How many genes underlie the occurrence of common complex diseases in the population?

Association between the FAAH C385A variant (rs324420) and obesity-related traits: a systematic review

BACKGROUND

Overweight and obesity are the consequence of a sustained positive energy balance. Twin studies show high heritability rates pointing to genetics as one of the principal risk factors. By 2022, genomic studies led to the identification of almost 300 obesity-associated variants that could help to fill the gap of the high heritability rates. The endocannabinoid system is a critical regulator of metabolism for its effects on the central nervous system and peripheral tissues. Fatty acid amide hydrolase (FAAH) is a key enzyme in the inactivation of one of the two endocannabinoids, anandamide, and of its congeners. The rs324420 variant within the FAAH gene is a nucleotide missense change at position 385 from cytosine to adenine, resulting in a non-synonymous amino acid substitution from proline to threonine in the FAAH enzyme. This change increases sensitivity to proteolytic degradation, leading to reduced FAAH levels and increased levels of anandamide, associated with obesity-related traits. However, association studies of this variant with metabolic parameters have found conflicting results. This work aims to perform a systematic review of the existing literature on the association of the rs324420 variant in the FAAH gene with obesity and its related traits.

METHODS

A literature search was conducted in PubMed, Web of Science, and Scopus. A total of 645 eligible studies were identified for the review.

RESULTS/CONCLUSIONS

After the identification, duplicate elimination, title and abstract screening, and full-text evaluation, 28 studies were included, involving 28 183 individuals. We show some evidence of associations between the presence of the variant allele and higher body mass index, waist circumference, fat mass, and waist-to-hip ratio levels and alterations in glucose and lipid homeostasis. However, this evidence should be taken with caution, as many included studies did not report a significant difference between genotypes. These discordant results could be explained mainly by the pleiotropy of the endocannabinoid system, the increase of other anandamide-like mediators metabolized by FAAH, and the influence of gene-environment interactions. More research is necessary to study the endocannabinoidomic profiles and their association with metabolic diseases.

Gene Polymorphism and Total Genetic Score in Martial Arts Athletes with Different Athletic Qualifications

The personalized approach in sports genetics implies considering the allelic variants of genes in polymorphic loci when adjusting the training process of athletes. The personalized approach is used both in sports genetics and in medicine to identify the influence of genotype on the manifestations of human physical qualities that allow to achieve high sports results or to assess the impact of genotype on the development and course of diseases. The impact of genes of the renin-angiotensin and kinin-bradykinin systems in the development of cardiovascular disease in athletes has not been defined. This study aims to determine the polymorphisms of four genes (ACE, BDKRB2, PPARGC1A and NOS3) and the total genetic score to reveal the predisposition to the formation of physical qualities in martial arts athletes with different athletic abilities. The products of these four genes are involved in the control of blood pressure. The allelic variants of these genes are associated with the development of the physical quality "endurance" and have an indirect influence on the formation of speed and power qualities. The total genetic score (TGS: from 0 to 100 arbitrary units) was calculated from the genotype score in each polymorphism. The athletes were divided into Group I with high and Group II with low qualifications depending on their sports success. Single nucleotide polymorphisms (SNPs) are identified through restriction endonucleases cleavage for PCR amplicons for discriminating between alleles of the target genes ACE (rs4646994), BDKRB2 (rs5810761), PPARGC1A (rs8192673) and NOS3 (rs1799983). Significant differences between the allelic variants of target genes and athletic ability were found between Group I and Group II for genotype G/G of NOS3 gene and genotypes Gly/Gly and Gly/Ser of PPARGC1A gene. The data obtained confirm that athletes with unfavourable genotypes are excluded in the screening phase because their endurance is not fully developed to the required level in martial arts. Martial arts athletes with the highest TGS have the highest skill level. Polymorphic loci of four genes whose products are involved in blood pressure control (ACE, BDKRB2, NOS3 and PPARGC1A) can be used in martial arts not only to determine predisposition to cardiovascular disease but also to predispose to the development of speed and power qualities and endurance. The total genetic score can serve as a tool for predicting athletic success.

Coagulation Factor XIII Val34Leu Polymorphism in the Prediction of Premature Cardiovascular Events-The Results of Two Meta-Analyses

Background: Polymorphisms within the gene that encodes for coagulation factor XIII (FXIII) have been suggested to be involved in the pathogeneses of ischemic stroke (IS) and myocardial infarction (MI). The Val34Leu polymorphism is one of the most commonly analysed FXIII polymorphisms. However, studies on the role of the Val34Leu polymorphism in the aetiology of vascular diseases often show contradictory results. In the present meta-analysis, we aimed to pool data from available articles to assess the relationship between the FXIII Val34Leu polymorphism and the susceptibilities to IS of undetermined source and premature MI in patients aged below 55 years. Methods: We searched databases (PubMed, Embase, Google Scholar, SciELO, and Medline) using specific keywords (the last search was in January 2022). Eventually, 18 studies (627 cases and 1639 controls for IS; 2595 cases and 4255 controls for MI) met the inclusion criteria. Data were analysed using RevMan 5.4 and StatsDirect 3 link software. The relation between Val34Leu polymorphism and disease was analysed in five genetic models, i.e., dominant, recessive, additive, heterozygous, and allelic. Results: No relation between Val34Leu polymorphism and IS in young adults was observed in all analysed genetic models. For premature MI, significant pooled OR was found between the carrier state of the Leu allele (Val/Leu + Leu/Leu vs. Val/Val) and a lack of MI, suggesting its protective role (OR = 0.80 95%CI 0.64–0.99, p = 0.04). A similar finding was observed for the heterozygous model in MI (Val/Leu vs. Val/Val) (OR = 0.77 95%CI 0.61–0.98, p = 0.03). No relation was found for the recessive, additive, and allelic models in MI. Conclusions: In the population of young adults, no positive correlation was found between the FXIII Val34Leu polymorphism and IS of undetermined source in any of the analysed genetic models. In turn, the carrier state of the 34Leu allele as well as FXIII heterozygotes themselves were found to play a protective role in relation to premature MI.

Association of human platelet alloantigens encoding gene polymorphisms with the risk of Coronary artery disease in Iranian patients

Background

Coronary artery disease (CAD) is characterized by narrowing/ blockade of coronary arteries that is mainly caused by atherosclerotic plaques. Considering the involvement of platelet abnormalities, such as defective aggregation and adhesion, in the cardiovascular-related disorders, genetic variations in human platelet alloantigens (HPA) have been implicated in the CAD susceptibility. Herein, we intended to determine the association of HPA-1 to -6, -9, and -15 biallelic polymorphisms with CAD in an Iranian population.

Methods

In this retrospective case–control study, 200 CAD subjects and 100 matched healthy individuals were enrolled. DNA samples were isolated from peripheral blood samples and genotyping of HPA polymorphisms was accomplished using polymerase chain reaction-sequence-specific primers.

Results

The alleles and genotypes of studied HPA polymorphisms were equally distributed among cases and controls and therefore no statistically significant differences were detected. Univariate analysis identified no association of combined haplotypes with CAD risk. However, multivariate analysis showed a positive association of the‌ HPA1b/2a/3b haplotype with CAD after adjustment for some covariates (including BMI, TG, LDL, FBS and blood pressure) that conferred a CAD susceptibility haplotype (P = 0.015; OR = 2.792; 95% CI 1.45–8.59).

Conclusions

Although alleles, genotypes, and haplotypes of HPA polymorphisms were not associated with CAD risk, HPA1b/2a/3b haplotype was found to be a dependent disease risk haplotype in Iranian population after correcting for confounding factors.

Epistatic effect of Ankyrin repeat and kinase domain containing 1 - Dopamine receptor D2 and catechol-o-methyltransferase single nucleotide polymorphisms on the risk for hazardous use of alcohol in Lithuanian population

AIM

The Lithuanian population has outstanding rates of alcohol consumption and alcohol related mortality. Alteration of brain dopaminergic system play a role in the risk for addiction disorders. We evaluated the association of one single nucleotide polymorphism rs1800497 in the Ankyrin Repeat and Kinase Domain Containing 1 - Dopamine Receptor D2 complex (ANKK1-DRD2) and a catechol-o-methyltransferase (COMT) rs4680 single nucleotide polymorphism with the risk for alcohol use disorder and impulsiveness in Lithuanian population. Both genetic polymorphisms are known to alter brain dopaminergic activity, thus we also investigated the possible interaction effect of these polymorphisms.

METHODS

The study included 329 participants recruited from the local community. Hazardous alcohol use was evaluated using the Alcohol Use Disorder Identification Test (AUDIT). Impulsiveness was measured using the Barratt Impulsiveness Scale - 11 (BIS-11). Between group differences of AUDIT and BIS-11 scores were examined stratified by genetic polymorphisms and their combinations. The independent effect of each polymorphism and their interaction for hazardous alcohol use were evaluated using adjusted logistic regression analyses.

RESULTS

The ANKK1-DRD2 rs1800497 polymorphism was associated with total AUDIT score, but not with the hazardous use of alcohol, as indicated by the AUDIT test cut-off of 8. The COMT rs4680 GG genotype was associated with the hazardous use of alcohol (adjusted OR = 2.094, p = 0.029), but this association was not statistically significant after adjustment for multiple comparisons. Presence of both COMT rs4680 and ANKK1-DRD2 rs1800497 GGxCT/TT polymorphisms was associated with significantly increased risk for hazardous use of alcohol (adjusted OR = 5.016, p = 0.005). The COMT rs4680 and ANKK1-DRD2 rs1800497 genetic polymorphisms, and their combination were not associated with impulsiveness.

CONCLUSIONS

Our study demonstrated that the interaction of COMT rs4680 and ANKK1-DRD2 rs1800497 genetic polymorphisms is associated with a hazardous use of alcohol.

Diverse types of genomic evidence converge on alcohol use disorder risk genes

BACKGROUND

Alcohol use disorder (AUD) is one of the most common forms of substance use disorders with a strong contribution of genetic (50%-60%) and environmental factors. Genome-wide association studies (GWAS) have identified a number of AUD-associated variants, including those in alcohol metabolism genes. These genetic variants may modulate gene expression, making individuals more susceptible to AUD. A long-term alcohol consumption can also change the transcriptome patterns of subjects via epigenetic modulations.

METHODS

To explore the interactive effect of genetic and epigenetic factors on AUD, we conducted a secondary analysis by integrating GWAS, CNV, brain transcriptome and DNA methylation data to unravel novel AUD-associated genes/variants. We applied the mega-analysis of OR (MegaOR) method to prioritise AUD candidate genes (AUDgenes).

RESULTS

We identified a consensus set of 206 AUDgenes based on the multi-omics data. We demonstrated that these AUDgenes tend to interact with each other more frequent than chance expectation. Functional annotation analysis indicated that these AUDgenes were involved in substance dependence, synaptic transmission, glial cell proliferation and enriched in neuronal and liver cells. We obtained a multidimensional evidence that AUD is a polygenic disorder influenced by both genetic and epigenetic factors as well as the interaction of them.

CONCLUSION

We characterised multidimensional evidence of genetic, epigenetic and transcriptomic data in AUD. We found that 206 AUD associated genes were highly expressed in liver, brain cerebellum, frontal cortex, hippocampus and pituitary. Our studies provides important insights into the molecular mechanism of AUD and potential target genes for AUD treatment.

NOTCH3 T6746C and TP53 P72R Polymorphisms Are Associated with the Susceptibility to Diffuse Cutaneous Systemic Sclerosis

Introduction. NOTCH pathway and TP53 protein are involved in the development of fibrosis and autoimmune disorders, respectively. The aim of this study was to evaluate the role of single nucleotide polymorphisms (SNPs) of NOTCH3 and TP53 genes and serum anti-TP53 antibodies with the susceptibility, clinical subset of systemic sclerosis (SSc), and clinical profile of SSc patient, particularly with lung involvement and disease activity. Objects and Methods. 124 white Polish SSc patients (101 with limited cutaneous SSc-lcSSc, and 23 with diffuse cutaneous SSc-dcSSc) and 100 healthy individuals were included in the study. Patients were assessed for the presence of autoantibodies and interstitial lung disease. Two SNPs at position 6746 of NOTCH3 and TP53 genes and serum anti-TP53 antibodies with the susceptibility, clinical subset of systemic sclerosis (SSc), and clinical profile of SSc patient, particularly with lung involvement and disease activity.

Results

The genotypic frequencies of the NOTCH3 and p=0.03; χ ² = 4.63). There was no significant difference between SSc patients and the control population in allele frequencies of both SNPs. The CT + CC genotypes of NOTCH3 and p=0.03; p=0.03; p=0.03; TP53 genes and serum anti-TP53 antibodies with the susceptibility, clinical subset of systemic sclerosis (SSc), and clinical profile of SSc patient, particularly with lung involvement and disease activity. p=0.03.

Conclusion

The CT + CC genotypes of NOTCH3 gene and PR + RR genotypes of the TP53 gene increased the risk of dcSSc development. Moreover, genotypes of CT + CC were associated with the active form of SSc suggesting the role of the NOTCH pathway in the pathogenesis of this disease.NOTCH3 and TP53 genes and serum anti-TP53 antibodies with the susceptibility, clinical subset of systemic sclerosis (SSc), and clinical profile of SSc patient, particularly with lung involvement and disease activity.

Germline Variants in Phosphodiesterase Genes and Genetic Predisposition to Pediatric Adrenocortical Tumors

Phosphodiesterases (PDEs) form a superfamily of enzymes that catalyze the hydrolysis of cyclic nucleotides adenosine 3′5′-cyclic monophosphate (cAMP) and guanosine 3′5′-cyclic monophosphate (cGMP) to their inactive 5′ monophosphates. cAMP plays a critical role as a second messenger in endocrine tissues, and activation of cAMP signaling has been reported in endocrine tumors. Germline variants in PDEs have been associated with benign cortisol-secreting adrenocortical adenomas and testicular germ cell cancer but not adrenocortical carcinoma. We performed whole genome sequencing (WGS) and whole exome sequencing (WES) of paired blood and tumor samples from 37 pediatric adrenocortical tumors (ACTs). Germline inactivating variants in PDEs were observed in 9 of 37 (24%) patients. Tumor DNA analysis revealed loss of heterozygosity, with maintenance of the mutated allele in all cases. Our results suggest that germline variants in PDEs and other regulators of the cAMP-signaling pathway may contribute to pediatric adrenocortical tumorigenesis, perhaps by cooperating with germline hypomorphic mutant TP53 alleles and uniparental disomy of chromosome 11p15 (Beckwith–Wiedemann syndrome).

Era of Intelligent Systems in Healthcare

The aim of this chapter is to prepare the reader for the outstanding trip that she/he embarked when starting reading this book. At first, we shall try to look for answers to some of the most important questions regarding the connection between intelligent systems and healthcare. What are intelligent systems? How can they be used in healthcare? Have they got benefits and prospects? Let us highlight some of the decisive factors for a successful deployment of intelligent systems in healthcare, including intelligent clinical support and intelligent patient management.

A Convergent Study of Genetic Variants Associated With Crohn's Disease: Evidence From GWAS, Gene Expression, Methylation, eQTL and TWAS

Crohn’s Disease (CD) is one of the predominant forms of inflammatory bowel disease (IBD). A combination of genetic and non-genetic risk factors have been reported to contribute to the development of CD. Many high-throughput omics studies have been conducted to identify disease associated risk variants that might contribute to CD, such as genome-wide association studies (GWAS) and next generation sequencing studies. A pressing need remains to prioritize and characterize candidate genes that underlie the etiology of CD. In this study, we collected a comprehensive multi-dimensional data from GWAS, gene expression, and methylation studies and generated transcriptome-wide association study (TWAS) data to further interpret the GWAS association results. We applied our previously developed method called mega-analysis of Odds Ratio (MegaOR) to prioritize CD candidate genes (CDgenes). As a result, we identified consensus sets of CDgenes (62–235 genes) based on the evidence matrix. We demonstrated that these CDgenes were significantly more frequently interact with each other than randomly expected. Functional annotation of these genes highlighted critical immune-related processes such as immune response, MHC class II receptor activity, and immunological disorders. In particular, the constitutive photomorphogenesis 9 (COP9) signalosome related genes were found to be significantly enriched in CDgenes, implying a potential role of COP9 signalosome involved in the pathogenesis of CD. Finally, we found some of the CDgenes shared biological functions with known drug targets of CD, such as the regulation of inflammatory response and the leukocyte adhesion to vascular endothelial cell. In summary, we identified highly confident CDgenes from multi-dimensional evidence, providing insights for the understanding of CD etiology.

Environmental risks outweigh dopaminergic genetic risks for alcohol use and abuse from adolescence through early adulthood

BACKGROUND

Alcohol use is a primary public health concern, particularly among adolescents and young adults. Based on the rapidly growing field of gene-environment models, this study assessed the combined role of environmental and dopamine-related genetic correlates of early alcohol use and abuse.

METHODS

Multilevel growth models assessed trajectories of alcohol use and intoxication and ordered logistic regressions assessed alcohol use disorder among a sample of 12,437 youth from the nationally representative Add Health study who were followed from mid-adolescence through early adulthood.

RESULTS

Endogenous and exogenous stressful life events and social norms supportive of alcohol use from parents and peers were significant predictors of alcohol use, intoxication, and alcohol use disorder, with consistent patterns across males and females. In contrast, a dopamine-system genetic risk score (GRS) was not associated with alcohol use trajectories nor alcohol use disorder in early adulthood, although weak connections emerged between the GRS and growth trajectories of intoxication, indicating that higher GRS predicted more frequent episodes of intoxication during the transition to adulthood but not during adolescence or later 20s. No evidence of gene-environment interactions emerged.

CONCLUSIONS

Results extend a substantial body of prior research primarily assessing single genetic polymorphisms in the dopamine system, suggesting that dopaminergic GRSs may be associated with more problematic alcohol behaviors at some developmental periods, but further, that social norms and stressful life experiences are more consistent correlates of early and problematic alcohol use among youth. These environmental factors present potential targets for research manipulating contexts to identify causal pathways.

Genetic Factors Are Not the Major Causes of Chronic Diseases

The risk of acquiring a chronic disease is influenced by a person’s genetics (G) and exposures received during life (the ‘exposome’, E) plus their interactions (G×E). Yet, investigators use genome-wide association studies (GWAS) to characterize G while relying on self-reported information to classify E. If E and G×E dominate disease risks, this imbalance obscures important causal factors. To estimate proportions of disease risk attributable to G (plus shared exposures), published data from Western European monozygotic (MZ) twins were used to estimate population attributable fractions (PAFs) for 28 chronic diseases. Genetic PAFs ranged from 3.4% for leukemia to 48.6% for asthma with a median value of 18.5%. Cancers had the lowest PAFs (median = 8.26%) while neurological (median = 26.1%) and lung (median = 33.6%) diseases had the highest PAFs. These PAFs were then linked with Western European mortality statistics to estimate deaths attributable to G for heart disease and nine cancer types. Of 1.53 million Western European deaths in 2000, 0.25 million (16.4%) could be attributed to genetics plus shared exposures. Given the modest influences of G-related factors on the risks of chronic diseases in MZ twins, the disparity in coverage of G and E in etiological research is problematic. To discover causes of disease, GWAS should be complemented with exposome-wide association studies (EWAS) that profile chemicals in biospecimens from incident disease cases and matched controls.

Genetic variation and adaptation in Africa: implications for human evolution and disease

Because modern humans originated in Africa and have adapted to diverse environments, African populations have high levels of genetic and phenotypic diversity. Thus, genomic studies of diverse African ethnic groups are essential for understanding human evolutionary history and how this leads to differential disease risk in all humans. Comparative studies of genetic diversity within and between African ethnic groups creates an opportunity to reconstruct some of the earliest events in human population history and are useful for identifying patterns of genetic variation that have been influenced by recent natural selection. Here we describe what is currently known about genetic variation and evolutionary history of diverse African ethnic groups. We also describe examples of recent natural selection in African genomes and how these data are informative for understanding the frequency of many genetic traits, including those that cause disease susceptibility in African populations and populations of recent African descent.

Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case-control cohort analysis

Background

Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD).

Methods

In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7.8 years for model training, testing and validation using seven machine learning methods (partial least square regression, the classification and regression tree, the C5.0 decision tree, random forest, naïve Bayes classification, neural network and support vector machine). We used 17 clinical attributes and 70 single nucleotide polymorphisms (SNPs) of 54 candidate genes to build different models. The top attributes selected by the best-performing models were then used to build models with performance comparable to those using the entire dataset.

Results

Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism were selected by most methods. Models generated by support vector machine (svmRadial) and random forest (cforest) had the best prediction accuracy whereas models derived from naïve Bayes classifier and partial least squares regression had the least optimal performance. Using 10 clinical attributes (systolic and diastolic blood pressure, age, age of diagnosis, triglyceride, white blood cell count, total cholesterol, waist to hip ratio, LDL cholesterol, and alcohol intake) and 5 genetic attributes (UGB G38A, LIPC -514C > T, APOB Thr71Ile, APOC3 3206T > G and APOC3 1100C > T), selected most often by SVM and cforest, we were able to build high-performance models.

Conclusions

Amongst different machine learning methods, svmRadial and cforest had the best performance. Genetic polymorphisms related to inflammation and lipid metabolism warrant further investigation for their associations with DKD.

Genetic associations of type 2 diabetes with islet amyloid polypeptide processing and degrading pathways in asian populations

Type 2 diabetes (T2D) is a complex disease characterized by beta cell dysfunctions. Islet amyloid polypeptide (IAPP) is highly conserved and co-secreted with insulin with over 40% of autopsy cases of T2D showing islet amyloid formation due to IAPP aggregation. Dysregulation in IAPP processing, stabilization and degradation can cause excessive oligomerization with beta cell toxicity. Previous studies examining genetic associations of pathways implicated in IAPP metabolism have yielded conflicting results due to small sample size, insufficient interrogation of gene structure and gene-gene interactions. In this multi-staged study, we screened 89 tag single nucleotide polymorphisms (SNPs) in 6 candidate genes implicated in IAPP metabolism and tested for independent and joint associations with T2D and beta cell dysfunctions. Positive signals in the stage-1 were confirmed by de novo and in silico analysis in a multi-centre unrelated case-control cohort. We examined the association of significant SNPs with quantitative traits in a subset of controls and performed bioinformatics and relevant functional analyses. Amongst the tag SNPs, rs1583645 in carboxypeptidase E (CPE) and rs6583813 in insulin degrading enzyme (IDE) were associated with 1.09 to 1.28 fold increased risk of T2D (P_Meta = 9.4×10⁻³ and 0.02 respectively) in a meta-analysis of East Asians. Using genetic risk scores (GRS) with each risk variant scoring 1, subjects with GRS≥3 (8.2% of the cohort) had 56% higher risk of T2D than those with GRS = 0 (P = 0.01). In a subcohort of control subjects, plasma IAPP increased and beta cell function index declined with GRS (P = 0.008 and 0.03 respectively). Bioinformatics and functional analyses of CPE rs1583645 predicted regulatory elements for chromatin modification and transcription factors, suggesting differential DNA-protein interactions and gene expression. Taken together, these results support the importance of dysregulation of IAPP metabolism in T2D in East Asians.

Genetic polymorphisms of glutathione S-transferase genes GSTM1, GSTT1 and risk of hepatocellular carcinoma

BACKGROUND

A number of case-control studies were conducted to investigate the association of glutathione S-transferase (GST) genetic polymorphisms and hepatocellular carcinoma (HCC) risk. However, these studies have yielded contradictory results. We therefore performed a meta-analysis to derive a more precise estimation of the association between polymorphisms on GSTM1, GSTT1 and HCC.

METHODOLOGY/PRINICPAL FINDINGS

PubMed, EMBASE, ISI web of science and the CNKI databases were systematically searched to identify relevant studies. Data were abstracted independently by two reviewers. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were used to assess the strength of association. Potential sources of heterogeneity were also assessed by subgroup analysis and meta-regression. Funnel plots and Egger's linear regression were used to test publication bias among the articles. A total of 34 studies including 4,463 cases and 6,857 controls were included in this meta-analysis. In a combined analysis, significantly increased HCC risks were found for null genotype of GSTM1 (OR = 1.29, 95% CI: 1.06-1.58; P = 0.01) and GSTT1 (OR = 1.43, 95% CI: 1.22-1.68; P<10(-5)). Potential sources of heterogeneity were explored by subgroup analysis and meta-regression. Significant results were found in East Asians and Indians when stratified by ethnicity; whereas no significant associations were found among Caucasians and African populations. By pooling data from 12 studies that considered combinations of GSTT1 and GSTM1 null genotypes, a statistically significant increased risk for HCC (OR = 1.88, 95% CI: 1.41-2.50; P<10(-4)) was detected for individuals with combined deletion mutations in both genes compared with positive genotypes.

CONCLUSIONS/SIGNIFICANCE

This meta-analysis suggests that the GSTM1 and GSTT1 null genotype may slightly increase the risk of HCC and that interaction between unfavourable GSTs genotypes may exist.

Multiple sclerosis

Multiple sclerosis (MS) is a multifocal demyelinating disease with progressive neurodegeneration caused by an autoimmune response to self-antigens in a genetically susceptible individual. While the formation and persistence of meningeal lymphoid follicles suggest persistence of antigens to drive the continuing inflammatory and humoral response, the identity of an antigen or infectious agent leading to the oligoclonal expansion of B and T cells is unknown. In this review we examine new paradigms for understanding the immunopathology of MS, present recent data defining the common genetic variants underlying disease susceptibility, and explore how improved understanding of immune pathway disruption can inform MS prognosis and treatment decisions.

Functional polymorphisms in the LTF gene and risk of coronary artery stenosis

Plasma lactoferrin concentrations are increased in patients with coronary artery stenosis. We investigated the effects of LTF gene polymorphisms in 305 healthy blood donors and their associations with coronary artery stenosis in 236 patients admitted for coronary angiography. Lactoferrin concentrations were determined by enzyme immunoassay. Genotyping was performed by polymerase chain reaction and DNA sequencing of LTF exons 2 and 4. In the blood donors, the deletion variant of rs10662431 and the G allele of rs1126478 were associated with higher plasma lactoferrin concentrations. The G allele of rs1126478 was more frequent in patients with significant coronary artery stenosis (p = 0.018, p value limit for significance by permutation = 0.030). The association remained significant in logistic regression with adjustment for clinical risk factors (odds ratio 2.485 [95% confidence interval 1.116-5.536], p = 0.026), but was weakened upon the inclusion of plasma lactoferrin (odds ratio 2.295 [0.949-5.550], p = 0.064). Current evidence indicates that rs1126478 affects the antibacterial effect of lactoferrin and that lactoferrin is involved in lipid metabolism. The relationships among lactoferrin genotypes, lactoferrin concentrations, and clinical factors on the risk for atherosclerosis are not fully understood, but the G allele of rs1126478 seems to have a detrimental effect in a European population.

"It's not like judgment day": public understanding of and reactions to personalized genomic risk information

The value of genomic risk assessment depends upon patients making appropriate behavioral changes in response to increased risk leading to disease prevention and early detection. To date, few studies have investigated consumers' response to personalized genomic disease risk information. To address this gap, we conducted semi-structured interviews with 60 adults participating in the Coriell Personalized Medicine Collaborative. The interviews took place after receiving results providing genomic and other risk information for up to eight common complex diseases. We found that participants were most likely to recall results which conferred an increased risk or those of particular personal interest. Participants understood the multi-factorial nature of common complex disease, and generally did not have negative emotional responses or overly deterministic perceptions of their results. Although most participants expressed a desire to use results to improve their health, a minority had actually taken action (behavior change or shared results with their doctor) at the time of the interview. These results suggest that participants have a reasonable understanding of genomic risk information and that provision of genomic risk information may motivate behavior change in some individuals; however additional work is needed to better understand the lack of change seen in the majority of participants.

Genetics of menstrual migraine: the molecular evidence

Migraine is considered to be a multifactorial disorder in which genetic, environmental, and, in the case of menstrual and menstrually related migraine, hormonal events influence the phenotype. Certainly, the role of female sex hormones in migraine has been well established, yet the mechanism behind this well-known relationship remains unclear. This review focuses on the potential role of hormonally related genes in migraine, summarizes results of candidate gene studies to date, and discusses challenges and issues involved in interpreting hormone-related gene results. In light of the molecular evidence presented, we discuss future approaches for analysis with the view to elucidate the complex genetic architecture that underlies the disorder.

New type 2 diabetes risk genes provide new insights in insulin secretion mechanisms

Type 2 diabetes results from the inability of beta cells to increase insulin secretion sufficiently to compensate for insulin resistance. Insulin resistance is thought to result mainly from environmental factors, such as obesity. However, there is compelling evidence that the decline of both insulin sensitivity and insulin secretion have also a genetic component. Recent genome-wide association studies identified several novel risk genes for type 2 diabetes. The vast majority of these genes affect beta cell function by molecular mechanisms that remain unknown in detail. Nevertheless, we and others could show that a group of genes affect glucose-stimulated insulin secretion, a group incretin-stimulated insulin secretion (incretin sensitivity or secretion) and a group proinsulin-to-insulin conversion. The most important so far type 2 diabetes risk gene, TCF7L2, interferes with all three mechanisms. In addition to advancing knowledge in the pathophysiology of type 2 diabetes, the discovery of novel genetic determinants of diabetes susceptibility may help understanding of gene-environment, gene-therapy and gene-gene interactions. It was also hoped that it could make determination of the individual risk for type 2 diabetes feasible. However, the allelic relative risks of most genetic variants discovered so far are relatively low. Thus, at present, clinical criteria assess the risk for type 2 diabetes with greater sensitivity and specificity than the combination of all known genetic variants.

The genetic aspects of multiple sclerosis

The epidemiology of multiple sclerosis has been extensively investigated and two features have consistently emerged: marked geographical variation in prevalence and substantial familial clustering. At first sight, geographic variation would seem to imply an environmental cause for the disease, while familial clustering would seem to suggest that genetic factors have the predominant etiological effect. However, given that geographic variation in prevalence could result from variation in the frequency of genetic risk alleles and that familial clustering might result from shared environmental exposure rather than shared genetic risk alleles, it is clear that these crude inferences are unreliable. Epidemiologists have been resourceful in their attempts to resolve this apparent conflict between “nurture and nature” and have employed a whole variety of sophisticated methods to try and untangle the etiology of multiple sclerosis. The body of evidence that has emerged from these efforts has formed the foundation for decades of research seeking to identify relevant genes and this is the obvious place to start any consideration of the genetics of multiple sclerosis.

Assessing causal relationships in genomics: From Bradford-Hill criteria to complex gene-environment interactions and directed acyclic graphs

Observational studies of human health and disease (basic, clinical and epidemiological) are vulnerable to methodological problems -such as selection bias and confounding- that make causal inferences problematic. Gene-disease associations are no exception, as they are commonly investigated using observational designs. A rich body of knowledge exists in medicine and epidemiology on the assessment of causal relationships involving personal and environmental causes of disease; it includes seminal causal criteria developed by Austin Bradford Hill and more recently applied directed acyclic graphs (DAGs). However, such knowledge has seldom been applied to assess causal relationships in clinical genetics and genomics, even in studies aimed at making inferences relevant for human health. Conversely, incorporating genetic causal knowledge into clinical and epidemiological causal reasoning is still a largely unexplored area.

As the contribution of genetics to the understanding of disease aetiology becomes more important, causal assessment of genetic and genomic evidence becomes fundamental. The method we develop in this paper provides a simple and rigorous first step towards this goal. The present paper is an example of integrative research, i.e., research that integrates knowledge, data, methods, techniques, and reasoning from multiple disciplines, approaches and levels of analysis to generate knowledge that no discipline alone may achieve.

Presymptomatic risk assessment for chronic non-communicable diseases

The prevalence of common chronic non-communicable diseases (CNCDs) far overshadows the prevalence of both monogenic and infectious diseases combined. All CNCDs, also called complex genetic diseases, have a heritable genetic component that can be used for pre-symptomatic risk assessment. Common single nucleotide polymorphisms (SNPs) that tag risk haplotypes across the genome currently account for a non-trivial portion of the germ-line genetic risk and we will likely continue to identify the remaining missing heritability in the form of rare variants, copy number variants and epigenetic modifications. Here, we describe a novel measure for calculating the lifetime risk of a disease, called the genetic composite index (GCI), and demonstrate its predictive value as a clinical classifier. The GCI only considers summary statistics of the effects of genetic variation and hence does not require the results of large-scale studies simultaneously assessing multiple risk factors. Combining GCI scores with environmental risk information provides an additional tool for clinical decision-making. The GCI can be populated with heritable risk information of any type, and thus represents a framework for CNCD pre-symptomatic risk assessment that can be populated as additional risk information is identified through next-generation technologies.

Predictive role of polymorphisms in interleukin-5 receptor alpha-subunit, lipoprotein lipase, integrin A2 and nitric oxide synthase genes on ischemic stroke in type 2 diabetes--an 8-year prospective cohort analysis of 1327 Chinese patients

OBJECTIVE

Ischemic stroke is prevalent in type 2 diabetes and may be due to metabolic, vascular and inflammatory factors. Genetic variants implicated in these pathways may have joint effects on stroke risk. In this proof-of-concept study, we examined gene-gene interactions on risk of incident ischemic stroke in an 8-year prospective cohort of Chinese type 2 diabetic patients.

METHODS

Seventy-seven single nucleotide polymorphisms (SNPs) of 53 candidate genes for cardiovascular disease and inflammation were genotyped in 1327 patients with no past history of ischemic stroke. The association of SNPs with stroke was tested using Cox proportional hazard regression analysis. Permutation procedure was performed to control for multiple statistical comparisons.

RESULTS

Genetic variants including A/A of IL5RA (interleukin-5 alpha subunit) -5091G>A, X/X of LPL (lipoprotein lipase) S447X, A/A of ITGA2 (integrin A2) G873A and T/T or G/T of NOS3 (endothelial nitric oxide synthase) G894T showed significant correlations with incident ischemic stroke. The hazard ratios (HR) increased with number of genetic risk factors reaching an adjusted HR (confidence interval) of 3.68 (1.78-7.62, P=4.4×10(-4)) in those with ≥2 genetic risk factors compared to those without.

CONCLUSION

Polymorphisms in IL5RA, LPL, ITGA2 and NOS3 genes were independently associated with ischemic stroke in Chinese diabetic population.

Heritability of body weight: moving beyond genetics

Obesity is a complex disease, arising from the interaction between several genetic and environmental factors. Until recently, the genetic basis of complex diseases in general, and of obesity in particular, were poorly characterized. While the relatively rare monogenic and syndromic forms of obesity clearly recognize a genetic origin, the actual worldwide epidemics of obesity represent a challenge for the identification of the genetic factors involved, being likely the effect of several loci each having a subtle influence on the phenotypic expression. Progress in DNA analysis techniques and in computational tools, and the increasing level of characterization of the variability of the human genome has recently allowed to study comprehensively the association between genetic variants and obesity. To date, well-conducted and powered genome-wide association studies allowed to consistently identify genomic regions - lying on different chromosomes and affecting different metabolic pathways - influencing the predisposition to the accumulation of body fat, ultimately leading to overweight and obesity. However, the population attributable risk for obesity linked to the most statistically significant loci, like FTO and MC4R, remains discouragingly low, explaining only small fractions of the overall variance of body weight. In the last few years, the role of the complex interaction between genetic determinants and environmental factors in the rapid global increase of obesity has been further challenged by the entry of new players, that is the transcriptional and post-transcriptional regulation, summarized under the emerging discipline of epigenetics. The key challenge now is to move from the identification of causal genes and variants to the integration of different "omics" disciplines, finally allowing the molecular understanding of obesity and related conditions.

Genetic risk sum score comprised of common polygenic variation is associated with body mass index

Genome-wide association studies (GWAS) of body mass index (BMI) using large samples have yielded approximately a dozen robustly associated variants and implicated additional loci. Individually these variants have small effects and in aggregate explain a small proportion of the variance. As a result, replication attempts have limited power to achieve genome-wide significance, even with several thousand subjects. Since there is strong prior evidence for genetic influence on BMI for specific variants, alternative approaches to replication can be applied. Instead of testing individual loci sequentially, a genetic risk sum score (GRSS) summarizing the total number of risk alleles can be tested. In the current study, GRSS comprising 56 top variants catalogued from two large meta-analyses was tested for association with BMI in the Molecular Genetics of Schizophrenia controls (2,653 European-Americans, 973 African-Americans). After accounting for covariates known to influence BMI (ancestry, sex, age), GRSS was highly associated with BMI (p value = 3.19 E-06) although explained a limited amount of the variance (0.66%). However, area under receiver operator criteria curve (AUC) estimates indicated that the GRSS and covariates significantly predicted overweight and obesity classification with maximum discriminative ability for predicting class III obesity (AUC = 0.697). The relative contributions of the individual loci to GRSS were examined post hoc and the results were not due to a few highly significant variants, but rather the result of numerous variants of small effect. This study provides evidence of the utility of a GRSS as an alternative approach to replication of common polygenic variation in complex traits.

Evaluation of the established non-MHC multiple sclerosis loci in an Indian population

BACKGROUND

Multiple sclerosis (MS) is a chronic demyelinating neurodegenerative disorder with a strong genetic component.

OBJECTIVE

The prevalence of MS in India is low compared with white populations of Northern European descent.

METHODS

In order to ascertain whether disease susceptibility genes are the same across different populations, we completed the first investigation in the Indian MS population of 15 MS loci outside of the major histocompatibility (MHC) region that were previously identified and validated with MS susceptibility through genome-wide association and replication studies in white populations.

RESULTS

In total, 197 Indian patients and 197 unrelated controls were analyzed. The most associated single nucleotide polymorphism (SNP) within this study was rs6897932 in the IL7R gene, which showed a strong protective effect in this data set (rs 6897932, OR = 0.5543, 95% CI = 0.37-0.78, p = 0.0009727). Two other SNPs were nominally associated with MS in this dataset, namely CLEC16A rs 12708716 (p = 0.0082, OR = 1.478, 95% CI = 1.106-1.975) and CD226 rs763361 (p = 0.03971, OR = 1.353, CI = 1.014-1.805). For the majority of the remaining SNPs (7/14), the trend for association was in the same direction as in previous studies in the white population.

CONCLUSIONS

Although the power of this study was limited, our preliminary data suggest that disease susceptibility genes in MS in the Indian population may be similar to those of western populations.

Prediction model for knee osteoarthritis based on genetic and clinical information

INTRODUCTION

Osteoarthritis (OA) is the most common bone and joint disease influenced by genetic and environmental factors. Recent association studies have uncovered the genetic factors behind OA, its susceptibility genes, which would enable us to predict disease occurrence based on genotype information. However, most previous studies have evaluated the effects of only a single susceptibility gene, and hence prediction based on such information is not as reliable. Here, we constructed OA-prediction models based on genotype information from a case-control association study and tested their predictability.

METHODS

We genotyped risk alleles of the three susceptibility genes, asporin (ASPN), growth differentiation factor 5 (GDF5), and double von Willebrand factor A domains (DVWA) for a total of 2,158 Japanese subjects (933 OA and 1,225 controls) and statistically analyzed their effects. After that, we constructed prediction models by using the logistic regression analysis.

RESULTS

When the effects of each allele were assumed to be the same and multiplicative, each additional risk allele increased the odds ratio (OR) by a factor of 1.23 (95% confidence interval (CI), 1.12 to 1.34). Individuals with five or six risk alleles showed significantly higher susceptibility when compared with those with zero or one, with an OR of 2.67 (95% CI, 1.46 to 4.87; P = 0.0020). Statistical evaluation of the prediction power of models showed that a model using only genotyping data had poor predictability. We obtained a model with good predictability by incorporating clinical data, which was further improved by rigorous age adjustment.

CONCLUSIONS

Our results showed that consideration of adjusted clinical information, as well as increases in the number of risk alleles to be integrated, is critical for OA prediction by using data from case-control studies. To the authors' knowledge, this is the first report of the OA-prediction model combining both genetic and clinical information.

Human platelet alloantigens HPA-1, HPA-2, and HPA-3 polymorphisms associated with extent of severe coronary artery disease

The contribution of human platelet antigen (HPA)-1 (GPIIb/IIIa), HPA-2 (GPIb/IX), and HPA-3 (GPIIb/IIIa) polymorphisms to the risk of coronary artery disease (CAD) was investigated in 341 CAD patients and 316 matched control subjects. HPA genotyping was performed by PCR-SSP. Regression analysis was employed in assessing the contribution of these variants to CAD risk. The frequency of HPA-1b (P = .009) and HPA-3b (P = .004) alleles, and HPA-1a/1b (P = .045), HPA-1b/1b (P = .007), and HPA-3b/3b (P = .008) genotypes were higher in patients than control subjects. No significant association was demonstrated between the HPA variants and 1-, 2- and 3-vessel disease. HPA-1b/2a/3b (Pc = .021) and HPA-1b/2b/3a (Pc = .002) haplotypes were positively associated with CAD, thereby conferring a disease susceptibility nature to these haplotypes. Multivariate analysis confirmed the positive association of HPA-1b/2a/3b (aOR = 3.72; 95% CI = 1.49-9.28), and in addition identified HPA-1b/2a/3a (aOR = 2.49; 95% CI = 1.06-5.86) to be positively associated with CAD, after adjusting for a number of covariates. Our results demonstrate positive association of HPA variants and specific HPA-1/HPA-2/HPA-3 haplotypes with CAD in Tunisians.

Genetic epidemiology of age-related osteoporosis and its clinical applications

Osteoporosis is an important and complex disorder that is highly prevalent worldwide. This disease poses a major challenge to modern medicine and its treatment is associated with high costs. Numerous studies have endeavored to decipher the pathogenesis of this disease. The clinical assessment of patients often incorporates information about a family history of osteoporotic fractures. Indeed, the observation of an increased risk of fracture in an individual with a positive parental history of hip fracture provides strong evidence for the heritability of osteoporosis. The onset and progression of osteoporosis are generally controlled by multiple genetic and environmental factors, as well as interactions between them, with rare cases determined by a single gene. In an attempt to identify the genetic markers of complex diseases such as osteoporosis, there has been a move away from traditional linkage mapping studies and candidate gene association studies to higher-density genome-wide association studies. The advent of high-throughput technology enables genotyping of millions of DNA markers in the human genome, and consequently the identification and characterization of causal variants and loci that underlie osteoporosis. This Review presents an overview of the major findings since 2007 and clinical applications of these genome-wide linkage and association studies.

The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer

Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., ~140–160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual’s CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci associated with CRC before the question of the potential utility of germline genomic profiling can be definitively answered.

Current status on Alzheimer disease molecular genetics: from past, to present, to future

Linkage studies, candidate gene and whole-genome association studies have resulted in a tremendous amount of putative risk genes for Alzheimer's disease (AD). Yet, besides the three causal genes-amyloid precursor protein and presenilin 1 and 2 genes-and one risk gene apolipoprotein E (APOE), no single functional risk variant was identified. Discussing the possible involvement of rare alleles and other types of genetic variants, this review summarizes the current knowledge on the genetic spectrum of AD and integrates different approaches and recent discoveries by genome-wide association studies.

Evidence for polygenic susceptibility to multiple sclerosis--the shape of things to come

It is well established that the risk of developing multiple sclerosis is substantially increased in the relatives of affected individuals and that most of this increase is genetically determined. The observed pattern of familial recurrence risk has long suggested that multiple variants are involved, but it has proven difficult to identify individual risk variants and little has been established about the genetic architecture underlying susceptibility. By using data from two independent genome-wide association studies (GWAS), we demonstrate that a substantial proportion of the thousands of variants that individually fail to show statistically significant evidence of association have allele frequencies in cases that are skewed away from the null distribution through the effects of multiple as-yet-unidentified risk loci. The collective effect of 12,627 SNPs with Cochran-Mantel-Haenszel test (p < 0.2) in our discovery GWAS set optimally explains approximately 3% of the variance in MS risk in our independent target GWAS set, estimated by Nagelkerke's pseudo-R(2). This model has a highly significant fit (p = 9.90E-19). These results statistically demonstrate a polygenic component to MS susceptibility and suggest that the risk alleles identified to date represent just the tip of an iceberg of risk variants likely to include hundreds of modest effects and possibly thousands of very small effects.

Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism

BACKGROUND

Although several candidate gene polymorphisms (SNPs) have been associated with increased risk of type 2 diabetes mellitus (T2DM), relatively few studies have assessed the ability of T2DM candidate genes to assess the risk of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and T2DM beyond the information provided by clinical risk factors.

OBJECTIVE

To test whether the inclusion of genetic markers in a regression model provides a better assessment of the risk of IFG, IGT, and T2DM than a model based only on non-genetic risk factors commonly assessed in clinical settings.

METHODS

Subjects (n = 485; 213 parents, 272 offspring) from the Quebec Family Study, not known to haveT2DM, were measured for several risk factors and underwent an oral glucose tolerance test. Thirty-eight SNPs in 25 susceptibility/ candidate genes previously reported to be associated with T2DM were genotyped. In order to identify risk factors associated with IFG/IGT/T2DM, two logistic regression models were tested: a full model (FM) including age, sex, body mass index (BMI), systolic and diastolic blood pressure, smoking status, and the 38 SNPs; and a reduced model (RM), in which the SNPs were dropped, which allowed us to test the null-hypothesis that the markers are not associated with the risk of IFG/IGT/T2DM. Performances of the models were compared by using a likelihood ratio test and the receiver-operating characteristic curves (ROC).The area under the curve (AUC) was calculated from the ROC curve.

RESULTS

The analyses showed that age (P < 0.0001), BMI (P < 0.0001), and six variants (IGF2BP2 rs4402960, P = 0.002; ADIPOQ+276 G>T, P = 0.004; UCP2Ala55Val, P = 0.01; CDKN2AI2B rs3731201, P = 0.02; rs495490, P = 0.02, and rsl 0811661, P = 0.03) were significantly associated with the risk of IFG/IGT/T2DM. Dropping genetic markers from the analysis significantly reduced the fit of the model to the data (chi-square = 38.98, P < 0.00001 contrasting RM to FM), suggesting that the genetic markers are significantly associated with the risk of IFG/IGT/T2DM. Furthermore, the AUC was higher for FM than for RM (0.85 (95% CI 0.81-0.89) versus 0.81 (95% CI 0.76-0.85), P = 0.004).

CONCLUSION

Our results suggest that combining genetic markers with traditional clinical risk factors has the potential to improve our ability to assess the risk of complex diseases such as T2DM.

CYP1A1 and CYP3A4 polymorphic variations in Delhi population of Northern India

Cytochrome P450 (CYP) 1A1 and CYP3A4 are important phase I xenobiotic metabolizing enzymes involved in the metabolism of numbers of toxins, endogenous hormones and pharmaceutical drugs. Polymorphisms in these phase I genes can alter enzyme activity and are known to be associated with cancer susceptibility related to environmental toxins and hormone exposure. Their genotypes may also display ethnicity dependent population frequencies. The present study was aimed to determine the frequencies of commonly known functional polymorphisms of CYP1A1 and CYP3A4 in North Indian population. Allelic frequency of CYP1A1 polymorphisms, m1, m2 and m4 were observed to be 40.3, 31.2 and 0% respectively. Frequency of CYP3A4*1B polymorphism was 0%. We observed inter as well as intra ethnic variation in the distribution of frequency of these polymorphisms. Analysis of polymorphisms in these genes might help in predicting the risk of cancer. Our results emphasize the need for more such studies in "high risk populations".

Bayes factors in complex genetics

The past few years have seen tremendous progress in our understanding of the genetics underlying complex disease, with associated variants being identified in dozens of traits. Despite the fact that this growing body of empirical evidence unequivocally shows the necessity for extreme levels of significance and large samples sizes, the reasoning behind these requirements is not always appreciated. As genome-wide association studies reach the limits of their resolution in the search for rarer and weaker effects, the need for appropriate design and interpretation will become ever more important. If the genetic analysis of complex disease is to avoid accumulating false positive claims, as it has in the past, then researchers will need to allow for less tangible variables such as power and prior odds rather than relying exclusively on significance when assessing the results of these studies. In this review, the basic foundations of association testing are explained from a Bayesian perspective and the potential benefits of Bayes factors as a means of measuring the weight of evidence in support of an association are described.

Bayes factors in complex genetics

The past few years have seen tremendous progress in our understanding of the genetics underlying complex disease, with associated variants being identified in dozens of traits. Despite the fact that this growing body of empirical evidence unequivocally shows the necessity for extreme levels of significance and large samples sizes, the reasoning behind these requirements is not always appreciated. As genome-wide association studies reach the limits of their resolution in the search for rarer and weaker effects, the need for appropriate design and interpretation will become ever more important. If the genetic analysis of complex disease is to avoid accumulating false positive claims, as it has in the past, then researchers will need to allow for less tangible variables such as power and prior odds rather than relying exclusively on significance when assessing the results of these studies. In this review, the basic foundations of association testing are explained from a Bayesian perspective and the potential benefits of Bayes factors as a means of measuring the weight of evidence in support of an association are described.

Causal relationship of susceptibility genes to ischemic stroke: comparison to ischemic heart disease and biochemical determinants

Interrelationships between genetic and biochemical factors underlying ischemic stroke and ischemic heart disease are poorly understood. We: 1) undertook the most comprehensive meta-analysis of genetic polymorphisms in ischemic stroke to date; 2) compared genetic determinants of ischemic stroke with those of ischemic heart disease, and 3) compared effect sizes of gene-stroke associations with those predicted from independent biochemical data using a mendelian randomization strategy. Electronic databases were searched up to January 2009. We identified: 1) 187 ischemic stroke studies (37,481 cases; 95,322 controls) interrogating 43 polymorphisms in 29 genes; 2) 13 meta-analyses testing equivalent polymorphisms in ischemic heart disease; and 3) for the top five gene-stroke associations, 146 studies (65,703 subjects) describing equivalent gene-biochemical relationships, and 28 studies (46,928 subjects) describing biochemical-stroke relationships. Meta-analyses demonstrated positive associations with ischemic stroke for factor V Leiden Gln506, ACE I/D, MTHFR C677T, prothrombin G20210A, PAI-1 5G allele and glycoprotein IIIa Leu33Pro polymorphisms (ORs: 1.11 – 1.60). Most genetic associations show congruent levels of risk comparing ischemic stroke with ischemic heart disease, but three genes—glycoprotein IIIa, PAI-1 and angiotensinogen—show significant dissociations. The magnitudes of stroke risk observed for factor V Leiden, ACE, MTHFR and prothrombin, but not PAI-1, polymorphisms, are consistent with risks associated with equivalent changes in activated protein C resistance, ACE activity, homocysteine, prothrombin, and PAI-1 levels, respectively. Our results demonstrate causal relationships for four of the most robust genes associated with stroke while also showing that PAI-1 4G/5G polymorphism influences cardiovascular risk via a mechanism not simply related to plasma levels of PAI-1 (or tPA) alone.

How many genetic variants remain to be discovered?

A great majority of genetic markers discovered in recent genome-wide association studies have small effect sizes, and they explain only a small fraction of the genetic contribution to the diseases. How many more variants can we expect to discover and what study sizes are needed? We derive the connection between the cumulative risk of the SNP variants to the latent genetic risk model and heritability of the disease. We determine the sample size required for case-control studies in order to achieve a certain expected number of discoveries in a collection of most significant SNPs. Assuming similar allele frequencies and effect sizes of the currently validated SNPs, complex phenotypes such as type-2 diabetes would need approximately 800 variants to explain its 40% heritability. Much smaller numbers of variants are needed if we assume rare-variants but higher penetrance models. We estimate that up to 50,000 cases and an equal number of controls are needed to discover 800 common low-penetrant variants among the top 5000 SNPs. Under common and rare low-penetrance models, the very large studies required to discover the numerous variants are probably at the limit of practical feasibility. Under rare-variant with medium- to high-penetrance models (odds-ratios between 1.6 and 4.0), studies comparable in size to many existing studies are adequate provided the genotyping technology can interrogate more and rarer variants.

Using lifetime risk estimates in personal genomic profiles: estimation of uncertainty

Personal genome tests are now offered direct-to-consumer (DTC) via genetic variants identified by genome-wide association studies (GWAS) for common diseases. Tests report risk estimates (age-specific and lifetime) for various diseases based on genotypes at multiple loci. However, uncertainty surrounding such risk estimates has not been systematically investigated. With breast cancer as an example, we examined the combined effect of uncertainties in population incidence rates, genotype frequency, effect sizes, and models of joint effects among genetic variants on lifetime risk estimates. We performed simulations to estimate lifetime breast cancer risk for carriers and noncarriers of genetic variants. We derived population-based cancer incidence rates from Surveillance, Epidemiology, and End Results (SEER) Program and comparative international data. We used data for non-Hispanic white women from 2003 to 2005. We derived genotype frequencies and effect sizes from published GWAS and meta-analyses. For a single genetic variant in FGFR2 gene (rs2981582), combination of uncertainty in these parameters produced risk estimates where upper and lower 95% simulation intervals differed by more than 3-fold. Difference in population incidence rates was the largest contributor to variation in risk estimates. For a panel of five genetic variants, estimated lifetime risk of developing breast cancer before age 80 for a woman that carried all risk variants ranged from 6.1% to 21%, depending on assumptions of additive or multiplicative joint effects and breast cancer incidence rates. Epidemiologic parameters involved in computation of disease risk have substantial uncertainty, and cumulative uncertainty should be properly recognized. Reliance on point estimates alone could be seriously misleading.