Using genetic association data to guide drug discovery and development: Review of methods and applications.

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

The benefits of using genetic information to design prevention trials.

Clinical trials for preventative therapies are complex and costly endeavors focused on individuals likely to develop disease in a short time frame, randomizing them to treatment groups, and following them over time. In such trials, statistical power is governed by the rate of disease events in each group and cost is determined by randomization, treatment, and follow-up. Strategies that increase the rate of disease events by enrolling individuals with high risk of disease can significantly reduce study size, duration, and cost. Comprehensive study of common, complex diseases has resulted in a growing list of robustly associated genetic markers. Here, we evaluate the utility--in terms of trial size, duration, and cost--of enriching prevention trial samples by combining clinical information with genetic risk scores to identify individuals at greater risk of disease. We also describe a framework for utilizing genetic risk scores in these trials and evaluating the associated cost and time savings. With type 1 diabetes (T1D), type 2 diabetes (T2D), myocardial infarction (MI), and advanced age-related macular degeneration (AMD) as examples, we illustrate the potential and limitations of using genetic data for prevention trial design. We illustrate settings where incorporating genetic information could reduce trial cost or duration considerably, as well as settings where potential savings are negligible. Results are strongly dependent on the genetic architecture of the disease, but we also show that these benefits should increase as the list of robustly associated markers for each disease grows and as large samples of genotyped individuals become available.

A multi-SNP locus-association method reveals a substantial fraction of the missing heritability.

There are many known examples of multiple semi-independent associations at individual loci; such associations might arise either because of true allelic heterogeneity or because of imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (from the Genetic Investigation of Anthropometric Traits consortium study), we show that for height, body mass index (BMI), and waist-to-hip ratio (WHR), 3%, 2%, and 1%, respectively, of additional phenotypic variance can be explained on top of the previously reported 10% (height), 1.5% (BMI), and 1% (WHR). The method also permitted a substantial increase (by up to 50%) in the number of loci that replicate in a discovery-validation design. Specifically, we identified 74 loci at which the multi-SNP, a linear combination of SNPs, explains significantly more variance than does the best individual SNP. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs that are not in linkage disequilibrium with the lead SNP, suggesting a major contribution of allelic heterogeneity to the missing heritability.

Maternal inheritance of a promoter variant in the imprinted PHLDA2 gene significantly increases birth weight.

Birth weight is an important indicator of both perinatal and adult health, but little is known about the genetic factors contributing to its variability. Intrauterine growth restriction is a leading cause of perinatal morbidity and mortality and is also associated with adult disease. A significant correlation has been reported between lower birth weight and increased expression of the maternal PHLDA2 allele in term placenta (the normal imprinting pattern was maintained). However, a mechanism that explains the transcriptional regulation of PHLDA2 on in utero growth has yet to be described. In this study, we sequenced the PHLDA2 promoter region in 263 fetal DNA samples to identify polymorphic variants. We used a luciferase reporter assay to identify in the PHLDA2 promoter a 15 bp repeat sequence (RS1) variant that significantly reduces PHLDA2-promoter efficiency. RS1 genotyping was then performed in three independent white European normal birth cohorts. Meta-analysis of all three (total n = 9,433) showed that maternal inheritance of RS1 resulted in a significant 93 g increase in birth weight (p = 0.01; 95% confidence interval [CI] = 22-163). Moreover, when the mother was homozygous for RS1, the influence on birth weight was 155 g (p = 0.04; 95% CI = 9-300), which is a similar magnitude to the reduction in birth weight caused by maternal smoking.

Refinement of variant selection for the LDL cholesterol genetic risk score in the diagnosis of the polygenic form of clinical familial hypercholesterolemia and replication in samples from 6 countries.

BACKGROUND: Familial hypercholesterolemia (FH) is an autosomal-dominant disorder caused by mutations in 1 of 3 genes. In the 60% of patients who are mutation negative, we have recently shown that the clinical phenotype can be associated with an accumulation of common small-effect LDL cholesterol (LDL-C)-raising alleles by use of a 12-single nucleotide polymorphism (12-SNP) score. The aims of the study were to improve the selection of SNPs and replicate the results in additional samples. METHODS: We used ROC curves to determine the optimum number of LDL-C SNPs. For replication analysis, we genotyped patients with a clinical diagnosis of FH from 6 countries for 6 LDL-C-associated alleles. We compared the weighted SNP score among patients with no confirmed mutation (FH/M-), those with a mutation (FH/M+), and controls from a UK population sample (WHII). RESULTS: Increasing the number of SNPs to 33 did not improve the ability of the score to discriminate between FH/M- and controls, whereas sequential removal of SNPs with smaller effects/lower frequency showed that a weighted score of 6 SNPs performed as well as the 12-SNP score. Metaanalysis of the weighted 6-SNP score, on the basis of polymorphisms in CELSR2 (cadherin, EGF LAG 7-pass G-type receptor 2), APOB (apolipoprotein B), ABCG5/8 [ATP-binding cassette, sub-family G (WHITE), member 5/8], LDLR (low density lipoprotein receptor), and APOE (apolipoprotein E) loci, in the independent FH/M- cohorts showed a consistently higher score in comparison to the WHII population (P < 2.2 × 10(-16)). Modeling in individuals with a 6-SNP score in the top three-fourths of the score distribution indicated a >95% likelihood of a polygenic explanation of their increased LDL-C. CONCLUSIONS: A 6-SNP LDL-C score consistently distinguishes FH/M- patients from healthy individuals. The hypercholesterolemia in 88% of mutation-negative patients is likely to have a polygenic basis.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

Comparative analysis of genome-wide association studies signals for lipids, diabetes, and coronary heart disease: Cardiovascular Biomarker Genetics Collaboration.

AIMS: To evaluate the associations of emergent genome-wide-association study-derived coronary heart disease (CHD)-associated single nucleotide polymorphisms (SNPs) with established and emerging risk factors, and the association of genome-wide-association study-derived lipid-associated SNPs with other risk factors and CHD events. METHODS AND RESULTS: Using two case-control studies, three cross-sectional, and seven prospective studies with up to 25 000 individuals and 5794 CHD events we evaluated associations of 34 genome-wide-association study-identified SNPs with CHD risk and 16 CHD-associated risk factors or biomarkers. The Ch9p21 SNPs rs1333049 (OR 1.17; 95% confidence limits 1.11-1.24) and rs10757274 (OR 1.17; 1.09-1.26), MIA3 rs17465637 (OR 1.10; 1.04-1.15), Ch2q36 rs2943634 (OR 1.08; 1.03-1.14), APC rs383830 (OR 1.10; 1.02, 1.18), MTHFD1L rs6922269 (OR 1.10; 1.03, 1.16), CXCL12 rs501120 (OR 1.12; 1.04, 1.20), and SMAD3 rs17228212 (OR 1.11; 1.05, 1.17) were all associated with CHD risk, but not with the CHD biomarkers and risk factors measured. Among the 20 blood lipid-related SNPs, LPL rs17411031 was associated with a lower risk of CHD (OR 0.91; 0.84-0.97), an increase in Apolipoprotein AI and HDL-cholesterol, and reduced triglycerides. SORT1 rs599839 was associated with CHD risk (OR 1.20; 1.15-1.26) as well as total- and LDL-cholesterol, and apolipoprotein B. ANGPTL3 rs12042319 was associated with CHD risk (OR 1.11; 1.03, 1.19), total- and LDL-cholesterol, triglycerides, and interleukin-6. CONCLUSION: Several SNPs predicting CHD events appear to involve pathways not currently indexed by the established or emerging risk factors; others involved changes in blood lipids including triglycerides or HDL-cholesterol as well as LDL-cholesterol. The overlapping association of SNPs with multiple risk factors and biomarkers supports the existence of shared points of regulation for these phenotypes.

Deep sequencing of the LRRK2 gene in 14,002 individuals reveals evidence of purifying selection and independent origin of the p.Arg1628Pro mutation in Europe.

Genetic variation in LRRK2 predisposes to Parkinson disease (PD), which underpins its development as a therapeutic target. Here, we aimed to identify novel genotype-phenotype associations that might support developing LRRK2 therapies for other conditions. We sequenced the 51 exons of LRRK2 in cases comprising 12 common diseases (n = 9,582), and in 4,420 population controls. We identified 739 single-nucleotide variants, 62% of which were observed in only one person, including 316 novel exonic variants. We found evidence of purifying selection for the LRRK2 gene and a trend suggesting that this is more pronounced in the central (ROC-COR-kinase) core protein domains of LRRK2 than the flanking domains. Population genetic analyses revealed that LRRK2 is not especially polymorphic or differentiated in comparison to 201 other drug target genes. Among Europeans, we identified 17 carriers (0.13%) of pathogenic LRRK2 mutations that were not significantly enriched within any disease or in those reporting a family history of PD. Analysis of pathogenic mutations within Europe reveals that the p.Arg1628Pro (c4883G>C) mutation arose independently in Europe and Asia. Taken together, these findings demonstrate how targeted deep sequencing can help to reveal fundamental characteristics of clinically important loci.

Comparison of methods and sampling designs to test for association between rare variants and quantitative traits.

Genome-wide association studies succeeded in finding genetic variants associated with various phenotypes, but a large portion of the predicted genetic contribution to many traits remains unknown. One plausible explanation is that some missing variation is due to rare variants. Latest sequencing technology facilitates the investigation of such rare variants, but their statistical analysis remains challenging. For quantitative traits, a commonly used approach is to contrast the frequency of putatively functional rare variants between subjects in the two tails of the trait distribution. The contrast is usually performed by Fisher's exact or similar test. These tests are conservative as they discard trait rank information and are most useful under the unrealistic homogeneity assumption (i.e., variants have similar effects). We propose, and investigate via simulations, various designs for resequencing studies and statistical methods that incorporate information about rank, predicted function and allow for heterogeneity of effects. We propose designs which accommodate heterogeneity by sequencing both tails and the middle of the trait and novel statistical tests for trend, for heterogeneity and for a combination of the two. The conclusions of the simulations are four fold: (1) sequencing both tails and the middle of the trait distributions is desirable when heterogeneity is suspected, (2) trend and heterogeneity statistics should be used alongside other methods, (3) using rank information improves power over Fisher's exact test when the number of rare variants is not very large and (4) due to high misclassification rates, incorporating current predictions of a variant's function does not improve power.

Bayesian semiparametric meta-analysis for genetic association studies.

We present a Bayesian semiparametric model for the meta-analysis of candidate gene studies with a binary outcome. Such studies often report results from association tests for different, possibly study-specific and non-overlapping genetic markers in the same genetic region. Meta-analyses of the results at each marker in isolation are seldom appropriate as they ignore the correlation that may exist between markers due to linkage disequilibrium (LD) and cannot assess the relative importance of variants at each marker. Also such marker-wise meta-analyses are restricted to only those studies that have typed the marker in question, with a potential loss of power. A better strategy is one which incorporates information about the LD between markers so that any combined estimate of the effect of each variant is corrected for the effect of other variants, as in multiple regression. Here we develop a Bayesian semiparametric model which models the observed genotype group frequencies conditional to the case/control status and uses pairwise LD measurements between markers as prior information to make posterior inference on adjusted effects. The approach allows borrowing of strength across studies and across markers. The analysis is based on a mixture of Dirichlet processes model as the underlying semiparametric model. Full posterior inference is performed through Markov chain Monte Carlo algorithms. The approach is demonstrated on simulated and real data.

Multilocus Bayesian meta-analysis of gene-disease associations.

Meta-analysis is a vital tool in genetic epidemiology. However, meta-analyses to identify gene-disease associations are compromised when contributing studies have typed partially overlapping sets of markers. Currently, only marginal analyses are possible, and these are restricted to the subset of studies typing that marker. This does not allow full use of available data and leads to the confounding of marker effects by closely associated markers. We present a Bayesian approach that exploits prior information on underlying haplotypes to allow multi-marker analysis incorporating data from all relevant studies of a gene or region, irrespective of the markers typed. We present results from application of our approach to data on a possible association between PDE4D and ischemic stroke.

Searching for genotype-phenotype structure: using hierarchical log-linear models in Crohn disease.

There has been considerable recent success in the detection of gene-disease associations. We consider here the development of tools that facilitate the more detailed characterization of the effect of a genetic variant on disease. We replace the simplistic classification of individuals according to a single binary disease indicator with classification according to a number of subphenotypes. This more accurately reflects the underlying biological complexity of the disease process, but it poses additional analytical difficulties. Notably, the subphenotypes that make up a particular disease are typically highly associated, and it becomes difficult to distinguish which genes might be causing which subphenotypes. Such problems arise in many complex diseases. Here, we concentrate on an application to Crohn disease (CD). We consider this problem as one of model selection based upon log-linear models, fitted in a Bayesian framework via reversible-jump Metropolis-Hastings approach. We evaluate the performance of our suggested approach with a simple simulation study and then apply the method to a real data example in CD, revealing a sparse disease structure. Most notably, the associated NOD2.908G-->R mutation appears to be directly related to more severe disease behaviors, whereas the other two associated NOD2 variants, 1007L-->FS and 702R-->W, are more generally related to disease in the small bowel (ileum and jejenum). The ATG16L1.300T-->A variant appears to be directly associated with only disease of the small bowel.

Effect modification by population dietary folate on the association between MTHFR genotype, homocysteine, and stroke risk: a meta-analysis of genetic studies and randomised trials.

BACKGROUND: The MTHFR 677C→T polymorphism has been associated with raised homocysteine concentration and increased risk of stroke. A previous overview showed that the effects were greatest in regions with low dietary folate consumption, but differentiation between the effect of folate and small-study bias was difficult. A meta-analysis of randomised trials of homocysteine-lowering interventions showed no reduction in coronary heart disease events or stroke, but the trials were generally set in populations with high folate consumption. We aimed to reduce the effect of small-study bias and investigate whether folate status modifies the association between MTHFR 677C→T and stroke in a genetic analysis and meta-analysis of randomised controlled trials. METHODS: We established a collaboration of genetic studies consisting of 237 datasets including 59,995 individuals with data for homocysteine and 20,885 stroke events. We compared the genetic findings with a meta-analysis of 13 randomised trials of homocysteine-lowering treatments and stroke risk (45,549 individuals, 2314 stroke events, 269 transient ischaemic attacks). FINDINGS: The effect of the MTHFR 677C→T variant on homocysteine concentration was larger in low folate regions (Asia; difference between individuals with TT versus CC genotype, 3·12 µmol/L, 95% CI 2·23 to 4·01) than in areas with folate fortification (America, Australia, and New Zealand, high; 0·13 µmol/L, -0·85 to 1·11). The odds ratio (OR) for stroke was also higher in Asia (1·68, 95% CI 1·44 to 1·97) than in America, Australia, and New Zealand, high (1·03, 0·84 to 1·25). Most randomised trials took place in regions with high or increasing population folate concentrations. The summary relative risk (RR) of stroke in trials of homocysteine-lowering interventions (0·94, 95% CI 0·85 to 1·04) was similar to that predicted for the same extent of homocysteine reduction in large genetic studies in populations with similar folate status (predicted RR 1·00, 95% CI 0·90 to 1·11). Although the predicted effect of homocysteine reduction from large genetic studies in low folate regions (Asia) was larger (RR 0·78, 95% CI 0·68 to 0·90), no trial has evaluated the effect of lowering of homocysteine on stroke risk exclusively in a low folate region. INTERPRETATION: In regions with increasing levels or established policies of population folate supplementation, evidence from genetic studies and randomised trials is concordant in suggesting an absence of benefit from lowering of homocysteine for prevention of stroke. Further large-scale genetic studies of the association between MTHFR 677C→T and stroke in low folate settings are needed to distinguish effect modification by folate from small-study bias. If future randomised trials of homocysteine-lowering interventions for stroke prevention are undertaken, they should take place in regions with low folate consumption. FUNDING: Full funding sources listed at end of paper (see Acknowledgments).

Bayesian meta-analysis of genetic association studies with different sets of markers.

Robust assessment of genetic effects on quantitative traits or complex-disease risk requires synthesis of evidence from multiple studies. Frequently, studies have genotyped partially overlapping sets of SNPs within a gene or region of interest, hampering attempts to combine all the available data. By using the example of C-reactive protein (CRP) as a quantitative trait, we show how linkage disequilibrium in and around its gene facilitates use of Bayesian hierarchical models to integrate informative data from all available genetic association studies of this trait, irrespective of the SNP typed. A variable selection scheme, followed by contextualization of SNPs exhibiting independent associations within the haplotype structure of the gene, enhanced our ability to infer likely causal variants in this region with population-scale data. This strategy, based on data from a literature based systematic review and substantial new genotyping, facilitated the most comprehensive evaluation to date of the role of variants governing CRP levels, providing important information on the minimal subset of SNPs necessary for comprehensive evaluation of the likely causal relevance of elevated CRP levels for coronary-heart-disease risk by Mendelian randomization. The same method could be applied to evidence synthesis of other quantitative traits, whenever the typed SNPs vary among studies, and to assist fine mapping of causal variants.

High-dose vitamin D supplements are not associated with linear growth in a large Finnish cohort.

High vitamin D intake in childhood has been suggested to have an adverse influence on linear growth. In Finland, in the mid-1960s the official recommendation for infant vitamin D supplementation was 2000 IU/d (50 µg/d). We investigated whether high-dose vitamin D supplementation in infancy was associated with subsequent growth in height. We used data from a prospective population-based birth cohort study including all children due to be born in the 2 northernmost provinces in Finland in 1966 (12,058 live-births, coverage 96%). Information on each participant's height was collected at birth and ages 1, 14, and 31 y, as were possible confounding factors (data for analyses available from 10,060 singletons). Information on the frequency and dose of vitamin D supplementation was collected in 1967 when participants were 1 y of age. A weak association was found between frequency of vitamin D supplementation with greater height at age 1 y (P = 0.005), which was explained by birth characteristics and maternal and social factors (adjusted P = 0.34). Neither frequency nor dose of vitamin D supplementation was associated with height at 14 or 31 y (P > 0.13). To conclude, contrary to proposed evidence suggesting that vitamin D has a negative influence on growth rate at a dosage of ~2000 IU/d, supplementation at this level in the Northern Finland Birth Cohort was not associated with reduced height at any age studied.

Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies.

Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants, which is a plausible scenario for many complex diseases. We show that simultaneous analysis of the entire set of SNPs from a genome-wide study to identify the subset that best predicts disease outcome is now feasible, thanks to developments in stochastic search methods. We used a Bayesian-inspired penalised maximum likelihood approach in which every SNP can be considered for additive, dominant, and recessive contributions to disease risk. Posterior mode estimates were obtained for regression coefficients that were each assigned a prior with a sharp mode at zero. A non-zero coefficient estimate was interpreted as corresponding to a significant SNP. We investigated two prior distributions and show that the normal-exponential-gamma prior leads to improved SNP selection in comparison with single-SNP tests. We also derived an explicit approximation for type-I error that avoids the need to use permutation procedures. As well as genome-wide analyses, our method is well-suited to fine mapping with very dense SNP sets obtained from re-sequencing and/or imputation. It can accommodate quantitative as well as case-control phenotypes, covariate adjustment, and can be extended to search for interactions. Here, we demonstrate the power and empirical type-I error of our approach using simulated case-control data sets of up to 500 K SNPs, a real genome-wide data set of 300 K SNPs, and a sequence-based dataset, each of which can be analysed in a few hours on a desktop workstation.

Review of factors that influence the abundance of ions produced in a tandem mass spectrometer and statistical methods for discovering these factors.

Proteomic technologies are important because they link genes, proteins and disease. The identification of proteins and peptides has been revolutionized in the last decade by the use of mass spectrometry. This method is highly sensitive and much faster than the chemical reactions used previously because it can fragment peptides in seconds rather than in hours or days. Proteins are digested with an enzyme, usually trypsin, and the resulting peptides are fragmented in a tandem mass spectrometer (MS/MS). The masses of the fragment ions formed in the MS/MS can be used to identify the sequence of amino acids in the peptides. However, a number of different factors have been found to influence the amount of the various types of fragment ion formed. In this article, we review these factors and their interrelation together with the statistical methods used to discover them. Information on the number of fragment ions formed is at present underused in peptide identification algorithms, and fully utilizing this information could improve current algorithms.

The clonal evolution of metastases from primary serous epithelial ovarian cancers.

Several models of evolution from primary cancers to metastases have been proposed; but the most widely accepted is the clonal evolution model proposed for colorectal cancer in which tumors develop by a process of linear clonal evolution driven by the accumulation of somatic genetic alterations. Various other models of cancer progression and metastasis have been proposed, including parallel evolution and the same gene model. The aim of this study was to investigate the evolution of metastases from primary cancer in 22 patients diagnosed with high-grade serous epithelial ovarian cancer. We established somatic genetic profiles based on the pattern of loss of heterozygosity, in several different regions of tumor tissue within the primary tumor and metastatic deposits from each case. Maximum parsimony tree analysis was used to examine the evolutionary relationship between the primary and metastatic samples for each patient. In addition, we investigated the extent of genetic heterogeneity within and between metastatic tumors compared with primary ovarian tumors. Our data suggest that most, if not all, metastases are clonally related to the primary tumors. However, the data oppose a single model of linear-clonal evolution whereby a late stage clone within the primary tumor acquires additional genetic changes that enable metastatic progression. Instead, the data support a model in which primary ovarian cancers have a common clonal origin, but become polyclonal, with different clones at both early and late stages of genetic divergence acquiring the ability to progress to metastasis.

Genetic risk factors for variant Creutzfeldt-Jakob disease: a genome-wide association study.

BACKGROUND: Human and animal prion diseases are under genetic control, but apart from PRNP (the gene that encodes the prion protein), we understand little about human susceptibility to bovine spongiform encephalopathy (BSE) prions, the causal agent of variant Creutzfeldt-Jakob disease (vCJD). METHODS: We did a genome-wide association study of the risk of vCJD and tested for replication of our findings in samples from many categories of human prion disease (929 samples) and control samples from the UK and Papua New Guinea (4254 samples), including controls in the UK who were genotyped by the Wellcome Trust Case Control Consortium. We also did follow-up analyses of the genetic control of the clinical phenotype of prion disease and analysed candidate gene expression in a mouse cellular model of prion infection. FINDINGS: The PRNP locus was strongly associated with risk across several markers and all categories of prion disease (best single SNP [single nucleotide polymorphism] association in vCJD p=2.5 x 10(-17); best haplotypic association in vCJD p=1 x 10(-24)). Although the main contribution to disease risk was conferred by PRNP polymorphic codon 129, another nearby SNP conferred increased risk of vCJD. In addition to PRNP, one technically validated SNP association upstream of RARB (the gene that encodes retinoic acid receptor beta) had nominal genome-wide significance (p=1.9 x 10(-7)). A similar association was found in a small sample of patients with iatrogenic CJD (p=0.030) but not in patients with sporadic CJD (sCJD) or kuru. In cultured cells, retinoic acid regulates the expression of the prion protein. We found an association with acquired prion disease, including vCJD (p=5.6 x 10(-5)), kuru incubation time (p=0.017), and resistance to kuru (p=2.5 x 10(-4)), in a region upstream of STMN2 (the gene that encodes SCG10). The risk genotype was not associated with sCJD but conferred an earlier age of onset. Furthermore, expression of Stmn2 was reduced 30-fold post-infection in a mouse cellular model of prion disease. INTERPRETATION: The polymorphic codon 129 of PRNP was the main genetic risk factor for vCJD; however, additional candidate loci have been identified, which justifies functional analyses of these biological pathways in prion disease.

Predicting clinical outcome in patients diagnosed with synchronous ovarian and endometrial cancer.

PURPOSE: Patients with synchronous ovarian and endometrial cancers may represent cases of a single primary tumor with metastasis (SPM) or dual primary tumors (DP). The diagnosis given will influence the patient's treatment and prognosis. Currently, a diagnosis of SPM or DP is made using histologic criteria, which are frequently unable to make a definitive diagnosis. EXPERIMENTAL DESIGN: In this study, we used genetic profiling to make a genetic diagnosis of SPM or DP in 90 patients with synchronous ovarian/endometrial cancers. We compared genetic diagnoses in these patients with the original histologic diagnoses and evaluated the clinical outcome in this series of patients based on their diagnoses. RESULTS: Combining genetic and histologic approaches, we were able make a diagnosis in 88 of 90 cases, whereas histology alone was able to make a diagnosis in only 64 cases. Patients diagnosed with SPM had a significantly worse survival than patients with DP (P = 0.002). Patients in which both tumors were of endometrioid histology survived longer than patients of other histologic subtypes (P = 0.025), and patients diagnosed with SPM had a worse survival if the mode of spread was from ovary to endometrium rather than from endometrium to ovary (P = 0.019). CONCLUSIONS: Genetic analysis may represent a powerful tool for use in clinical practice for distinguishing between SPM and DP in patients with synchronous ovarian/endometrial cancer and predicting disease outcome. The data also suggest a hitherto uncharacterized level of heterogeneity in these cases, which, if accurately defined, could lead to improved treatment and survival.