Selection and mutation in the “new” genetics: an emerging hypothesis

It has been anticipated that new, much more sensitive, next generation sequencing (NGS) techniques, using massively parallel sequencing, will likely provide radical insights into the genetics of multifactorial diseases. While NGS has been used initially to analyze individual human genomes, and has revealed considerable differences between healthy individuals, we have used NGS to examine genetic variation within individuals, by sequencing tissues “in depth”, i.e., oversequencing many thousands of times. Initial studies have revealed intra-tissue genetic heterogeneity, in the form of multiple variants of a single gene that exist as distinct “majority and “minority” variants. This highly specialized form of somatic mosaicism has been found within both cancer and normal tissues. If such genetic variation within individual tissues is widespread, it will need to be considered as a significant factor in the ontogeny of many multifactorial diseases, including cancer. The discovery of majority and minority gene variants and the resulting somatic cell heterogeneity in both normal and diseased tissues suggests that selection, as opposed to mutation, might be the critical event in disease ontogeny. We, therefore, are proposing a hypothesis to explain multifactorial disease ontogeny in which pre-existing multiple somatic gene variants, which may arise at a very early stage of tissue development, are eventually selected due to changes in tissue microenvironments.     

Improving the prediction of complex diseases by testing for multiple disease-susceptibility genes

Studies have argued that genetic testing will provide limited information for predicting the probability of common diseases, because of the incomplete penetrance of genotypes and the low magnitude of associated risks for the general population. Such studies, however, have usually examined the effect of one gene at time. We argue that disease prediction for common multifactorial diseases is greatly improved by considering multiple predisposing genetic and environmental factors concurrently, provided that the model correctly reflects the underlying disease etiology. We show how likelihood ratios can be used to combine information from several genetic tests to compute the probability of developing a multifactorial disease. To show how concurrent use of multiple genetic tests improves the prediction of a multifactorial disease, we compute likelihood ratios by logistic regression with simulated case-control data for a hypothetical disease influenced by multiple genetic and environmental risk factors. As a practical example, we also apply this approach to venous thrombosis, a multifactorial disease influenced by multiple genetic and nongenetic risk factors. Under reasonable conditions, the concurrent use of multiple genetic tests markedly improves prediction of disease. For example, the concurrent use of a panel of three genetic tests (factor V Leiden, prothrombin variant G20210A, and protein C deficiency) increases the positive predictive value of testing for venous thrombosis at least eightfold. Multiplex genetic testing has the potential to improve the clinical validity of predictive testing for common multifactorial diseases.     

Multiple DNA Variant Association Analysis: Application to the Insulin Gene Region in Type 1 Diabetes

Association and linkage studies have shown that at least one of the genetic factors involved in susceptibility to insulin-dependent diabetes mellitus (IDDM) is contained within a 4.1-kb region of the insulin gene. Sequence analysis has led to the identification of 10 DNA variants in this region that are associated with increased risk for IDDM. These variants are in strong linkage disequilibrium with each other, and previous studies have failed to distinguish between the variant(s) that cause increased susceptibility to IDDM and others that are associated with the disease because of linkage disequilibrium. To address this problem, we have undertaken a large population study of French diabetics and controls and have analyzed genotype patterns for several of the variant sites simultaneously. This has led to the identification of a subset consisting of four variants (−2733AC, −23HphI, −365VNTR, and +1140AC), at least one of which appears to be directly implicated in disease susceptibility. The multiple-DNA-variant association-analysis approach that is applied here to the problem of identifying potential susceptibility variants in IDDM is likely to be important in studies of many other multifactorial diseases.     

环境和遗传因素与慢性代谢性疾病的人群研究

几十年来我国居民经历了快速的营养转型,与不健康的膳食和生活方式相关的＂致肥环境＂,以及遗传倾向是导致我国慢性代谢性疾病如代谢综合征和2型糖尿病快速流行的主要推手。然而,我国目前非常缺乏针对导致慢性代谢性疾病的主要遗传和环境危险因素而开展的系统研究。在过去若干年中,通过开展基于社区人群的流行病学研究,本课题组发现了多个与代谢性疾病相关的基因变异、环境因素和生物标记物。与此同时,通过对代谢综合征或2型糖尿病患者进行的营养干预,发现添加亚麻子或其衍生物木酚素、核桃,以及用糙米替代白米能不同程度地改善代谢综合征或血糖控制。总之,所有努力旨在增进对导致中国人代谢性疾病高易感性相关的病因和机制的理解,同时也希望为疾病的预测和预防提供新的思路和线索。     

Genetic determinants of platelet function in thromboembolic diseases

Within the past decade our understanding of thromboembolic disorders has become even more sophisticated as recent discoveries have suggested the influence of gene variants on the development of atherosclerotic disease and arterial thrombosis. Candidate genes encode proteins involved in processes relevant to atherosclerosis, ranging from cholesterol metabolism to arterial thrombosis. Platelets are key elements in primary hemostasis, but also in arterial thrombosis. Moreover, a number of genetic polymorphisms of platelet proteins may also induce gain or loss of function, supporting a role predisposing some individuals to thrombotic events. However, after thousands of studies, much controversy remains whether individual platelet polymorphisms contribute to an increased likelihood of thromboembolic disorders. Although platelet polymorphisms are a promising addition to more established cardiovascular risk factors, identifying genetic variants as a single cause of cardiovascular disease would be an oversimplification; instead, the contribution of these polymorphisms should also be considered in the context of a multifactorial disease. Gene-gene and gene-environment studies would identify specific combinations associated with a high risk to suffer from these diseases. The platelet's genetic heterogeneity should also be considered in every aspect of clinical medicine, ranging from susceptibility to diseases, pathogenesis, and clinical outcome to diversity in responses to drug treatment (pharmacogenomics), and bleeding.     

A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data

Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal–normal, normal–disease, and disease–disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene–environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene–environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases.     

The sense and nonsense of direct-to-consumer genetic testing for cardiovascular disease

Expectations are high that increasing knowledge of the genetic basis of cardiovascular disease will eventually lead to personalised medicine—to preventive and therapeutic interventions that are targeted to at-risk individuals on the basis of their genetic profiles. Most cardiovascular diseases are caused by a complex interplay of many genetic variants interacting with many non-genetic risk factors such as diet, exercise, smoking and alcohol consumption. Since several years, genetic susceptibility testing for cardiovascular diseases is being offered via the internet directly to consumers. We discuss five reasons why these tests are not useful, namely: (1) the predictive ability is still limited; (2) the risk models used by the companies are based on assumptions that have not been verified; (3) the predicted risks keep changing when new variants are discovered and added to the test; (4) the tests do not consider non-genetic factors in the prediction of cardiovascular disease risk; and (5) the test results will not change recommendations of preventive interventions. Predictive genetic testing for multifactorial forms of cardiovascular disease clearly lacks benefits for the public. Prevention of disease should therefore remain focused on family history and on non-genetic risk factors as diet and physical activity that can have the strongest impact on disease risk, regardless of genetic susceptibility.     

Towards the genetic analysis of mulfactorial diseases: the estimation of allele frequency and epistasis

OBJECTIVES: The general aim of this paper is to reactivate the original intention behind the Elston-Stewart algorithm: i.e. physiological characterisation of the effects of individual loci underlying quantitative variation. The specific aim is the estimation of allele frequency and epistasis in multifactorial genetic diseases. METHODS: In a general genetic model, the probability of disease is a sigmoid function of the number of disease alleles summed over all loci. This model has just 4 parameters: the number of loci; the population frequency of disease alleles; a threshold expressed as a proportion of disease alleles; and the slope of the sigmoid curve. Assuming 10 loci, the remaining parameters can be estimated from empirical data: population frequency of the disease, monozygotic twin concordance rates, and disease frequency in sibs of affected probands. RESULTS: For 10 typical multifactorial diseases, the estimates of allele frequency are generally high, of the order of 20%, with strong epistatic interactions between loci. It follows that the frequencies of subphenotypes specific for a single disease locus will also be high, and only about two-fold greater in affected individuals than in normal controls. CONCLUSIONS: Because of allelic heterogeneity, purely genomic approaches are unlikely to succeed in unravelling the genetics of multifactorial diseases; this will rather require articulation with physiology and the identification of biologically meaningful subphenotypes.     

A critical analysis of disease-associated DNA polymorphisms in the genes of cattle,goat, sheep,and pig

Genetic variations through their effects on gene expression and protein function underlie disease susceptibility in farm animal species. The variations are in the form of single nucleotide polymorphisms, deletions/insertions of nucleotides or whole genes, gene or whole chromosomal rearrangements, gene duplications, and copy number polymorphisms or variants. They exert varying degrees of effects on gene action, such as substitution of an amino acid for another, shift in reading frame and premature termination of translation, and complete deletion of entire exon(s) or gene(s) in diseased individuals. These factors influence gene function by affecting mRNA splicing pattern or by altering/eliminating protein function. Elucidating the genetic bases of diseases under the control of many genes is very challenging, and it is compounded by several factors, including host × pathogen × environment interactions. In this review, the genetic variations that underlie several diseases of livestock (under monogenic and polygenic control) are analyzed. Also, factors hampering research efforts toward identification of genetic influences on animal disease identification and control are highlighted. A better understanding of the factors analyzed could be better harnessed to effectively identify and control, genetically, livestock diseases. Finally, genetic control of animal diseases can reduce the costs associated with diseases, improve animal welfare, and provide healthy animal products to consumers, and should be given more attention.     

Population analysis and determination of the ethnic background are necessary in the study of multifactorial diseases: A study using the Dagestan population as a model

Psoriasis, a multifactorial disease with genetic predisposition, has been used as an example to study the role of the ethnic background in multifactorial diseases in the Dagestan population. The individual information card (IIC) is proposed as the main tool for correct collection and processing of information. The results of the study demonstrate that the Dagestan population is a convenient and adequate model population for studying multifactorial diseases, such as psoriasis, and may serve as an object for studying the role of heredity in the etiologies and pathogeneses of this and other multifactorial diseases.     

Population analysis and determination of the ethnic background are necessary in the study of multifactorial diseases: a study using the Dagestan population as a model

Psoriasis, a multifactorial disease with genetic predisposition, has been used as an example to study the role of the ethnic background in multifactorial diseases in the Dagestan population. The individual information card (IIC) is proposed as the main tool for correct collection and processing of information. The results of the study demonstrate that the Dagestan population is a convenient and adequate model population for studying multifactorial diseases, such as psoriasis, and may serve as an object for studying the role of heredity in the etiologies and pathogeneses of this and other multifactorial diseases.     

Ionizing radiation and genetic risks: VI. Chronic multifactorial diseases: a review of epidemiological and genetical aspects of coronary heart disease,essential hypertension and diabetes mellitus

This paper provides a broad overview of the epidemiological and genetical aspects of common multifactorial diseases in man with focus on three well-studied ones, namely, coronary heart disease (CHD), essential hypertension (EHYT) and diabetes mellitus (DM). In contrast to mendelian diseases, for which a mutant gene either in the heterozygous or homozygous condition is generally sufficient to cause disease, for most multifactorial diseases, the concepts of `genetic susceptibility' and `risk factors' are more appropriate. For these diseases, genetic susceptibility is heterogeneous. The well-studied diseases such as CHD permit one to conceptualize the complex relationships between genotype and phenotype for chronic multifactorial diseases in general, namely that allelic variations in genes, through their products interacting with environmental factors, contribute to the quantitative variability of biological risk factor traits and thus ultimately to disease outcome. Two types of such allelic variations can be distinguished, namely those in genes whose mutant alleles have (i) small to moderate effects on the risk factor trait, are common in the population (polymorphic alleles) and therefore contribute substantially to the variability of biological risk factor traits and (ii) profound effects, are rare in the population and therefore contribute far less to the variability of biological risk factor traits. For all the three diseases considered in this review, a positive family history is a strong risk factor. CHD is one of the major contributors to mortality in most industrialized countries. Evidence from epidemiological studies, clinical correlations, genetic hyperlipidaemias etc., indicate that lipids play a key role in the pathogenesis of CHD. The known lipid-related risk factors include: high levels of low density lipoprotein cholesterol, low levels of high density lipoprotein cholesterol, high apoB levels (the major protein fraction of the low density lipoprotein particles) and elevated levels of Lp(a) lipoprotein. Among the risk factors which are not related to lipids are: high levels of homocysteine, low activity of paraoxonase and possibly also elevated plasma fibrinogen levels. In addition to the above, hypertension, diabetes and obesity (which themselves have genetic determinants) are important risk factors for CHD. Among the environmental risk factors are: high dietary fat intake, smoking, stress, lack of exercise etc. About 60% of the variability of the plasma cholesterol is genetic in origin. While a few genes have been identified whose mutant alleles have large effects on this trait (e.g., LDLR, familial defective apoB-100), variability in cholesterol levels among individuals in most families is influenced by allelic variation in many genes (polymorphisms) as well as environmental exposures. A proportion of this variation can be accounted for by two alleles of the apoE locus that increase (ϵ4) and decrease (ϵ2) cholesterol levels, respectively. A polymorphism at the apoB gene (XbaI) also has similar effects, but is probably not mediated through lipids. High density lipoprotein cholesterol levels are genetically influenced and are related to apoA1 and hepatic lipase (LIPC) gene functions. Mutations in the apoA1 gene are rare and there are data which suggest a role of allelic variation at or linked LIPC gene in high density lipoprotein cholesterol levels. Polymorphism at the apoA1–C3 loci is often associated with hypertriglyceridemia. The apo(a) gene which codes for Lp(a) is highly polymorphic, each allele determining a specific number of multiple tandem repeats of a unique coding sequence known as Kringle 4. The size of the gene correlates with the size of the Lp(a) protein. The smaller the size of the Lp(a) protein, the higher are the Lp(a) levels. Hyperhomocyst(e)inemia is a risk factor for myocardial infarction, stroke and peripheral vascular disease, but the precise nature and intensity of this association, the biochemical mechanisms involved and the role of environmental factors remain to be fully elucidated. Recently, it has been suggested that polymorphisms in genes that code for paraoxonase may need to be added to the list of genetic risk factors for CHD. There are suggestions that high plasma fibrinogen levels (which is exacerbated by smoking which also lowers high density lipoprotein cholesterol levels) may constitute yet another risk factor for CHD. Essential hypertension (EHYT) affects some 10–25% of the people of the industrial world. Its clinical relevance stems from the fact that it is one of the major risk factors for cardiovascular and renal diseases, especially, stroke, coronary heart disease and end-stage renal disease. The role of genetic factors in EHYT is clearly indicated by family studies in which correlations in blood pressure levels have been studied. The variations in the range and magnitude of these correlations however suggest that other, environmental factors must play an important role and which vary from individual to individual and population to population. No major genes controlling blood pressure have been identified. However during the past five years or so, linkage and association studies have shown that there are at least three gene loci, polymorphism at which may contribute to EHYT: these include the AGT, AT1 and ACE genes. Additionally, the molecular basis of three rare mendelian disorders associated with hypertension, namely those involved in glucocorticosteroid-remediable aldosteronism (GRA), Liddle syndrome and apparent mineralocorticosteroid excess (AME) have been delineated. On the basis of clinical phenotypes, four types of diabetes mellitus are distinguished, of which insulin-dependent diabetes melltius (IDDM) and non-insulin-dependent diabetes mellitus (NIDDM) have been the subject of extensive studies. IDDM is a group of heterogeneous diseases probably resulting from exposure to some environmental agent(s) in those individuals with a genetically-determined susceptibility. IDDM is the result of the destruction of insulin-producing β-cells of the pancreas, principally by immunologically-mediated (autoimmune) mechanisms. The major defined risk factor is genetic susceptibility: apart from IDDM1 (linked to the HLA complex) and IDDM2 (in the insulin gene region) at least 10 other genes are involved, mutations at which cause susceptibility to IDDM. There is recent evidence for the possible involvement of an endogenous retrovirus in the aetiology of acute onset IDDM. NIDDM is a very common disease and its prevalence varies in different populations. As in the case of IDDM, its major determinant is genetic susceptibility. Compared to IDDM, the concordance rates in monozygotic twins and risks to first-degree relatives are higher. With the exception of MODY subtype with earlier onset, most cases have onset in middle or late life. The known geographical variations in the prevalence and studies of migrant populations suggest that environmental factors might also be important. The number of genes mutations at which cause susceptibility to NIDDM is not yet known and so far, one putative major gene locus has recently been identified in a Mexican–American population. Several candidate genes are currently being investigated. The available data indicate that some of the genes act through inherited susceptibility to insulin resistance and to decreased capacity for insulin secretion. Rare forms are due to dominant mutations i.e., the MODY diabetes and rarer still are forms due to the production of abnormal insulin due to mutations in the insulin gene itself. Finally, a small proportion of diabetes may be due to mutations in the mitochondrial genome. The attributes, risk factors and interrelationships between the three diseases considered in this review clearly show that the task of using this information for reliably predicting the risk of any of these diseases is formidable, even for a scenario of no radiation exposures, not to mention radiation scenarios. Nonetheless, these data provide a useful framework for developing models aimed at quantifying the response of these diseases to an increase in mutation rate due to radiation. One such model is discussed in a later paper of this series.     

Evidence that autoimmunity in man is a Mendelian dominant trait.

Family studies of autoimmune diseases are consistent with multifactorial etiology. However, familial occurrence of the autoimmune trait as defined by the presence of autoimmune disease and/or high titer autoantibody supports the hypothesis that autoimmunity is inherited as an autosomal dominant trait. Based on genetic analysis of 18 autoimmune kindreds, the population frequency of this primary autoimmune gene is approximately .10 with penetrance estimates of 92% in females and 49% in males. The estimated high penetrance of the autoimmune gene in females suggests that the interacting genetic and/or environmental factors must be numerous or ubiquitous. Sex, age, and specific major histocompatibility complex (MHC) antigens are among the genetic and physiological factors known to influence autoimmunity. A genetic model is proposed that takes these factors into account. Inherent in the hypothesis of a primary autoimmune gene is that it is epistatic to other, secondary, genes that influence the autoimmune phenotype. The genetic model further postulates that the secondary genes, including those of the MHC, confer specificity to the phenotype. The effects of the secondary genes can be modulated by gonadal steroids and, over time, may be abrogated by environmental challenges, such as viral infections.     

Gene–environment interactions at the FKBP5 locus: sensitive periods,mechanisms and pleiotropism

Psychiatric phenotypes are multifactorial and polygenic, resulting from the complex interplay of genes and environmental factors that act cumulatively throughout an organism's lifetime. Adverse life events are strong predictors of risk for a number of psychiatric disorders and a number of studies have focused on gene–environment interactions (GxEs) occurring at genetic loci involved in the stress response. Such a locus that has received increasing attention is the gene encoding FK506 binding protein 51 (FKBP5), a heat shock protein 90 cochaperone of the steroid receptor complex that among other functions regulates sensitivity of the glucocorticoid receptor. Interactions between FKBP5 gene variants and life stressors alter the risk not only for mood and anxiety disorders, but also for a number of other disease phenotypes. In this review, we will focus on molecular and system‐wide mechanisms of this GxE with the aim of establishing a framework that explains GxE interactions. We will also discuss how an understanding of the biological effects of this GxE may lead to novel therapeutic approaches .     

FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Despite remarkable success in uncovering many risk variants and providing novel insights into disease biology, genetic variants identified to date fail to explain the vast majority of the heritability for most complex diseases. One explanation is that there are still a large number of common variants that remain to be discovered, but their effect sizes are generally too small to be detected individually. Accordingly, gene set analysis of GWAS, which examines a group of functionally related genes, has been proposed as a complementary approach to single-marker analysis. Here, we propose a flexible and adaptive test for gene sets (FLAGS), using summary statistics. Extensive simulations showed that this method has an appropriate type I error rate and outperforms existing methods with increased power. As a proof of principle, through real data analyses of Crohn’s disease GWAS data and bipolar disorder GWAS meta-analysis results, we demonstrated the superior performance of FLAGS over several state-of-the-art association tests for gene sets. Our method allows for the more powerful application of gene set analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.     

Blood polymorphism in the study of isolated communities

Isolated communities offer a unique opportunity for the study of biological and social consequences of consanguinity and migration. The studies of genetic polymorphisms have contributed greatly, not only to knowledge of the genetic constitution of a given individual and population, but also to clarify either relationship between structure and function of polymorphic traits or the susceptibility to multifactorial diseases, in which interaction between the gene and environment cannot be ignored. For over 25 years, we have investigated the effect of consanguinity and genetic polymorphisms in 9 isolated communities in Western Japan. We reported here different values of gene frequency for each polymorphic trait, compared with the neighboring communities and described how we applied these data to clarification of the genetic constitution of isolated communities as well as of genetic susceptibility to some diseases.     

SNPs Associated with Cerebrospinal Fluid Phospho-Tau Levels Influence Rate of Decline in Alzheimer's Disease

Alzheimer''s Disease (AD) is a complex and multifactorial disease. While large genome-wide association studies have had some success in identifying novel genetic risk factors for AD, case-control studies are less likely to uncover genetic factors that influence progression of disease. An alternative approach to identifying genetic risk for AD is the use of quantitative traits or endophenotypes. The use of endophenotypes has proven to be an effective strategy, implicating genetic risk factors in several diseases, including anemia, osteoporosis and heart disease. In this study we identify a genetic factor associated with the rate of decline in AD patients and present a methodology for identification of other such factors. We have used an established biomarker for AD, cerebrospinal fluid (CSF) tau phosphorylated at threonine 181 (ptau₁₈₁) levels as an endophenotype for AD, identifying a SNP, rs1868402, in the gene encoding the regulatory sub-unit of protein phosphatase B, associated with CSF ptau₁₈₁ levels in two independent CSF series . We show no association of rs1868402 with risk for AD or age at onset, but detected a very significant association with rate of progression of disease that is consistent in two independent series . Our analyses suggest that genetic variants associated with CSF ptau₁₈₁ levels may have a greater impact on rate of progression, while genetic variants such as APOE4, that are associated with CSF Aβ₄₂ levels influence risk and onset but not the rate of progression. Our results also suggest that drugs that inhibit or decrease tau phosphorylation may slow cognitive decline in individuals with very mild dementia or delay the appearance of memory problems in elderly individuals with low CSF Aβ₄₂ levels. Finally, we believe genome-wide association studies of CSF tau/ptau₁₈₁ levels should identify novel genetic variants which will likely influence rate of progression of AD.     

Ziele und Grenzen der Quantifizierung genetischer Risiken

This present article deals with the question of how meaningful statistical risk assessments in genetic counseling may be for the decision-making process of individuals who ask for genetic advice. Even in some Mendelian diseases and generally in the case of multifactorial diseases it is not satisfactory to provide an a priori risk for all affected families. The question of whether a given person or his or her future children may develop the disease in question with severe, possibly untreatable symptoms may largely depend on individual circumstances. Accordingly, the inclusion of such circumstances in the assessment of an individual’s a posteriori risk may largely deviate upwards or downwards from this a priori risk. Currently, the lack of such data often prevents the quantification of an individual a posteriori risk; it is, however, obvious that individual risk assessment will play an increasing role in future genetic counseling. Besides a better understanding of individual environmental influences our growing insight into the impact of epigenetic changes will enforce a re-evaluation of individual risks. Epigenetic changes acquired during fetal development or early childhood may lead to lasting physiological changes and, accordingly, may significantly affect the risk of an individual to develop a certain disease during his or her later life. Due to the complexity of genetic and epigenetic networks it is as inappropriate to categorize mutated genes or alleles which increase the risk for a certain disease as “bad” or “sick” genes as it is to consider genes with apparently health-promoting properties as “good” or “healthy” genes. Such rhetoric may foster feelings of either guilt or superiority in individuals and may have an even more politically dangerous impact by encouraging neo-eugenic thinking. In contrast, the goal of non-directive genetic counseling is to provide individuals seeking advice with reliable information which helps them arrive at a personal decision. Given their personal circumstances this decision should be ethically acceptable and realistically allow them to cope with their problem in their future lives. As seen in cases of Mendelian diseases, such as sickle cell anemia or thalassemias, heterozygous carriers may have selective advantages, in this particular case a natural resistance against Malaria tropica. This example demonstrates that a genetic burden may not have only disadvantages but also advantages for the health of a whole population. This consideration may hold for many alleles which have been present in the gene pool of a population for many generations, although they contribute to a genetic predisposition for a multifactorial disease. In debates about the heritability of a given trait, the heritability coefficient has often been used as an argument for and against the predominant impact of genes on the trait in question. The authors wish to remind their readers that this coefficient reflects the genetic variation measured in a given population divided by the variation of all possible parameters which affect the phenotypic trait. It neither gives an answer about the absolute influence of genes and other factors on this trait nor does it provide information about how unforeseen changes in the environment may affect the heritability of the trait in the future. Contrary to a common misunderstanding in public discussion this coefficient does not allow any conclusion on the interaction of genetic, epigenetic and environmental factors involved in the development of the trait in question in an individual. Finally, in the closing section the authors highlight the opportunities of risk-adapted disease prevention.     

Multifactorial diseases: a nightmare for the geneticist

Common diseases are often familial, but they do not show in most families, a simple pattern of inheritance. In a few families these diseases may be caused by a mutation in a single gene. In most families these diseases are multifactorial, they result from a complex interaction between a genetic component which is often polygenic and many environmental factors. Two major, model free, methods are used to locate and identify susceptibility genes that predispose to multifactorial diseases. The first is a non parametric linkage analysis that relies on affected sib pairs, or an affected pedigree member, the second method is association studies which looks for increase frequency of particular alleles or genotypes in affected compared with unaffected individuals in the population. Most of the results have not been replicated, identifying susceptibility genes is proving much more difficult than most geneticists imagined 20 years ago. The main reason for this irreproducibility is genetic heterogeneity.