Methods Of Genetic Epidemiology Research Articles

Alcohol intake and cause-specific mortality: conventional and genetic evidence in a prospective cohort study of 512 000 adults in China

Genetic variants that affect alcohol use in East Asian populations could help assess the causal effects of alcohol consumption on cause-specific mortality. We aimed to investigate the associations between alcohol intake and cause-specific mortality using conventional and genetic epidemiological methods among more than 512 000 adults in China. The prospective China Kadoorie Biobank cohort study enrolled 512 724 adults (210 205 men and 302 519 women) aged 30-79 years, during 2004-08. Residents with no major disabilities from ten diverse urban and rural areas of China were invited to participate, and alcohol use was self-reported. During 12 years of follow-up, 56 550 deaths were recorded through linkage to death registries, including 23 457 deaths among 168 050 participants genotyped for ALDH2-rs671 and ADH1B-rs1229984. Adjusted hazard ratios (HRs) for cause-specific mortality by self-reported and genotype-predicted alcohol intake were estimated using Cox regression. 33% of men drank alcohol most weeks. In conventional observational analyses, ex-drinkers, non-drinkers, and heavy drinkers had higher risks of death from most major causes than moderate drinkers. Among current drinkers, each 100 g/week higher alcohol intake was associated with higher mortality risks from cancers (HR 1·18 [95% CI 1·14-1·22]), cardiovascular disease (CVD; HR 1·19 [1·15-1·24]), liver diseases (HR 1·51 [1·27-1·78]), non-medical causes (HR 1·15 [1·08-1·23]), and all causes (HR 1·18 [1·15-1·20]). In men, ALDH2-rs671 and ADH1B-rs1229984 genotypes predicted 60-fold differences in mean alcohol intake (4 g/week in the lowest group vs 255 g/week in the highest). Genotype-predicted alcohol intake was uniformly and positively associated with risks of death from all causes (n=12 939; HR 1·07 [95% CI 1·05-1·10]) and from pre-defined alcohol-related cancers (n=1274; 1·12 [1·04-1·21]), liver diseases (n=110; 1·31 [1·02-1·69]), and CVD (n=6109; 1·15 [1·10-1·19]), chiefly due to stroke (n=3285; 1·18 [1·12-1·24]) rather than ischaemic heart disease (n=2363; 1·06 [0·99-1·14]). Results were largely consistent using a polygenic score to predict alcohol intake, with higher intakes associated with higher risks of death from alcohol-related cancers, CVD, and all causes. Approximately 2% of women were current drinkers, and although power was low to assess observational associations of alcohol with mortality, the genetic evidence suggested that the excess risks in men were due to alcohol, not pleiotropy. Higher alcohol intake increased the risks of death overall and from major diseases for men in China. There was no genetic evidence of protection from moderate drinking for all-cause and cause-specific mortality, including CVD. Kadoorie Charitable Foundation, National Natural Science Foundation of China, British Heart Foundation, Cancer Research UK, GlaxoSmithKline, Wellcome Trust, Medical Research Council, and Chinese Ministry of Science and Technology.

Progress in research of genomic epidemiology of pathogenic fungi

Genomic epidemiology, based on whole-genome sequencing technology and bioinformatics analysis, can make up for the shortcomings of traditional molecular typing methods and provide a novel insight for the genetic evolution and transmission of pathogenic fungi. The combination of genetic information and epidemiological methods of pathogenic fungi can predict fungi transmission routes and risks, and provide a theoretical basis for the development of public health strategies for fungi infection prevention and control. This paper summarizes the development of molecular epidemiology and genomic epidemiology, as well as the application of genomic epidemiology methods in the analyses of genetic relationship, origin, evolution, drug resistance, virulence, and genome-wide association of pathogenic fungi, and discusses the development of pathogenic fungi genomic epidemiology.

Lung function and cardiovascular disease: a two-sample Mendelian randomisation study.

Observational studies suggest an association between reduced lung function and risk of coronary artery disease and ischaemic stroke, independent of shared cardiovascular risk factors such as cigarette smoking. We use the latest genetic epidemiological methods to determine whether impaired lung function is causally associated with an increased risk of cardiovascular disease. Mendelian randomisation uses genetic variants as instrumental variables to investigate causation. Preliminary analysis used two-sample Mendelian randomisation with lung function single nucleotide polymorphisms. To avoid collider bias, the main analysis used single nucleotide polymorphisms for lung function identified from UKBiobank in a multivariable Mendelian randomisation model conditioning for height, body mass index and smoking.Multivariable Mendelian randomisation shows strong evidence that reduced forced vital capacity (FVC) causes increased risk of coronary artery disease (OR 1.32, 95% CI 1.19-1.46 per standard deviation). Reduced forced expiratory volume in 1 s (FEV1) is unlikely to cause increased risk of coronary artery disease, as evidence of its effect becomes weak after conditioning for height (OR 1.08, 95% CI 0.89-1.30). There is weak evidence that reduced lung function increases risk of ischaemic stroke. There is strong evidence that reduced FVC is independently and causally associated with coronary artery disease. Although the mechanism remains unclear, FVC could be taken into consideration when assessing cardiovascular risk and considered a potential target for reducing cardiovascular events. FEV1 and airflow obstruction do not appear to cause increased cardiovascular events; confounding and collider bias may explain previous findings of a causal association.

Human Campylobacteriosis Cases Traceable to Chicken Meat-Evidence for Disseminated Outbreaks in Finland.

Campylobacter jejuni (C. jejuni) is the most common cause of human bacterial gastroenteritis in the world. Food-borne campylobacteriosis is thought to be commonly caused by the handling and consumption of undercooked chicken meat, but the epidemiology of this disease is complex and remains poorly characterized, especially in the Nordic countries. Here, we used state-of-the-art methods in genetic epidemiology combined with patient background and temporal association data to trace domestically acquired human C. jejuni infections (n = 50) to chicken meat, in a midsize Nordic town in Finland during a seasonal peak. Although 59.2% of the human isolates shared a sequence type (ST) with a chicken batch slaughtered prior to the onset of disease, further analysis at the whole-genome level (core genome and whole-genome multilocus sequence typing, cgMLST and wgMLST, respectively) traced a mere nine cases (18.4%) to fresh chicken meat. Human isolates also shared genotypes with isolates collected from chicken batches slaughtered after the onset of the human disease, highlighting the role of alternative transmission pathways from chickens to humans besides the food chain, or a shared third source. The high resolution offered by wgMLST, combined with simple metadata, offers a more accurate way to trace sporadic cases to possible sources and reveal disseminated outbreak clustering in time, confirming the importance of complementing epidemiological investigations with molecular epidemiological data.

Attitudes to Gun Control in an American Twin Sample: Sex Differences in the Causes of Variation.

The genetic and social causes of individual differences in attitudes to gun control are estimated in a sample of senior male and female twin pairs in the United States. Genetic and environmental parameters were estimated by weighted least squares applied to polychoric correlations for monozygotic (MZ) and dizygotic (DZ) twins of both sexes. The analysis suggests twin similarity for attitudes to gun control in men is entirely genetic while that in women is purely social. Although the volunteer sample is small, the analysis illustrates how the well-tested concepts and methods of genetic epidemiology may be a fertile resource for deepening our scientific understanding of biological and social pathways that affect individual risk to gun violence.

RETRACTION: Illuminating the dark road from schizophrenia genetic associations to disease mechanisms

Abstract Recent large-scale genome-wide association studies (GWAS) have enabled the discovery of common genetic variations contributing to risk architectures of schizophrenia in human populations; however, the majority of GWAS-identified variants are located in large genomic regions spanning multiple genes, and recognizing the precise targets and mechanisms of these clinical associations is now the major challenge. Here, we review recent progress in schizophrenia genetics, functional genomics and related neuroscience research, and propose a functional pipeline to translate schizophrenia GWAS risk loci into disease biology and information for drug discovery. The pipeline includes identification of underlying molecular mechanisms using transcriptomic data in human brain, prioritization of putative functional causative variants by the integration of genetic epidemiological and bioinformatics methods as well as molecular approaches, and in vitro and in vivo experimental characterizations of the identified targeted species and causative variants to dissect the relevant disease biology. These approaches will accelerate progress from schizophrenia genetic studies to biological mechanisms and ultimately guide the development of prognostic, preventive and therapeutic measures.

THE POSSIBILITY OF PROGNOSIS INCIDENCE BY MEANS OF DEGREES OF CANCER AGGRAVATED FAMILY HISTORY

Background: Aim of the study was to estimate the possibility of prognosis incidence by means of degrees of cancer aggravated family history. Patients and methods: 1233 families (n =4689) from the Moscow Cancer Family Registry who answered 5-years later the first questionnaire were divided into 4 groups according to our classification of degrees of cancer aggravated family history: (1) not aggravated, (2) little aggravated, (3) aggravated, (4) syndromes (see detailed description in the text). The methods of genetic epidemiology, epidemiology, statistics were used. Results: Incidence in the first and second groups were near population expected cases, some higher in the aggravated group and sharp rise in women from the syndromes-associated families (the most syndromes predisposed to cancer of women reproductive system), relative risk was 10,76 for probands and 8,19 for the first relative women. There was no increase in frequency of new cases among men in syndrome-associated families. Conclusion: Analysis of degrees of cancer aggravated family history can be used for the incidence prognosis; one or two cancer cases among first degree relations don’t regard as a high oncogenetic risk factor; members of families with syndromes are obligatory cancer risk group.

Genes associated with RSV lower respiratory tract infection and asthma: the application of genetic epidemiological methods to understand causality.

Infants with respiratory syncytial virus (RSV) lower respiratory tract infections (LRIs) are at increased risk for childhood asthma. The objectives of this article are to review the genes associated with both RSV LRI and asthma, review analytic approaches to assessing shared genetic risk and propose a future perspective on how these approaches can help us to understand the role of infant RSV infection as both an important risk factor for asthma and marker of shared genetic etiology between the two conditions. The review of shared genes and thus pathways associated with severity of response to RSV infection and asthma risk can help us to understand mechanisms of disease and ultimately propose new and novel targets for primary prevention of both diseases.

Jumping on the Train of Personalized Medicine: A Primer for Non-Geneticist Clinicians: Part 2. Fundamental Concepts in Genetic Epidemiology

With the decrease in sequencing costs, personalized genome sequencing will eventually become common in medical practice. We therefore write this series of three reviews to help non-geneticist clinicians to jump into the fast-moving field of personalized medicine. In the first article of this series, we reviewed the fundamental concepts in molecular genetics. In this second article, we cover the key concepts and methods in genetic epidemiology including the classification of genetic disorders, study designs and their implementation, genetic marker selection, genotyping and sequencing technologies, gene identification strategies, data analyses and data interpretation. This review will help the reader critically appraise a genetic association study. In the next article, we will discuss the clinical applications of genetic epidemiology in the personalized medicine area.

The possibility of prognosis incidence by means of degrees of cancer aggravated family history

Aim of the study was to estimate the possibility of prognosis incidence by means of degrees of cancer aggravated family history. 1233 families (n = 4689) from the Moscow Cancer Family Registry who answered 5-years later the first questionnaire were divided into 4 groups according to our classification of degrees of cancer aggravated family history: (1) not aggravated, (2) little aggravated, (3) aggravated, (4) syndromes (see detailed description in the text). The methods of genetic epidemiology, epidemiology, statistics were used. Incidence in the first and second groups were near population expected cases, some higher in the aggravated group and sharp rise in women from the syndromes-associated families (the most syndromes predisposed to cancer of women reproductive system), relative risk was 10.76 for probands and 8.19 for the first relative women. There was no increase infrequency of new cases among men in syndrome-associated families. Analysis of degrees of cancer aggravated family history can be used for the incidence prognosis; one or two cancer cases among first degree relations don't regard as a high oncogenetic risk factor; members of families with syndromes are obligatory cancer risk group.

Replication of Breast Cancer Susceptibility Loci in Whites and African Americans Using a Bayesian Approach

Genome-wide association studies (GWAS) and candidate gene analyses have led to the discovery of several dozen genetic polymorphisms associated with breast cancer susceptibility, many of which are considered well-established risk factors for the disease. Despite attempts to replicate these same variant-disease associations in African Americans, the evaluable populations are often too small to produce precise or consistent results. We estimated the associations between 83 previously identified single nucleotide polymorphisms (SNPs) and breast cancer among Carolina Breast Cancer Study (1993-2001) participants using maximum likelihood, Bayesian, and hierarchical methods. The selected SNPs were previous GWAS hits (n = 22), near-hits (n = 19), otherwise well-established risk loci (n = 5), or located in the same genes as selected variants (n = 37). We successfully replicated 18 GWAS-identified SNPs in whites (n = 2,352) and 10 in African Americans (n = 1,447). SNPs in the fibroblast growth factor receptor 2 gene (FGFR2) and the TOC high mobility group box family member 3 gene (TOX3) were strongly associated with breast cancer in both races. SNPs in the mitochondrial ribosomal protein S30 gene (MRPS30), mitogen-activated protein kinase kinase kinase 1 gene (MAP3K1), zinc finger, MIZ-type containing 1 gene (ZMIZ1), and H19, imprinted maternally expressed transcript gene (H19) were associated with breast cancer in whites, and SNPs in the estrogen receptor 1 gene (ESR1) and H19 gene were associated with breast cancer in African Americans. We provide precise and well-informed race-stratified odds ratios for key breast cancer-related SNPs. Our results demonstrate the utility of Bayesian methods in genetic epidemiology and provide support for their application in small, etiologically driven investigations.

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

Recent advances in the genetic epidemiology and molecular genetics of substance use disorders

This article reviews current advances in the genetics of substance use disorders (SUDs). Both genetic and environmental sources of risk are required to develop a complete picture of SUD etiology. Genetic sources of risk for SUDs are not highly substance specific in their effects. Genetic and environmental risks for SUDs typically do not only add together but also interact with each other over development. Risk gene identification for SUDs has been difficult, with one recent success in identifying nicotinic receptor variants that affect risk for nicotine dependence. The impact of genetic variants on SUD risk will individually be small. Although genetic epidemiologic methods are giving us an increasingly accurate map of broad causal pathways to SUDs, gene discovery will be needed to identify the specific biological systems. Identifying these risk genes and understanding their action will require large clinical samples, and interaction between these studies and work in model organisms.

Brief review of regression‐based and machine learning methods in genetic epidemiology: the Genetic Analysis Workshop 17 experience

Genetics Analysis Workshop 17 provided common and rare genetic variants from exome sequencing data and simulated binary and quantitative traits in 200 replicates. We provide a brief review of the machine learning and regression-based methods used in the analyses of these data. Several regression and machine learning methods were used to address different problems inherent in the analyses of these data, which are high-dimension, low-sample-size data typical of many genetic association studies. Unsupervised methods, such as cluster analysis, were used for data segmentation and, subset selection. Supervised learning methods, which include regression-based methods (e.g., generalized linear models, logic regression, and regularized regression) and tree-based methods (e.g., decision trees and random forests), were used for variable selection (selecting genetic and clinical features most associated or predictive of outcome) and prediction (developing models using common and rare genetic variants to accurately predict outcome), with the outcome being case-control status or quantitative trait value. We include a discussion of cross-validation for model selection and assessment, and a description of available software resources for these methods.

Introduction to genetic epidemiology

Genetic epidemiology is of topical and increasingly practical relevance. The subject attempts to answer 2 questions: (1) is there a genetic component to a disease, and (2) what genes are involved? This article summarizes genetic epidemiologic methods, describing family- and population-based methods used to locate and identify genes and the advantages and disadvantages of each approach. Health care professionals are faced with more and more genetic information, both from interested patients and from the media, and understanding the principles underlying genetic studies allows such information to be placed in context.

Progress in the genetics of common obesity and type 2 diabetes

The prevalence of obesity and diabetes, which are heritable traits that arise from the interactions of multiple genes and lifestyle factors, continues to rise worldwide, causing serious health problems and imposing a substantial economic burden on societies. For the past 15 years, candidate gene and genome-wide linkage studies have been the main genetic epidemiological approaches to identify genetic loci for obesity and diabetes, yet progress has been slow and success limited. The genome-wide association approach, which has become available in recent years, has dramatically changed the pace of gene discoveries. Genome-wide association is a hypothesis-generating approach that aims to identify new loci associated with the disease or trait of interest. So far, three waves of large-scale genome-wide association studies have identified 19 loci for common obesity and 18 for common type 2 diabetes. Although the combined contribution of these loci to the variation in obesity and diabetes risk is small and their predictive value is typically low, these recently identified loci are set to substantially improve our insights into the pathophysiology of obesity and diabetes. This will require integration of genetic epidemiological methods with functional genomics and proteomics. However, the use of these novel insights for genetic screening and personalised treatment lies some way off in the future.

Psychiatric Genetics: Applications in Clinical Practiceedited by Jordan W. Smoller, M.D., Sc.D., Beth Rosen Sheidley, M.S., C.G.C., and Ming T. Tsuang, M.D., Ph.D., D.Sc. Arlington, Va., American Psychiatric Publishing, 2008, 337 pp., $60.00.

Psychiatric Genetics: Applications in Clinical Practiceedited by Jordan W. Smoller, M.D., Sc.D., Beth Rosen Sheidley, M.S., C.G.C., and Ming T. Tsuang, M.D., Ph.D., D.Sc. Arlington, Va., American Psychiatric Publishing, 2008, 337 pp., $60.00.

Nutrigenomics research for personalized nutrition and medicine

Current nutritional and genetic epidemiological methods yield 'risk factors' on the basis of population studies. Risk factors, however, are statistical estimates of the percentage reduction in disease in the population if the risk were to be avoided or the gene variant were not present. These measures are often assumed to apply to individuals who are likely to differ in genetic make-up, lifestyle, and dietary patterns than to the individuals in the study population. Developing individual risk factors in light of the genetic diversity of human populations, the complexity of foods, culture and lifestyle, and the variety of metabolic processes that lead to health or disease is a significant challenge for personalizing dietary advice for healthy or individuals with chronic disease.

Genetic Determinants of Prepubertal and Pubertal Growth and Development

This article surveys the current general understanding of genetic influences on within- and between-population variation in growth and development in the context of establishing an International Growth Standard for Preadolescent and Adolescent Children. Traditional genetic epidemiologic analysis methods are reviewed, and evidence from family studies for genetic effects on different measures of growth and development is then presented. Findings from linkage and association studies seeking to identify specific genomic locations and allelic variants of genes influencing variation in growth and maturation are then summarized. Special mention is made of the need to study the interactions between genes and environments. At present, specific genes and polymorphisms contributing to variation in growth and maturation are only beginning to be identified. Larger genetic epidemiologic studies are needed in different parts of the world to better explore population differences in gene frequencies and gene-environment interactions. As advances continue to be made in molecular and statistical genetic methods, the genetic architecture of complex processes, including those of growth and development, will become better elucidated. For now, it can only be concluded that although the fundamental genetic underpinnings of the growth and development of children worldwide are likely to be essentially the same, there are also likely to be differences between populations in the frequencies of allelic gene variants that influence growth and maturation and in the nature of gene-environment interactions. This does not necessarily preclude an international growth reference, but it does have important implications for the form that such a reference might ultimately take.

Multilocus interactions at maternal tumor necrosis factor-α, tumor necrosis factor receptors, interleukin-6 and interleukin-6 receptor genes predict spontaneous preterm labor in European-American women

We hypothesize that genetic variations (single nucleotide polymorphisms-SNPs) in the tumor necrosis factor-alpha (TNF-alpha), TNF receptors (TNFRI and TNFRII), interleukin-6 (IL-6) and IL-6 receptor (IL-6R) genes predict high-risk status for spontaneous preterm birth (sPTB) in European-American women. In this study we examine the allelic and genotypic variations and the gene-gene interactions in the TNF-alpha, TNFRs, IL-6, and IL-6R genes in maternal DNA samples by using a case-control model. Maternal DNA from cases of sPTB after preterm labor (n = 101) and controls (normal term labor and delivery) (n = 321) were genotyped for SNPs in the TNF-alpha (6), TNFRI (6), TNFRII (7), IL-6 (5), and IL-6R (3) loci. SNPs were tested for both allele and genotype differences (cases vs controls) with the use of standard genetic epidemiologic methods. Multilocus interaction was assessed with multifactor dimensionality reduction analysis (MDR) to test all single and multilocus combinations for the ability to predict sPTB. Few significant allelic and genotypic associations were detected between cases and controls in maternal DNA. Single locus analysis documented independent association of SNPs at -7294 (allele and genotype) of TNFRI and 24660 (genotype) TNFRII loci with sPTB. MDR revealed a significant 3 locus model that includes SNPs -3448 of TNF-alpha, -7227 of IL-6, and 33314 of IL-6R. This interactive model allowed the successful prediction of pre- to low-risk genotypes is 3.50 (95% CI 2.52-4.87, P < .001). This is the first report to document a multilocus interaction in sPTB that predicts 65.2% of the cases in a European-American sample. Although putatively significant associations with sPTB were seen at a few single locus sites in TNFRI and TNFRII, they were not as predictive as the 3-locus model produced by MDR, suggesting the use of multilocus analyses in gene association studies of complex disease such as sPTB.