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.     

A Decision-Tree Strategy for Combining Diagnostic Tests for Prediction

In the context of medical screening, various diagnostic tests have been developed for determining whether a disease is present in an individual. Similarly, in the context of toxicological screening, a variety of short-term assays have been developed to predict whether a chemical would be carcinogenic if tested in a long-term bioassay. In both contexts, it is a challenge to combine the results of several predictive tests in a way that improves on the predictivity of the individual tests. Increases in positive predictivity can be accompanied by decreases in negative predictivity, and vice versa. This article presents a decision-tree classification model for combining results from several independent short-term or diagnostic tests to quantify the likelihood of a true positive result (patient has disease, or chemical is carcinogenic). The decision-tree strategy determines the most advantageous sequence for conducting the predictive tests. The classification model is based on statistical confidence limits on the predictive probability of disease (carcinogenicity) rather than on the central estimate of the predictive probability. This model is applied to the assessment of the abilities of four short-term tests in the prediction of chemical carcinogenicity under the assumption of independence among the four tests, and is used to demonstrate a testing strategy for the application of three pancreatic cancer diagnostic tests.     

Comparison of Family History and SNPs for Predicting Risk of Complex Disease

The clinical utility of family history and genetic tests is generally well understood for simple Mendelian disorders and rare subforms of complex diseases that are directly attributable to highly penetrant genetic variants. However, little is presently known regarding the performance of these methods in situations where disease susceptibility depends on the cumulative contribution of multiple genetic factors of moderate or low penetrance. Using quantitative genetic theory, we develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)–based methods for assessing risk of polygenic disorders. We show that family history is most useful for highly common, heritable conditions (e.g., coronary artery disease), where it explains roughly 20%–30% of disease heritability, on par with the most successful SNP models based on associations discovered to date. In contrast, we find that for diseases of moderate or low frequency (e.g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases. These results indicate that, for a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history–based counterparts, despite the large fraction of missing heritability that remains to be explained. Our model illustrates the difficulty of using either family history or SNPs for standalone disease prediction. On the other hand, we show that, unlike family history, SNP–based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis.     

The impact of age on genetic risk for common diseases

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.     

Using the Optimal Robust Receiver Operating Characteristic (ROC) Curve for Predictive Genetic Tests

Summary Current ongoing genome‐wide association (GWA) studies represent a powerful approach to uncover common unknown genetic variants causing common complex diseases. The discovery of these genetic variants offers an important opportunity for early disease prediction, prevention, and individualized treatment. We describe here a method of combining multiple genetic variants for early disease prediction, based on the optimality theory of the likelihood ratio (LR). Such theory simply shows that the receiver operating characteristic (ROC) curve based on the LR has maximum performance at each cutoff point and that the area under the ROC curve so obtained is highest among that of all approaches. Through simulations and a real data application, we compared it with the commonly used logistic regression and classification tree approaches. The three approaches show similar performance if we know the underlying disease model. However, for most common diseases we have little prior knowledge of the disease model and in this situation the new method has an advantage over logistic regression and classification tree approaches. We applied the new method to the type 1 diabetes GWA data from the Wellcome Trust Case Control Consortium. Based on five single nucleotide polymorphisms, the test reaches medium level classification accuracy. With more genetic findings to be discovered in the future, we believe a predictive genetic test for type 1 diabetes can be successfully constructed and eventually implemented for clinical use.     

Mouse models of type 1 and type 2 diabetes derived from the same closed colony: genetic susceptibility shared between two types of diabetes

Except for rare subtypes of diabetes, both type 1 and type 2 diabetes are multifactorial diseases in which genetic factors consisting of multiple susceptibility genes and environmental factors contribute to the disease development. Due to complex interaction among multiple susceptibility genes and between genetic and environmental factors, genetic analysis of multifactorial diseases is difficult in humans. Inbred animal models, in which the genetic background is homogeneous and environmental factors can be controlled, are therefore valuable in genetic dissection of multifactorial diseases. We are fortunate to have excellent animal models for both type 1 and type 2 diabetes--the nonobese diabetic (NOD) mouse and the Nagoya-Shibata-Yasuda (NSY) mouse, respectively. Congenic mapping of susceptibility genes for type 1 diabetes in the NOD mouse has revealed that susceptibility initially mapped as a single locus often consists of multiple components on the same chromosome, indicating the importance of congenic mapping in defining genes responsible for polygenic diseases. The NSY mouse is an inbred animal model of type 2 diabetes established from Jcl:ICR, from which the NOD mouse was also derived. We have recently mapped three major loci contributing to type 2 diabetes in the NSY mouse. Interestingly, support intervals where type 2 diabetes susceptibility genes were mapped in the NSY mouse overlapped the regions where type 1 diabetes susceptibility genes have been mapped in the NOD mouse. Although additional evidence is needed, it may be possible that some of the genes predisposing to diabetes are derived from a common ancestor contained in the original closed colony, contributing to type 1 diabetes in the NOD mouse and type 2 diabetes in the NSY mouse. Such genes, if they exist, will provide valuable information on etiological pathways common to both forms of diabetes, for the establishment of effective methods for prediction, prevention, and intervention in both type 1 and type 2 diabetes.     

Genetic risk factors of venous thrombosis

Venous thrombosis, whose main clinical presentations include deep vein thrombosis and pulmonary embolism, represents a major health problem worldwide. Numerous conditions are known to predispose to venous thrombosis and these conditions are commonly referred to as risk indicators or risk factors. Generally accepted or "classically" acquired risk factors for venous thromboembolism include advanced age, prolonged immobilisation, surgery, fractures, use of oral contraceptives and hormone replacement therapy, pregnancy, puerperium, cancer and antiphospholipid syndrome. In addition to these well-established risk factors for venous thrombosis, several lines of evidence that have emerged over the past few decades indicate a role of novel genetic risk factors, mainly related to the haemostatic system, in influencing thrombotic risk. The most significant breakthrough has been the confirmation of the concept that inherited hypercoagulable conditions are present in a large proportion of patients with venous thromboembolic disease. These include mutations in the genes that encode antithrombin, protein C and protein S, and the factor V Leiden and factor II G20210 A mutations. Moreover, plasmatic risk indicators, such as hyperhomocysteinemia and elevated concentrations of factors II, VIII, IX, XI and fibrinogen, have also been documented. This extensive list of genetic and acquired factors serves to illustrate that a single cause of venous thrombosis does not exist and that this condition should be considered as a complex or multifactorial trait. Complex traits can be understood by assuming an interaction between different mutations in candidate susceptibility genes. The risk that is associated with each genetic defect may be relatively low in isolation but the simultaneous presence of several mutations may dramatically increase disease susceptibility. Moreover, environmental factors may interact with one or more genetic variations to add further to the risk. The analysis of genetic risk factors and plasmatic factors, together with private life style and environmental factors, has contributed significantly to our understanding of the genetic predisposition to venous thrombosis.     

A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait.     

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.     

Evaluating online direct-to-consumer marketing of genetic tests: informed choices or buyers beware?

Commercialization of genetic technologies is expanding the horizons for the marketing and sales of genetic tests direct-to-consumers (DTCs). This study assesses the information provision and access requirements that are in place for genetic tests that are being advertised DTC over the Internet. Sets of key words specific to DTC genetic testing were entered into popular Internet search engines to generate a list of 24 companies engaging in DTC advertising. Company requirements for physician mediation, genetic counseling arrangements, and information provision were coded to develop categories for quantitative analysis within each variable. Results showed that companies offering risk assessment and diagnostic testing were most likely to require that testing be mediated by a clinician, and to recommend physician-arranged counseling. Companies offering enhancement testing were less likely to require physician mediation of services and more likely to provide long-distance genetic counseling. DTC advertisements often provided information on disease etiology; this was most common in the case of multifactorial diseases. The majority of companies cited outside sources to support the validity of claims about clinical utility of the tests being advertised; companies offering risk assessment tests most frequently cited all information sources. DTC advertising for genetic tests that lack independent professional oversight raises troubling questions about appropriate use and interpretation of these tests by consumers and carries implications for the standards of patient care. These implications are discussed in the context of a public healthcare system.     

Pathway Analysis of GWAS Provides New Insights into Genetic Susceptibility to 3 Inflammatory Diseases

Although the introduction of genome-wide association studies (GWAS) have greatly increased the number of genes associated with common diseases, only a small proportion of the predicted genetic contribution has so far been elucidated. Studying the cumulative variation of polymorphisms in multiple genes acting in functional pathways may provide a complementary approach to the more common single SNP association approach in understanding genetic determinants of common disease. We developed a novel pathway-based method to assess the combined contribution of multiple genetic variants acting within canonical biological pathways and applied it to data from 14,000 UK individuals with 7 common diseases. We tested inflammatory pathways for association with Crohn''s disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) with 4 non-inflammatory diseases as controls. Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC). The generalisability of these predictive models was tested on an independent birth cohort from Northern Finland. Multiple canonical inflammatory pathways showed highly significant associations (p 10⁻³–10⁻²⁰) with CD, T1D and RA. Variable selection identified on average a set of 205 SNPs (149 genes) for T1D, 350 SNPs (189 genes) for RA and 493 SNPs (277 genes) for CD. The pattern of polymorphisms at these SNPS were found to be highly predictive of T1D (91% AUC) and RA (85% AUC), and weakly predictive of CD (60% AUC). The predictive ability of the T1D model (without any parameter refitting) had good predictive ability (79% AUC) in the Finnish cohort. Our analysis suggests that genetic contribution to common inflammatory diseases operates through multiple genes interacting in functional pathways.     

Predicting Mendelian Disease-Causing Non-Synonymous Single Nucleotide Variants in Exome Sequencing Studies

Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ∼22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.     

Retrospective versus prospective score tests for genetic association with case‐control data

Since the seminal work of Prentice and Pyke, the prospective logistic likelihood has become the standard method of analysis for retrospectively collected case‐control data, in particular for testing the association between a single genetic marker and a disease outcome in genetic case‐control studies. In the study of multiple genetic markers with relatively small effects, especially those with rare variants, various aggregated approaches based on the same prospective likelihood have been developed to integrate subtle association evidence among all the markers considered. Many of the commonly used tests are derived from the prospective likelihood under a common‐random‐effect assumption, which assumes a common random effect for all subjects. We develop the locally most powerful aggregation test based on the retrospective likelihood under an independent‐random‐effect assumption, which allows the genetic effect to vary among subjects. In contrast to the fact that disease prevalence information cannot be used to improve efficiency for the estimation of odds ratio parameters in logistic regression models, we show that it can be utilized to enhance the testing power in genetic association studies. Extensive simulations demonstrate the advantages of the proposed method over the existing ones. A real genome‐wide association study is analyzed for illustration.     

Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms

Genome-wide association studies (GWAS) are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a “black box” in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs) by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF), suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.     

Stochastic search gene suggestion: a Bayesian hierarchical model for gene mapping

Mapping the genes for a complex disease, such as diabetes or rheumatoid arthritis (RA), involves finding multiple genetic loci that may contribute to the onset of the disease. Pairwise testing of the loci leads to the problem of multiple testing. Looking at haplotypes, or linear sets of loci, avoids multiple tests but results in a contingency table with sparse counts, especially when using marker loci with multiple alleles. We propose a hierarchical Bayesian model for case-parent triad data that uses a conditional logistic regression likelihood to model the probability of transmission to a diseased child. We define hierarchical prior distributions on the allele main effects to model the genetic dependencies present in the human leukocyte antigen (HLA) region of chromosome 6. First, we add a hierarchical level for model selection that accounts for both locus and allele selection. This allows us to cast the problem of identifying genetic loci relevant to the disease into a problem of Bayesian variable selection. Second, we attempt to include linkage disequilibrium as a covariance structure in the prior for model coefficients. We evaluate the performance of the procedure with some simulated examples and then apply our procedure to identifying genetic markers in the HLA region that influence risk for RA. Our software is available on the website http://www.epigenetic.org/Linkage/ssgs-public/.     

Genetic Insights into Cardiometabolic Risk Factors

Many biochemical traits are recognised as risk factors, which contribute to or predict the development of disease. Only a few are in widespread use, usually to assist with treatment decisions and motivate behavioural change. The greatest effort has gone into evaluation of risk factors for cardiovascular disease and/or diabetes, with substantial overlap as ‘cardiometabolic’ risk. Over the past few years many genome-wide association studies (GWAS) have sought to account for variation in risk factors, with the expectation that identifying relevant polymorphisms would improve our understanding or prediction of disease; others have taken the direct approach of genomic case-control studies for the corresponding diseases. Large GWAS have been published for coronary heart disease and Type 2 diabetes, and also for associated biomarkers or risk factors including body mass index, lipids, C-reactive protein, urate, liver function tests, glucose and insulin. Results are not encouraging for personal risk prediction based on genotyping, mainly because known risk loci only account for a small proportion of risk. Overlap of allelic associations between disease and marker, as found for low density lipoprotein cholesterol and heart disease, supports a causal association, but in other cases genetic studies have cast doubt on accepted risk factors. Some loci show unexpected effects on multiple markers or diseases. An intriguing feature of risk factors is the blurring of categories shown by the correlation between them and the genetic overlap between diseases previously thought of as distinct. GWAS can provide insight into relationships between risk factors, biomarkers and diseases, with potential for new approaches to disease classification.     

Epidemiological evaluation of the use of genetics to improve the predictive value of disease risk factors.

The prevention of common diseases relies on identifying risk factors and implementing intervention in high-risk groups. Nevertheless, most known risk factors have low positive predictive value (PPV) and low population-attributable fraction (PAF) for diseases (e.g., cholesterol and coronary heart disease). With advancing genetic technology, it will be possible to refine the risk-factor approach to target intervention to individuals with risk factors who also carry disease-susceptibility allele(s). We provide an epidemiological approach to assess the impact of genetic testing on the PPV and PAF associated with risk factors. Under plausible models of interaction between a risk factor and a genotype, we derive values of PPV and PAF associated with the joint effects of a risk factor and a genotype. The use of genetic testing can markedly increase the PPV of a risk factor. PPV increases with increasing genotype-risk factor interaction and increasing marginal relative risk associated with the factor, but it is inversely proportional to the prevalences of the genotype and the factor. For example, for a disease with lifetime risk of 1%, if all the risk-factor effect is confined to individuals with a susceptible genotype, a risk factor with 10% prevalence and disease relative risk of 2 in the population will have a disease PPV of 1.8%, but it will have a PPV of 91.8% among persons with a genotype of 1% prevalence. On the other hand, genetic testing and restriction of preventive measures to those susceptible may decrease the PAF of the risk factor, especially at low prevalences of the risk factor and genotype.(ABSTRACT TRUNCATED AT 250 WORDS)     

Modellvorstellungen zur Genetik multifaktorieller Krankheiten

In contrast to monogenic diseases, a straightforward genotype–phenotype relationship is unlikely for multifactorial diseases because of a number of genetic and nongenetic factors, including genetic heterogeneity, gene–gene and gene–environment interactions, and epigenetic mechanisms. As a consequence, the relative risk of particular genetic variants will generally be small, which implies that large sample sizes are required for their initial identification. No conclusions as to the frequency and diversity of the causative genetic variation can generally be drawn from the prevalence of a disease alone. Homogenization of the genetic background of the study population and the use of simple and clearly defined phenotypes together with “educated guesses” in candidate gene and gene–environment studies appear to be the most promising way to identify the genetic factors underlying multifactorial diseases. Replication of initial disease association findings, particularly for rare variants, should be carried out in populations that are genetically as similar as possible to the original population.     

The power comparison of the haplotype-based collapsing tests and the variant-based collapsing tests for detecting rare variants in pedigrees

Background

Both common and rare genetic variants have been shown to contribute to the etiology of complex diseases. Recent genome-wide association studies (GWAS) have successfully investigated how common variants contribute to the genetic factors associated with common human diseases. However, understanding the impact of rare variants, which are abundant in the human population (one in every 17 bases), remains challenging. A number of statistical tests have been developed to analyze collapsed rare variants identified by association tests. Here, we propose a haplotype-based approach. This work inspired by an existing statistical framework of the pedigree disequilibrium test (PDT), which uses genetic data to assess the effects of variants in general pedigrees. We aim to compare the performance between the haplotype-based approach and the rare variant-based approach for detecting rare causal variants in pedigrees.

Results

Extensive simulations in the sequencing setting were carried out to evaluate and compare the haplotype-based approach with the rare variant methods that drew on a more conventional collapsing strategy. As assessed through a variety of scenarios, the haplotype-based pedigree tests had enhanced statistical power compared with the rare variants based pedigree tests when the disease of interest was mainly caused by rare haplotypes (with multiple rare alleles), and vice versa when disease was caused by rare variants acting independently. For most of other situations when disease was caused both by haplotypes with multiple rare alleles and by rare variants with similar effects, these two approaches provided similar power in testing for association.

Conclusions

The haplotype-based approach was designed to assess the role of rare and potentially causal haplotypes. The proposed rare variants-based pedigree tests were designed to assess the role of rare and potentially causal variants. This study clearly documented the situations under which either method performs better than the other. All tests have been implemented in a software, which was submitted to the Comprehensive R Archive Network (CRAN) for general use as a computer program named rvHPDT.     

Prediction of individual genetic risk of complex disease

Most common diseases are caused by multiple genetic and environmental factors. In the last 2 years, genome-wide association studies (GWAS) have identified polymorphisms that are associated with risk to common disease, but the effect of any one risk allele is typically small. By combining information from many risk variants, will it be possible to predict accurately each individual person's genetic risk for a disease? In this review we consider the lessons from GWAS and the implications for genetic risk prediction to common disease. We conclude that with larger GWAS sample sizes or by combining studies, accurate prediction of genetic risk will be possible, even if the causal mutations or the mechanisms by which they affect susceptibility are unknown.