into the definition of the regression trees (Shannon et al. , 2000) are discussed, and the pros and cons of recursive partitioning vs the related approach of context-dependent analysis are reviewed as two promising analysis strategies that may be useful for genetic dissection of complex traits.
Abstract: Recursive partitioning/ tree models are discussed as a method of dissecting the complex nature of traits with different causal mechanisms operating in different subsets of the data (e.g., different genes operating in different subsets of families). In addition to the straightforward application of classification and regression trees to define more homogeneous subsets of the data on which to conduct further analysis, developments incorporating linkage analysis into the definition of the regression trees (Shannon et al. , 2000) are discussed. The pros and cons of recursive partitioning vs the related approach of context-dependent analysis (Turner et al. , 1999) are also reviewed as two promising analysis strategies that may be useful for genetic dissection of complex traits.
53 citations
TL;DR: Simulation studies found that partitioning sibpairs into homogeneous subgroups is feasible and significantly increases the power to detect linkage, thus demonstrating the practical utility and potential this new methodology holds.
Abstract: We propose a new splitting rule for recursively partitioning sibpair data into relatively more homogeneous subgroups. This strategy is designed to identify subgroups of sibpairs such that within-subgroup analyses result in increased power to detect linkage using Haseman-Elston regression. We assume that the subgroups can be defined by patterns of non-genetic binary covariates measured on each sibpair. The data we consider consists of the squared difference of a quantitative trait measurement on each sibpair, estimates of identity-by-descent (IBD) values at each genetic marker, and binary covariate data describing characteristics of the sibpair (e.g., race, sex, family history of disease). To test the efficacy of this method in linkage analysis, we performed two simulation experiments. In the first, we simulated a mixture consisting of 66.6% of the sibpairs with no linkage and 33.3% of the sibpairs with genetic linkage to one marker. The two groups were distinguished by the value of a single binary covariate. We also simulated one unlinked marker and one random covariate to include as noise in the data. In the second experiment, we simulated a mixture consisting of 55% of the sibpairs with no genetic linkage, 22.5% of the sibpairs with genetic linkage to one marker, and 22.5% of the sibpairs with linkage to a different marker. Each subgroup was defined by a distinct pattern of two binary covariates. We also simulated one unlinked marker and two random covariates to include as noise in the data. Our simulation studies found that we can significantly increase the overall power to detect linkage by fitting Haseman-Elston regression models to homogeneous subgroups with only a small increase in the false-positive rate. Second, the splitting rule can correctly identify important covariates and linked markers. Third, recursive partitioning of sibpair data using this splitting rule can correctly identify sibpair subgroups. These results indicate that partitioning sibpairs into homogeneous subgroups is feasible and significantly increases the power to detect linkage, thus demonstrating the practical utility and potential this new methodology holds.
50 citations
Book•
01 Jan 2001
TL;DR: The present work presents a meta-analysis of Model-free methods for Linkage Analysis and classification Methods for Confronting Heterogeneity for Complex Inheritance, which aims to clarify the genetic architecture of a Multivariate Phenotype.
Abstract: Genetic Dissection of Complex Traits
Rao Table of Contents Contributors. Preface. Acknowledgments. NEWTON MORTON'S CONTRIBUTIONS Newton Morton: The Wisconsin Years. Newton Morton's Influence on Genetics: The Morton Number. OVERVIEW AND PRELIMINARIES Genetic Dissection of Complex Traits: An Overview. Familial Resemblance and Heritability. Linkage and Association: Basic Concepts. PHENOTYPES AND GENOTYPES Definition of the Phenotype. Genotyping for Human Whole-Genome Scans: Past, Present, and Future. MODEL-BASED METHODS FOR LINKAGE ANALYSIS The Lod Score Method. Extension of the Lod Score: The Mod Score. Major Strengths and Weaknesses of the Lod Score Method. MODEL-FREE METHODS FOR LINKAGE AND ASSOCIATION ANALYSIS Overview of Model-free Methods for Linkage Analysis. Variance Component Methods for Detecting Complex Trait Loci. Linkage and Association with Structural Relationships. The Future of Genetic Case-Control Studies. Cost of Linkage versus Association Methods. Genotype-Environment Interaction in Transmission Disequilibrium Tests. Major Strengths and Weaknesses of Model-free Methods. MORE RECENT METHODS Meta-analysis for Model-free Methods. Classification Methods for Confronting Heterogeneity. Applications of Neural Networks for Gene Finding. Genome Partitioning and Whole-Genome Analysis. Deciphering the Genetic Architecture of a Multivariate Phenotype. OPTIMUM STRATEGIES On the Resolution and Feasibility of Genome Scanning Approaches. One-Stage versus Two-Stage Strategies for Genome Scans. MULTIPLE COMPARISONS AND SIGNIFICANCE LEVELS Significance Levels in Genome Scans. False Positives and False Negatives in Genome Scans. Sequential Methods of Analysis for Genome Scans. CHALLENGES FOR THE NEW MILLENNIUM From Genetics to Mechanism of Disease Liability. Complex Inheritance: The 21st Century. Appendix: Research Contributions of Newton E. Morton. Index.
30 citations
TL;DR: In this paper, the authors exploit the sequential multiple decision procedures (SMDP) theory, which generalizes the standard two-hypotheses tests to consider multiple alternative hypotheses, and develop a single, genome-wide test that simultaneously partitions all markers into signal and noise groups, with tight control over both type I and type II errors.
Abstract: As the preceding chapters illustrate, now that whole-genome scan analyses are becoming more common, there is considerable disagreement about the best way to balance between false positives and false negatives (traditionally called type I and type II errors in the statistical parlance). Type I and type II errors can be simultaneously controlled, if we are willing to let the sample size of analysis vary. This is the secret that Wald 1947 discovered in the 1940s that led to the theory of sequential sampling and was the inspiration for Newton Morton in developing the lod score method. We can exploit this idea further and capitalize on an old, but nearly forgotten theory: sequential multiple decision procedures (SMDP) Bechhoffer, et al. 1968, which generalizes the standard “two-hypotheses” tests to consider multiple alternative hypotheses. Using this theory, we can develop a single, genome-wide test that simultaneously partitions all markers into “signal” and “noise” groups, with tight control over both type I and type II errors ( Province, 2000 ). Conceiving this approach as an analysis tool for fixed sample design (instead of a true sequential sampling scheme), we can let the data decide at which point we should move from the hypothesis generation phase of a genome scan (where multiple comparisons make the interpretation of p values and significance levels difficult and controversial), to a true hypothesis-testing phase (where the problem of multiple comparison of multiple comparison has been all but eliminated so that p values may be accepted at face value.
28 citations
TL;DR: 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 and has the potential to become an integral part of the toolbox that will expedite the search for complex human disease genes.
Abstract: 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.
TL;DR: For linkage analysis of spirometry phenotypes in A1AT deficiency, qualitative phenotypes provided stronger evidence for linkage than quantitative phenotypes, and this study provides guidelines for studies of the genetics of COPD unrelated to A 1AT deficiency.
Abstract: Objectives: Severe alpha 1-antitrypsin (A1AT) deficiency is the one proven genetic risk factor for chronic obstructive pulmonary disease (COPD). Familial aggregation has been demons
University of Bonn1, Indian Statistical Institute2, University of Texas MD Anderson Cancer Center3, French Institute of Health and Medical Research4, University of Texas Health Science Center at Houston5, University of Southampton6, Washington University in St. Louis7, University of California, Irvine8, University of Southern California9
TL;DR: Allegations against Neel are found that these allegations are false and Tierney has grossly misrepresented Neel’s views on a wide range of social implications of modern civilization for the long‐term health of the gene pool.
Abstract: The International Genetic Epidemiology Society (IGES) has examined the charges against James V. Neel and his colleagues contained in the recently published book by Patrick Tierney entitled Darkness in El Dorado: How Scientists and Journalists Devastated the Amazon (W.W. Norton, 2000). The book implicates Neel in causing or promoting an epidemic of measles among the Yanomamo Indians of Venezuela in 1968 leading to "hundreds if not thousands" of deaths by using a "dinosaur" vaccine (Edmonston B) as a deliberate "experiment" to test his "eugenic" theories. Tierney also attempts to link this research, funded by the Atomic Energy Commission (AEC), with a broader tapestry of human radiation experiments. To investigate these serious charges, the IGES undertook a thorough examination of most source documents referenced in Tierney's book, Neel's field logs, notes, first-hand reports, contemporary writings, film sound tracks, etc., and conducted interviews with many relevant persons. The IGES finds that these allegations are false. Neel was not a eugenicist and was in fact highly critical of both the scientific basis of eugenics and its coercive social policies. In this regard, Tierney has grossly misrepresented Neel's views on a wide range of social implications of modern civilization for the long-term health of the gene pool. Far from causing an epidemic of measles, Neel did his utmost to protect the Yanomamo from the ravages of the impending epidemic by a vaccination program using a vaccine that was widely used at the time and administered in an appropriate manner. There was nothing experimental about the vaccination program, which in fact severely hindered the primary scientific objectives of the expedition. Although the research was funded in large part by the AEC, there was no element of radiation research and the work had no connection with the ethical abuses that have been reported from AEC-sponsored radiation research, such as studies of heavy isotopes. Neel's seminal contributions to a broad range of topics in human genetics have been extensively chronicled elsewhere. His research on the Yanomamo in particular has provided unique insights into the evolutionary biology of our species, the role of sociocultural practices, such as kinship relationships and selective pressures in shaping the genetic diversity of primitive population isolates, as well as the general picture of health in such populations. The IGES decries the damage done to the reputation of one of its founders and its first President and the misperception this book may have caused about the conduct of research in genetic epidemiology. Ethical issues about scientific research in primitive populations deserve serious and wide discussion, but the IGES condemns the gross misrepresentation of the facts and demonization of the principal characters in this book.
TL;DR: The use of structural equations (path analysis) provides an alternative, equivalent formulation to variance components models that can continue to be extended to meet the challenges of modeling and dissecting the genetic nature of complex traits in the new century.
Abstract: The use of structural equations (path analysis) provides an alternative, equivalent formulation to variance components models. Instead of partitioning the variance, we focus on modeling the underlying random variables themselves through a system of linear, mixed model, regression equations. A few specific examples of genetic path models for linkage and association (linkage disequilibrium) are discussed. This formulation provides a simple yet elegant framework that can continue to be extended to meet the challenges of modeling and dissecting the genetic nature of complex traits in the new century.