Beyond odds ratios--communicating disease risk based on genetic profiles

Ethical issues in the application of genome-wide association studies to US military recruitment and personnel assignments

Genome-wide association studies seek to associate an organism's genotypes with phenotypes. The goal of such research is to identify specific genetic variants that may be used to predict an individual's risk for a specific physical or mental disease. Recently, it has been recommended that policymakers in the USA should employ genomic surveillance so that it can be used for initial military personnel selection and personnel assignments. However, such a proposal highlights the necessity of subjecting such recommendations to rigorous ethical analysis, including concerns regarding recruitment, transparency and the return of genetic results.

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

Two Novel Functional Mutations in Promoter Region of SCN3B Gene Associated with Atrial Fibrillation

The sodium voltage-gated channel beta subunit 3 (SCN3B) plays a crucial role in electrically excitable cells and conduction tissue in the heart. Some previous studies have established that genetic modification in sodium voltage-channel genes encoding for the cardiac β-subunits, such as SCN1B, SCN2B, SCN3B and SCN4B, can result in atrial fibrillation (AF). In the current study, we identified two rare variants in 5′UTR (NM_018400.4: c.-324C>A, rs976125894 and NM_018400.4: c.-303C>T, rs1284768362) of SCN3B in two unrelated lone AF patients. Our further functional studies discovered that one of them, the A allele of c.-324C>A (rs976125894), can improve transcriptional activity and may raise SCN3B expression levels. The A allele of c.-324C>A (rs976125894) has higher transcriptional activity when it interacts with GATA4, as we confirmed transcription factor GATA4 is a regulator of SCN3B. To the best of our knowledge, the current study is the first to demonstrate that the gain-of-function mutation of SCN3B can produce AF and the first to link a mutation occurring in the non-coding 5′UTR region of SCN3B to lone AF. The work also offers empirical proof that GATA4 is a critical regulator of SCN3B gene regulation. Our findings may serve as an encyclopedia for AF susceptibility variants and can also provide insight into the investigation of the functional mechanisms behind AF variants discovered by genetic methods.

Contribution of a genetic risk score to ethnic differences in fatty liver disease

BACKGROUND AND AIMS

Susceptibility to fatty liver disease (FLD) varies among individuals and between racial/ethnic groups. Several genetic variants influence FLD risk, but whether these variants explain racial/ethnic differences in FLD prevalence is unclear. We examined the contribution of genetic risk factors to racial/ethnic-specific differences in FLD.

METHODS

A case-control study comparing FLD patients (n = 1194) and population-based controls (n = 3120) was performed. Patient characteristics, FLD risk variants (PNPLA3-rs738409 + rs6006460, TM6SF2-rs58542926, HSD17B13-rs80182459 + rs72613567, MBOAT7/TMC4-rs641738, and GCKR-rs1260326) and a multi-locus genetic risk score (GRS) were examined. The odds of FLD for individuals with different risk factor burdens were determined.

RESULTS

Hispanics and Whites were over-represented (56% vs. 38% and 36% vs. 29% respectively) and Blacks under-represented (5% vs. 23%) among FLD patients, compared to the population from which controls were selected (p < .001). Among cases and controls, Blacks had a lower and Hispanics a greater, net number of risk alleles than Whites (p < .001). GRS was associated with increased odds of FLD (OR_Q5vsQ1  = 8.72 [95% CI = 5.97-13.0], p = 9.8 × 10^-28 ), with the association being stronger in Hispanics (OR_Q5vsQ1  = 14.8 [8.3-27.1]) than Blacks (OR_Q5vsQ1  = 3.7 [1.5-11.5], P-interaction = 0.002). After accounting for GRS, the odds of FLD between Hispanics and Whites did not differ significantly (OR = 1.06 [0.87-1.28], p = .58), whereas Blacks retained much lower odds of FLD (OR = 0.21, [0.15-0.30], p < .001).

CONCLUSIONS

Blacks had a lower and Hispanics a greater FLD risk allele burden than Whites. These differences contributed to, but did not fully explain, racial/ethnic differences in FLD prevalence. Identification of additional factors protecting Blacks from FLD may provide new targets for prevention and treatment of FLD.

Evaluating the Potential of Polygenic Risk Score to Improve Colorectal Cancer Screening

BACKGROUND

Colorectal cancer has high incidence and associated mortality worldwide. Screening programs are recommended for men and women over 50. Intermediate screens such as fecal immunochemical testing (FIT) select patients for colonoscopy with suboptimal sensitivity. Additional biomarkers could improve the current scenario.

METHODS

We included 2,893 individuals with a positive FIT test. They were classified as cases when a high-risk lesion for colorectal cancer was detected after colonoscopy, whereas the control group comprised individuals with low-risk or no lesions. 65 colorectal cancer risk genetic variants were genotyped. Polygenic risk score (PRS) and additive models for risk prediction incorporating sex, age, FIT value, and PRS were generated.

RESULTS

Risk score was higher in cases compared with controls [per allele OR = 1.04; 95% confidence interval (CI), 1.02-1.06; P < 0.0001]. A 2-fold increase in colorectal cancer risk was observed for subjects in the highest decile of risk alleles (≥65), compared with those in the first decile (≤54; OR = 2.22; 95% CI, 1.59-3.12; P < 0.0001). The model combining sex, age, FIT value, and PRS reached the highest accuracy for identifying patients with a high-risk lesion [cross-validated area under the ROC curve (AUROC): 0.64; 95% CI, 0.62-0.66].

CONCLUSIONS

This is the first investigation analyzing PRS in a two-step colorectal cancer screening program. PRS could improve current colorectal cancer screening, most likely for higher at-risk subgroups. However, its capacity is limited to predict colorectal cancer risk status and should be complemented by additional biomarkers.

IMPACT

PRS has capacity for risk stratification of colorectal cancer suggesting its potential for optimizing screening strategies alongside with other biomarkers.

Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries

BACKGROUND

Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk.

METHODS

Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds.

RESULTS

Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases.

CONCLUSION

Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.

Predicting nonrecovery in adults with incident traffic injuries including post-traumatic headache

IMPORTANCE

The management of traffic injuries is challenging for clinicians. Knowledge about predictors of nonrecovery from traffic injuries may help to improve patient care.

OBJECTIVE

To develop a prediction model for self-reported overall nonrecovery from traffic injuries six months post-collision in adults with incident traffic injuries including post-traumatic headache (PTH).

DESIGN

Inception cohort studies of adults with incident traffic injuries (including PTH) injured in traffic collisions between November 1997 and December 1999 in Saskatchewan, Canada; and between January 2004 and January 2005 in Sweden.

METHODS

Prediction model development and geographical external validation.

SETTING

The Saskatchewan cohort (development) was population-based (N = 4,162). The Swedish cohort (validation) (N = 379) were claimants from two insurance companies covering 20% of cars driven in Sweden in 2004.

PARTICIPANTS

All adults injured in traffic collisions who completed a baseline questionnaire within 30 days of collision. Excluded were those hospitalized > 2 days, lost consciousness > 30 min, or reported headache < 3/10 on the numerical rating scale. Follow-up rates for both cohorts were approximately 80%.

PREDICTORS

Baseline sociodemographic, pre-injury, and injury factors.

OUTCOME

Self-reported nonrecovery from all injuries (not "all better (cured)" on the self-perceived recovery scale) six months after traffic collision.

RESULTS

Both cohorts were predominantly female (69.8% in Saskatchewan, 65.2% in Sweden), with median ages 35.9 years (Saskatchewan), and 38.0 years (Sweden). Predictors were age, low back pain, symptoms in arms or hands, hearing problems, sleeping problems, pre-existing headache, and lower recovery expectations. With a positive score (i.e., ≥0.85 probability), the model can rule in the presence of self-reported nonrecovery from all injuries at six months (development: specificity = 91.3%, 95% CI 89.2%-93.0%; sensitivity = 27.8%, 95% CI 26.0%-29.7%; positive likelihood ratio (LR + ) = 3.2, 95% CI 2.5-4.0; negative likelihood ratio (LR-) = 0.79, 95% CI 0.76-0.82; validation: specificity = 72.6%, 95% CI 61.4%-81.5%; sensitivity = 60.5%, 95% CI 53.9%-66.7%); LR+ = 2.2, 95% CI 1.5-3.3; LR- = 0.5, 95% CI 0.4-0.7).

CONCLUSIONS AND RELEVANCE

In adults with incident traffic injuries including PTH, predictors other than those related to baseline head and neck pain drive overall nonrecovery. Developing and testing interventions targeted at the modifiable predictors may help to improve outcomes for adults after traffic collision.

Polygenic Risk Scores in Prostate Cancer Risk Assessment and Screening

More than 40% of the risk of developing prostate cancer (PCa) is from genetic factors. Genome-wide association studies have led to the discovery of more than 140 variants associated with PCa risk. Polygenic risk scores (PRS) generated using these variants show promise in identifying individuals at much higher (and lower) lifetime risk than the average man. PCa PRS also improve the predictive value of prostate-specific antigen screening, may inform the age for starting PCa screening, and are informative for development of more aggressive tumors. Despite the promise, few clinical trials have evaluated the benefit of PCa PRS for clinical care.

Development and validation of a model predicting post-traumatic headache six months after a motor vehicle collision in adults

IMPORTANCE

The prognosis of post-traumatic headache is poorly understood.

OBJECTIVE

To develop and validate a prognostic model to predict the presence of post-traumatic headache six months after a traffic collision in adults with incident post-traumatic headache.

DESIGN

Secondary analyses of adults with incident post-traumatic headache injured in traffic collisions between November 1997 and December 1999 in Saskatchewan, Canada (development cohort); and between January 2004 and January 2005 in Sweden (validation cohort).

SETTING

The Saskatchewan cohort (development) was population-based (N = 4162). The Swedish cohort (validation) (N = 379) were claimants from two insurance companies covering 20 % of cars driven in Sweden in 2004.

PARTICIPANTS

All adults injured in traffic collisions who completed a baseline questionnaire within 30 days of collision. Excluded were those hospitalized >2 days, lost consciousness >30 min, or reported headache <3/10 on the numerical rating scale. Follow-up rates for both cohorts were approximately 80 %.

PREDICTORS

Baseline sociodemographic, pre-injury, and injury factors.

OUTCOME

Self-reported headache pain intensity ≥3 (numerical rating scale) six months after injury.

RESULTS

Both cohorts were predominantly female (69.7 % in Saskatchewan, 65.2 % in Sweden), with median ages 35.9 years (Saskatchewan), and 38.0 years (Sweden). Predictors were age, work status, headache pain intensity, symptoms in arms or hands, dizziness or unsteadiness, stiffness in neck, pre-existing headache, and lower recovery expectations. With a positive score (i.e., ≥0.75 probability), the model can rule in the presence of post-traumatic headache at six months (development: specificity = 99.8 %, 95 % CI 99.5 %-99.9 %; sensitivity = 1.6 %, 95 % CI 1.0 %-2.6 %; positive likelihood ratio (LR+) = 8.0, 95 % CI 2.7-24.1; negative likelihood ratio (LR-) = 1.0, 95 % CI 1.0-1.0; validation: specificity = 95.5 %, 95 % CI 91.1 %-97.8 %; sensitivity = 27.2 %, 95 % CI 20.4 %-35.2 %); LR+ = 6.0, 95 % CI 2.8-13.2; LR- = 0.8, 95 % CI 0.7-0.8).

CONCLUSIONS AND RELEVANCE

Clinicians can collect patient information on the eight predictors of our model to identify patients that will report ongoing post-traumatic headache six months after a traffic collision. Future research should focus on selecting patients at high risk of poor outcomes (using our model) for inclusion in intervention studies, and determining effective interventions for these patients.

Update on NAFLD genetics: From new variants to the clinic

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver diseases in high-income countries and the burden of NAFLD is increasing at an alarming rate. The risk of developing NAFLD and related complications is highly variable among individuals and is determined by environmental and genetic factors. Genome-wide association studies have uncovered robust and reproducible associations between variations in genes such as PNPLA3, TM6SF2, MBOAT7, GCKR, HSD17B13 and the natural history of NAFLD. These findings have provided compelling new insights into the biology of NAFLD and highlighted potentially attractive pharmaceutical targets. More recently the development of polygenic risk scores, which have shown promising results for the clinical risk prediction of other complex traits (such as cardiovascular disease and breast cancer), have provided new impetus for the clinical validation of genetic variants in NAFLD risk stratification. Herein, we review current knowledge on the genetic architecture of NAFLD, including gene-environment interactions, and discuss the implications for disease pathobiology, drug discovery and risk prediction. We particularly focus on the potential clinical translation of recent genetic advances, discussing methodological hurdles that must be overcome before these discoveries can be implemented in everyday practice.

The relations among three popular indices of risks

The relative risk, risk difference, and odds ratio are three major indices of differences in risks of diseases between different groups. Although widely used in research and practice in biomedical and epidemiologic research, misconceptions are not uncommon about their relationships. Many publications offer contradicting advices in how to use them in studies. Some biomedical researchers believe that these indices are related in a monotone fashion, and, thus, changes in one direction in one of the indices can be interpreted as same directional changes in the other two. Misconceptions about these three indices such as the monotone relationship are so prevalent in the biomedical and epidemiologic research that clarifications of such popular beliefs are warranted. In this paper, we take a systematic approach to characterize the relationships among the indices. We develop key results to elucidate the intricate relationships between the indices. Our findings speak to the need for investigators to carefully consider the different indices before using them in their studies, since they are not interchangeable and results based on one index are generally not translatable into any of the others.

Genome-wide association studies and polygenic risk scores for skin cancer: clinically useful yet?

BACKGROUND

Genome-wide association studies (GWAS) have identified thousands of susceptibility variants, although most have been associated with small individual risk estimates that offer little predictive value. However, combining multiple variants into polygenic risk scores (PRS) may be more informative. Multiple studies have developed PRS composed of GWAS-identified variants for cutaneous cancers. This review highlights data from these studies.

OBJECTIVES

To review published GWAS and PRS studies for melanoma, cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC), and discuss their potential clinical utility.

METHODS

We searched PubMed and the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue to identify relevant studies.

RESULTS

Results from 21 GWAS (11 melanoma, 3 cSCC, 7 BCC) and 11 PRS studies are summarized. Six loci in pigmentation genes overlap between these three cancers (ASIP/RALY, IRF4, MC1R, OCA2, SLC45A2 and TYR). Additional loci overlap for cSCC/BCC and BCC/melanoma, but no other loci are shared between cSCC and melanoma. PRS for melanoma show roughly two-to-threefold increases in risk and modest improvements in risk prediction (2-7% increases). PRS are associated with twofold and threefold increases in risk of cSCC and BCC, respectively, with small improvements (2% increase) in predictive ability.

CONCLUSIONS

Existing data indicate that PRS may offer small, but potentially meaningful, improvements to risk prediction. Additional research is needed to clarify the potential utility of PRS in cutaneous carcinomas. Clinical translation will require well-powered validation studies incorporating known risk factors to evaluate PRS as tools for screening. What's already known about this topic? Over 50 susceptibility loci for melanoma, basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) have been identified in genome-wide association studies (GWAS). Polygenic risk scores (PRS) using variants identified from GWAS have also been developed for melanoma, BCC and cSCC, and investigated with respect to clinical risk prediction. What does this study add? This review provides an overview of GWAS findings and the potential clinical utility of PRS for melanoma, BCC and cSCC.

Predictors of Smoking Cessation Among College Students in a Pragmatic Randomized Controlled Trial

An effective strategy to quit smoking should consider demographic aspects, smoking-related characteristics and psychological factors. This study examined potential predictors of smoking cessation in Spanish college students. A total of 255 college student smokers (18-24 years old), recruited to a cessation trial (Spain, 2013-2014), comprised an observational cohort. The main outcome was biochemically verified (urine cotinine) abstinence at the 6-month follow-up. Baseline potential predictors included socio-demographic, smoking-related and psychological variables (Fagerström Test for Nicotine Dependence (FTND), expired monoxide level (CO), intention to quit, previous quit attempts, participation in previous multicomponent programmes and confidence in quitting). Logistic regression models were used to identify potential predictors, the area under the ROC curve (AUC) was used to discriminate the capacity of the predictors and the Hosmer-Lemeshow goodness-of-fit test was used to assess model calibration. After 6 months of follow-up, variables related to high nicotine dependence, FTND and expired CO levels were associated with lower odds of quitting smoking (OR = 0.69 [95% CI 0.54-0.89] and 0.84 [0.77-0.92], respectively). Furthermore, being prepared to change (OR = 3.98 [1.49-10.64], p = 0.006) and being confident to quit (OR = 4.73 [2.12-10.55], p < 0.001) were also potential predictors of smoking cessation. The model that combined all these variables had the best predictive validity (AUC = 0.84 [0.78-0.91], p = 0.693) and showed good predictive capacity (χ² = 10.36, p = 0.241). Findings highlight that, in this population of college student smokers, having a lower level of nicotine dependence, being prepared to quit and having the confidence in the ability to quit were associated with smoking cessation, and these factors had good predictive capacity.

Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk

Genetic factors play an important role in cutaneous squamous cell carcinoma risk. Genome-wide association studies have identified 21 single nucleotide polymorphisms associated with cutaneous squamous cell carcinoma risk. Yet no studies have attempted to quantify the contribution of heritability to cutaneous squamous cell carcinoma risk by calculating the population attributable risk using a combination of all discovered genetic variants. Using an additive multi-locus linear logistic model, we determined the cumulative association of these 21 genetic regions to cutaneous squamous cell carcinoma population attributable risk. We computed a multi-locus population attributable risk of 62%, suggesting that if the effects of all the risk alleles were removed from a population, the cutaneous squamous cell carcinoma risk would drop by 62%. Using stratified analysis, we also examined the impact of sex on polygenic risk score, and found that men have an increased relative risk throughout the spectrum of the polygenic risk score. Quantifying the impact of genetic predisposition on the proportion of cancer cases can guide future research decisions and public health policy planning.

Genetics and epigenetics of NAFLD and NASH: Clinical impact

Non-alcoholic fatty liver disease (NAFLD) is now recognised as the most common liver disease worldwide. It encompasses a broad spectrum of conditions, from simple steatosis, through non-alcoholic steatohepatitis, to fibrosis and ultimately cirrhosis and hepatocellular carcinoma. A hallmark of NAFLD is the substantial inter-patient variation in disease progression. NAFLD is considered a complex disease trait such that interactions between the environment and a susceptible polygenic host background determine disease phenotype and influence progression. Recent years have witnessed multiple genome-wide association and large candidate gene studies, which have enriched our understanding of the genetic basis of NAFLD. Notably, the I148M PNPLA3 variant has been identified as the major common genetic determinant of NAFLD. Variants with moderate effect size in TM6SF2, MBOAT7 and GCKR have also been shown to have a significant contribution. The premise for this review is to discuss the status of research into important genetic and epigenetic modifiers of NAFLD progression. The potential to translate the accumulating wealth of genetic data into the design of novel therapeutics and the clinical implementation of diagnostic/prognostic biomarkers will be explored. Finally, personalised medicine and the opportunities for future research and challenges in the immediate post genetics era will be illustrated and discussed.

Field Synopsis and Re-analysis of Systematic Meta-analyses of Genetic Association Studies in Multiple Sclerosis: a Bayesian Approach

To provide an up-to-date summary of multiple sclerosis-susceptible gene variants and assess the noteworthiness in hopes of finding true associations, we investigated the results of 44 meta-analyses on gene variants and multiple sclerosis published through December 2016. Out of 70 statistically significant genotype associations, roughly a fifth (21%) of the comparisons showed noteworthy false-positive rate probability (FPRP) at a statistical power to detect an OR of 1.5 and at a prior probability of 10^-6 assumed for a random single nucleotide polymorphism. These associations (IRF8/rs17445836, STAT3/rs744166, HLA/rs4959093, HLA/rs2647046, HLA/rs7382297, HLA/rs17421624, HLA/rs2517646, HLA/rs9261491, HLA/rs2857439, HLA/rs16896944, HLA/rs3132671, HLA/rs2857435, HLA/rs9261471, HLA/rs2523393, HLA-DRB1/rs3135388, RGS1/rs2760524, PTGER4/rs9292777) also showed a noteworthy Bayesian false discovery probability (BFDP) and one additional association (CD24 rs8734/rs52812045) was also noteworthy via BFDP computation. Herein, we have identified several noteworthy biomarkers of multiple sclerosis susceptibility. We hope these data are used to study multiple sclerosis genetics and inform future screening programs.

Performance Metrics for Selecting Single Nucleotide Polymorphisms in Late-onset Alzheimer's Disease

Previous genome-wide association studies using P-values to select single nucleotide polymorphisms (SNPs) have suffered from high false-positive and false-negative results. This case-control study recruited 713 late-onset Alzheimer's disease (LOAD) cases and controls aged ≥65 from three teaching hospitals in northern Taiwan from 2007 to 2010. Performance metrics were used to select SNPs in stage 1, which were then genotyped to another dataset (stage 2). Four SNPs (CPXM2 rs2362967, APOC1 rs4420638, ZNF521 rs7230380, and rs12965520) were identified for LOAD by both traditional P-values (without correcting for multiple tests) and performance metrics. After correction for multiple tests, no SNPs were identified by traditional P-values. Simultaneous testing of APOE e4 and APOC1 rs4420638 (the SNP with the best performance in the performance metrics) significantly improved the low sensitivity of APOE e4 from 0.50 to 0.78. A point-based genetic model including these 2 SNPs and important covariates was constructed. Compared with elders with low-risks score (0-6), elders belonging to moderate-risk (score = 7-11) and high-risk (score = 12-18) groups showed a significantly increased risk of LOAD (adjusted odds ratio = 7.80 and 46.93, respectively; Ptrend < 0.0001). Performance metrics allow for identification of markers with moderate effect and are useful for creating genetic tests with clinical and public health implications.

PNPLA3 gene in liver diseases

Genome-wide association studies (GWAS) in the field of liver diseases have revealed previously unknown pathogenic loci and generated new biological hypotheses. In 2008, a GWAS performed in a population-based sample study, where hepatic liver fat content was measured by magnetic spectroscopy, showed a strong association between a variant (rs738409 C>G p.I148M) in the patatin-like phospholipase domain containing 3 (PNPLA3) gene and nonalcoholic fatty liver disease. Further replication studies have shown robust associations between PNPLA3 and steatosis, fibrosis/cirrhosis, and hepatocellular carcinoma on a background of metabolic, alcoholic, and viral insults. The PNPLA3 protein has lipase activity towards triglycerides in hepatocytes and retinyl esters in hepatic stellate cells. The I148M substitution leads to a loss of function promoting triglyceride accumulation in hepatocytes. Although PNPLA3 function has been extensively studied, the molecular mechanisms leading to hepatic fibrosis and carcinogenesis remain unclear. This unsuspected association has highlighted the fact that liver fat metabolism may have a major impact on the pathophysiology of liver diseases. Conversely, alone, this locus may have limited predictive value with regard to liver disease outcomes in clinical practice. Additional studies at the genome-wide level will be required to identify new variants associated with liver damage and cancer to explain a greater proportion of the heritability of these phenotypes. Thus, incorporating PNPLA3 and other genetic variants in combination with clinical data will allow for the development of tailored predictive models. This attractive approach should be evaluated in prospective cohorts.

Relationships among three popular measures of differential risks: relative risk, risk difference, and odds ratio

The relative risk, risk difference, and odds ratio are the three most commonly used measures for comparing the risk of disease between different groups. Although widely popular in biomedical and psychosocial research, the relationship among the three measures has not been clarified in the literature. Many researchers incorrectly assume a monotonic relationship, such that higher (or lower) values in one measure are associated with higher (or lower) values in the other measures. In this paper we discuss three theorems and provide examples demonstrating that this is not the case; there is no logical relationship between any of these measures. Researchers must be very cautious when implying a relationship between the different measures or when combining results of studies that use different measures of risk.

Regulatory polymorphisms modulate the expression of HLA class II molecules and promote autoimmunity

Targeted sequencing of sixteen SLE risk loci among 1349 Caucasian cases and controls produced a comprehensive dataset of the variations causing susceptibility to systemic lupus erythematosus (SLE). Two independent disease association signals in the HLA-D region identified two regulatory regions containing 3562 polymorphisms that modified thirty-seven transcription factor binding sites. These extensive functional variations are a new and potent facet of HLA polymorphism. Variations modifying the consensus binding motifs of IRF4 and CTCF in the XL9 regulatory complex modified the transcription of HLA-DRB1, HLA-DQA1 and HLA-DQB1 in a chromosome-specific manner, resulting in a 2.5-fold increase in the surface expression of HLA-DR and DQ molecules on dendritic cells with SLE risk genotypes, which increases to over 4-fold after stimulation. Similar analyses of fifteen other SLE risk loci identified 1206 functional variants tightly linked with disease-associated SNPs and demonstrated that common disease alleles contain multiple causal variants modulating multiple immune system genes.

Machine learning derived risk prediction of anorexia nervosa

Background

Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role.

Methods

In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children’s Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects.

Results

Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC’s of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values.

Conclusions

To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0165-x) contains supplementary material, which is available to authorized users.

Polygenic Scores in Epidemiology: Risk Prediction, Etiology, and Clinical Utility

Genes account for a significant proportion of the risk for most common diseases. The genome-wide association scan (GWAS) era of genetic epidemiology has generated a massive amount of data, revolutionized our thinking on the genetic architecture of common diseases and positioned the field to realistically consider risk prediction for common polygenic diseases, such as non-familial cancers, and autoimmune, cardiovascular and psychiatric diseases. Polygenic scoring is an approach that shows promise for understanding the polygenic contribution to common human diseases. This is an approach typically relying on genome-wide SNP data, where a set of SNPs identified in a discovery GWAS are used to construct composite polygenic scores. These scores are then used in additional samples for association testing or risk prediction. This review summarizes the extant literature on the use, power, and accuracy of polygenic scores in studies of the etiology of disease and the promise for disease risk prediction.

Development of a genetic marker set to diagnose aspirin-exacerbated respiratory disease in a genome-wide association study

We developed a genetic marker set of single nucleotide polymorphisms (SNPs) by summing risk scores of 14 SNPs showing a significant association with aspirin-exacerbated respiratory disease (AERD) from our previous 660 W genome-wide association data. The summed scores were higher in the AERD than in the aspirin-tolerant asthma (ATA) group (P=8.58 × 10(-37)), and were correlated with the percent decrease in forced expiratory volume in 1 s after aspirin challenge (r(2)=0.150, P=5.84 × 10(-30)). The area under the curve of the scores for AERD in the receiver operating characteristic curve was 0.821. The best cutoff value of the summed risk scores was 1.01328 (P=1.38 × 10(-32)). The sensitivity and specificity of the best scores were 64.7% and 85.0%, respectively, with 42.1% positive and 93.4% negative predictive values. The summed risk score may be used as a genetic marker with good discriminative power for distinguishing AERD from ATA.

Multifactor dimensionality reduction analysis to elucidate the cross-talk between one-carbon and xenobiotic metabolic pathways in multi-disease models

Putatively functional polymorphisms of one-carbon and xenobiotic metabolic pathways influence susceptibility for wide spectrum of diseases. The current study was aimed to explore gene-gene interactions among these two metabolic pathways in four diseases i.e. breast cancer, systemic lupus erythematosus (SLE), coronary artery disease (CAD) and Parkinson's disease (PD). Multifactor dimensionality reduction analysis was carried out on four case-control datasets. Cross-talk was observed between one-carbon and xenobiotic pathways in breast cancer (RFC 80 G>A, COMT H108L and TYMS 5'-UTR 28 bp tandem repeat) and SLE (CYP1A1 m1, MTRR 66 A>G and GSTT1). Gene-gene interactions within one-carbon metabolic pathway were observed in CAD (GCPII 1561 C>T, SHMT 1420 C>T and MTHFR 677 C>T) and PD (cSHMT 1420 C>T, MTRR 66 A>G and RFC1 80 G>A). These interaction models showed good predictability of risk for PD (The area under the receiver operating characteristic curve (C) = 0.83) and SLE (C = 0.73); and moderate predictability of risk for breast cancer (C = 0.64) and CAD (C = 0.63). Cross-talk between one-carbon and xenobiotic pathways was observed in diseases with female preponderance. Gene-gene interactions within one-carbon metabolic pathway were observed in diseases with male preponderance.

Genetics of cerebral small vessel disease

Cerebral small vessel disease (SVD) is an important cause of stroke and cognitive impairment among the elderly and is a more frequent cause of stroke in Asia than in the US or Europe. Although traditional risk factors such as hypertension or diabetes mellitus are important in the development of cerebral SVD, the exact pathogenesis is still uncertain. Both, twin and family history studies suggest heritability of sporadic cerebral SVD, while the candidate gene study and the genome-wide association study (GWAS) are mainly used in genetic research. Robust associations between the candidate genes and occurrence of various features of sporadic cerebral SVD, such as lacunar infarction, intracerebral hemorrhage, or white matter hyperintensities, have not yet been elucidated. GWAS, a relatively new technique, overcomes several shortcomings of previous genetic techniques, enabling the detection of several important genetic loci associated with cerebral SVD. In addition to the more common, sporadic cerebral SVD, several single-gene disorders causing cerebral SVD have been identified. The number of reported cases is increasing as the clinical features become clear and diagnostic examinations are more readily available. These include cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy, COL4A1-related cerebral SVD, autosomal dominant retinal vasculopathy with cerebral leukodystrophy, and Fabry disease. These rare single-gene disorders are expected to play a crucial role in our understanding of cerebral SVD pathogenesis by providing animal models for the identification of cellular, molecular, and biochemical changes underlying cerebral small vessel damage.

Summarizing polygenic risks for complex diseases in a clinical whole-genome report

PURPOSE

Disease-causing mutations and pharmacogenomic variants are of primary interest for clinical whole-genome sequencing. However, estimating genetic liability for common complex diseases using established risk alleles might one day prove clinically useful.

METHODS

We compared polygenic scoring methods using a case-control data set with independently discovered risk alleles in the MedSeq Project. For eight traits of clinical relevance in both the primary-care and cardiomyopathy study cohorts, we estimated multiplicative polygenic risk scores using 161 published risk alleles and then normalized them using the population median estimated from the 1000 Genomes Project.

RESULTS

Our polygenic score approach identified the overrepresentation of independently discovered risk alleles in cases as compared with controls using a large-scale genome-wide association study data set. In addition to normalized multiplicative polygenic risk scores and rank in a population, the disease prevalence and proportion of heritability explained by known common risk variants provide important context in the interpretation of modern multilocus disease risk models.

CONCLUSION

Our approach in the MedSeq Project demonstrates how complex trait risk variants from an individual genome can be summarized and reported for the general clinician and also highlights the need for definitive clinical studies to obtain reference data for such estimates and to establish clinical utility.

Strategies to predict rheumatoid arthritis development in at-risk populations

The development of RA is conceived as a multiple hit process and the more hits that are acquired, the greater the risk of developing clinically apparent RA. Several at-risk phases have been described, including the presence of genetic and environmental factors, RA-related autoantibodies and biomarkers and symptoms. Intervention in these preclinical phases may be more effective compared with intervention in the clinical phase. One prerequisite for preventive strategies is the ability to estimate an individual's risk adequately. This review evaluates the ability to predict the risk of RA in the various preclinical stages. Present data suggest that a combination of genetic and environmental factors is helpful to identify persons at high risk of RA among first-degree relatives. Furthermore, a combination of symptoms, antibody characteristics and environmental factors has been shown to be relevant for risk prediction in seropositive arthralgia patients. Large prospective studies are needed to validate and improve risk prediction in preclinical disease stages.

Coding variants in nephrin (NPHS1) and susceptibility to nephropathy in African Americans

BACKGROUND AND OBJECTIVES

Presumed genetic risk for diabetic and nondiabetic end stage renal disease is strong in African Americans.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Exome sequencing data from African Americans with type 2 diabetic end stage renal disease and nondiabetic, non-nephropathy controls in the T2D-GENES study (Discovery, n=529 patients and n=535 controls) were evaluated, focusing on missense variants in NPHS1. Associated variants were then evaluated in independent type 2 diabetic end stage renal disease (Replication, n=1305 patients and n=760 controls), nondiabetic end stage renal disease (n=1705), and type 2 diabetes-only, non-nephropathy samples (n=503). All participants were recruited from dialysis facilities and internal medicine clinics across the southeastern United States from 1991 to present. Additional NPHS1 missense variants were identified from exome sequencing resources, genotyped, and sequence kernel association testing was then performed.

RESULTS

Initial analysis identified rs35238405 (T233A; minor allele frequency=0.0096) as associated with type 2 diabetic end stage renal disease (adjustment for admixture P=0.042; adjustment for admixture+APOL1 P=0.080; odds ratio, 2.89 and 2.36, respectively); with replication in independent type 2 diabetic end stage renal disease samples (P=0.018; odds ratio, 4.30) and nondiabetic end stage renal disease samples (P=0.016; odds ratio, 4.48). In a combined analysis (all patients with end stage renal disease versus all controls), T233A was associated with all-cause end stage renal disease (P=0.0038; odds ratio, 2.82; n=3270 patients and n=1187 controls). A P-value of <0.001 was obtained after adjustment for admixture and APOL1 in sequence kernel association testing. Two additional variants (H800R and Y1174H) were nominally associated with protection from end stage renal disease (P=0.036; odds ratio, 0.44; P=0.0084; odds ratio, 0.040, respectively) in the locus-wide single-variant association tests.

CONCLUSIONS

Coding variants in NPHS1 are associated with both risk for and protection from common forms of nephropathy in African Americans.

The PXR rs7643645 polymorphism is associated with the risk of higher prostate-specific antigen levels in prostate cancer patients

Levels of enzymes that determine testosterone catabolism such as CYP3A4 have been associated with prostate cancer (PCa) risk. Although some studies have related CYP3A4*1B allele, a gene polymorphism that modifies CYP3A4 expression level, with PCa risk, others have failed, suggesting that additional genetic variants may be involved. Expression of CYP3A4 is largely due to the activation of Pregnane X Receptor (PXR). Particularly, rs2472677 and rs7643645 PXR polymorphisms modify CYP3A4 expression levels. To evaluate whether PXR-HNF3β/T (rs2472677), PXR-HNF4/G (rs7643645), and CYP3A4*1B (rs2740574) polymorphisms are associated with PCa a case control-study was performed. The multiple testing analysis showed that the PXR-HNF4/G polymorphism was associated with higher levels of prostate-specific antigen (PSA) in patients with PCa (OR = 3.99, p = 0.03). This association was stronger in patients diagnosed at the age of 65 years or older (OR = 10.8, p = 0.006). Although the CYP3A4*1B/*1B genotype was overrepresented in PCa patients, no differences were observed in the frequency of this and PXR-HNF3β/T alleles between controls and cases. Moreover, no significant association was found between these polymorphisms and PSA, Gleason grade, or tumor lymph node metastasis.

Integrating genetics and social science: genetic risk scores

The sequencing of the human genome and the advent of low-cost genome-wide assays that generate millions of observations of individual genomes in a matter of hours constitute a disruptive innovation for social science. Many public use social science datasets have or will soon add genome-wide genetic data. With these new data come technical challenges, but also new possibilities. Among these, the lowest-hanging fruit and the most potentially disruptive to existing research programs is the ability to measure previously invisible contours of health and disease risk within populations. In this article, we outline why now is the time for social scientists to bring genetics into their research programs. We discuss how to select genetic variants to study. We explain how the polygenic architecture of complex traits and the low penetrance of individual genetic loci pose challenges to research integrating genetics and social science. We introduce genetic risk scores as a method of addressing these challenges and provide guidance on how genetic risk scores can be constructed. We conclude by outlining research questions that are ripe for social science inquiry.

Biomarker research with prospective study designs for the early detection of cancer

This article describes the principles of marker research with prospective studies along with examples for diagnostic tumor markers. A plethora of biomarkers have been claimed as useful for the early detection of cancer. However, disappointingly few biomarkers were approved for the detection of unrecognized disease, and even approved markers may lack a sound validation phase. Prospective studies aimed at the early detection of cancer are costly and long-lasting and therefore the bottleneck in marker research. They enroll a large number of clinically asymptomatic subjects and follow-up on incident cases. As invasive procedures cannot be applied to collect tissue samples from the target organ, biomarkers can only be determined in easily accessible body fluids. Marker levels increase during cancer development, with samples collected closer to the occurrence of symptoms or a clinical diagnosis being more informative than earlier samples. Only prospective designs allow the serial collection of pre-diagnostic samples. Their storage in a biobank upgrades cohort studies to serve for both, marker discovery and validation. Population-based cohort studies, which may collect a wealth of data, are commonly conducted with just one baseline investigation lacking serial samples. However, they can provide valuable information about factors that influence the marker level. Screening programs can be employed to archive serial samples but require significant efforts to collect samples and auxiliary data for marker research. Randomized controlled trials have the highest level of evidence in assessing a biomarker's benefit against usual care and present the most stringent design for the validation of promising markers as well as for the discovery of new markers. In summary, all kinds of prospective studies can benefit from a biobank as they can serve as a platform for biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Circulating mitochondrial DNA in patients in the ICU as a marker of mortality: derivation and validation

BACKGROUND

Mitochondrial DNA (mtDNA) is a critical activator of inflammation and the innate immune system. However, mtDNA level has not been tested for its role as a biomarker in the intensive care unit (ICU). We hypothesized that circulating cell-free mtDNA levels would be associated with mortality and improve risk prediction in ICU patients.

METHODS AND FINDINGS

Analyses of mtDNA levels were performed on blood samples obtained from two prospective observational cohort studies of ICU patients (the Brigham and Women's Hospital Registry of Critical Illness [BWH RoCI, n = 200] and Molecular Epidemiology of Acute Respiratory Distress Syndrome [ME ARDS, n = 243]). mtDNA levels in plasma were assessed by measuring the copy number of the NADH dehydrogenase 1 gene using quantitative real-time PCR. Medical ICU patients with an elevated mtDNA level (≥3,200 copies/µl plasma) had increased odds of dying within 28 d of ICU admission in both the BWH RoCI (odds ratio [OR] 7.5, 95% CI 3.6-15.8, p = 1×10(-7)) and ME ARDS (OR 8.4, 95% CI 2.9-24.2, p = 9×10(-5)) cohorts, while no evidence for association was noted in non-medical ICU patients. The addition of an elevated mtDNA level improved the net reclassification index (NRI) of 28-d mortality among medical ICU patients when added to clinical models in both the BWH RoCI (NRI 79%, standard error 14%, p<1×10(-4)) and ME ARDS (NRI 55%, standard error 20%, p = 0.007) cohorts. In the BWH RoCI cohort, those with an elevated mtDNA level had an increased risk of death, even in analyses limited to patients with sepsis or acute respiratory distress syndrome. Study limitations include the lack of data elucidating the concise pathological roles of mtDNA in the patients, and the limited numbers of measurements for some of biomarkers.

CONCLUSIONS

Increased mtDNA levels are associated with ICU mortality, and inclusion of mtDNA level improves risk prediction in medical ICU patients. Our data suggest that mtDNA could serve as a viable plasma biomarker in medical ICU patients.

GCPII modulates oxidative stress and prostate cancer susceptibility through changes in methylation of RASSF1, BNIP3, GSTP1 and Ec-SOD

Glutamate carboxypeptidase II (GCPII) haplotypes were found to influence susceptibility to prostate cancer. In the current study, we have elucidated the impact of these haplotypes on the expression of PSMA, BNIP3, Ec-SOD, GSTP1 and RASSF1 genes to understand the epigenetic basis of oxidative stress and prostate cancer risk. Expression analysis was carried out by RT-PCR. Bisulphite treated DNA was subjected to MS-PCR and COBRA for epigenetic studies. Plasma MDA and glutathione levels were measured. In prostate cancer, upregulation of BNIP3 (204.4 ± 23.77 vs. 143.9 ± 16.42 %, p = 0.03); and downregulation of Ec-SOD (105.8 ± 13.69 vs. 176.3 ± 21.1 %, p = 0.027) and RASSF1A (16.67 ± 16.0 vs. 90.8 ± 8.5 %, p = 0.0048) was observed. Hypomethylation of BNIP3 (31.25 ± 16.19 vs. 45.70 ± 2.42 %, p < 0.0001), hypermethylation of Ec-SOD (71.4 ± 6.75 vs. 10.0 ± 3.78 %, p < 0.0001) and RASSF1 (76.25 ± 12.53 vs. 30.0 ± 8.82 %, p = 0.0077) was observed in prostate cancer. The gene expression signature of PSMA, BNIP3, Ec-SOD, GSTP1, clearly demarcated cases and controls (AUC = 0.89 in the ROC curve). D191V variant of GCPII showed positive association with oxidative stress and inverse association with Ec-SOD expression. H475Y variant showed positive association with Ec-SOD expression and inverse association with oxidative stress. R190W variant was found to reduce oxidative stress by increasing glutathione levels. GCPII genetic variants contribute to increased oxidative stress and prostate cancer risk by modulating the CpG island methylation of Ec-SOD.

Bringing genome-wide association findings into clinical use

Genome-wide association studies (GWASs) have been heralded as a major advance in biomedical discovery, having identified ~2,000 robust associations with complex diseases since 2005. Despite this success, they have met considerable scepticism regarding their clinical applicability; this scepticism arises from such aspects as the modest effect sizes of associated variants and their unclear functional consequences. There are, however, promising examples of GWAS findings that will or that may soon be translated into clinical care. These examples include variants identified through GWASs that provide strongly predictive or prognostic information or that have important pharmacological implications; these examples may illustrate promising approaches to wider clinical application.

Genetic variants and their interactions in disease risk prediction - machine learning and network perspectives

A central challenge in systems biology and medical genetics is to understand how interactions among genetic loci contribute to complex phenotypic traits and human diseases. While most studies have so far relied on statistical modeling and association testing procedures, machine learning and predictive modeling approaches are increasingly being applied to mining genotype-phenotype relationships, also among those associations that do not necessarily meet statistical significance at the level of individual variants, yet still contributing to the combined predictive power at the level of variant panels. Network-based analysis of genetic variants and their interaction partners is another emerging trend by which to explore how sub-network level features contribute to complex disease processes and related phenotypes. In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how physical or genetic interaction networks could be used as a priori information for providing improved predictive power and mechanistic insights into the disease networks. These developments are geared toward explaining a part of the missing heritability, and when combined with individual genomic profiling, such systems medicine approaches may also provide a principled means for tailoring personalized treatment strategies in the future.

Defining genes using "blueprint" versus "instruction" metaphors: effects for genetic determinism, response efficacy, and perceived control

Evidence supports mixed attributions aligned with personal and/or clinical control and gene expression for health in this era of genomic science and health care. We consider variance in these attributions and possible relationships to individual mind sets associated with essentialist beliefs that genes determine health versus threat beliefs that genes increase susceptibility for disease and severity linked to gene-environment interactions. Further, we contribute to theory and empirical research to evaluate the use of metaphors to define genes. Participants (N = 324) read a message that varied the introduction by providing a definition of genes that used either an "instruction" metaphor or a "blueprint" metaphor. The "instruction" metaphor compared to the "blueprint" metaphor promoted stronger threat perceptions, which aligned with both belief in the response efficacy of genetic research for health and perceived behavioral control linked to genes and health. The "blueprint" metaphor compared to the "instruction" metaphor promoted stronger essentialist beliefs, which aligned with more intense positive regard for the efficacy of genetic research and human health. Implications for health communicators include societal effects aligned with stigma and discrimination that such findings portend.

Genome-wide association studies in preterm birth: implications for the practicing obstetrician-gynaecologist

Preterm birth has the highest mortality and morbidity of all pregnancy complications. The burden of preterm birth on public health worldwide is enormous, yet there are few effective means to prevent a preterm delivery. To date, much of its etiology is unexplained, but genetic predisposition is thought to play a major role. In the upcoming year, the international Preterm Birth Genome Project (PGP) consortium plans to publish a large genome wide association study in early preterm birth. Genome-wide association studies (GWAS) are designed to identify common genetic variants that influence health and disease. Despite the many challenges that are involved, GWAS can be an important discovery tool, revealing genetic variations that are associated with preterm birth. It is highly unlikely that findings of a GWAS can be directly translated into clinical practice in the short run. Nonetheless, it will help us to better understand the etiology of preterm birth and the GWAS results will generate new hypotheses for further research, thus enhancing our understanding of preterm birth and informing prevention efforts in the long run.

A highly sensitive and specific genetic marker to diagnose aspirin-exacerbated respiratory disease using a genome-wide association study

The aim of the present study was to develop a diagnostic set of single-nucleotide polymorphisms (SNPs) for discriminating aspirin-exacerbated respiratory disease (AERD) from aspirin-tolerant asthma (ATA) using the genome-wide association study (GWAS) data; the GWAS data were filtered according to p-values and odds ratios (ORs) using PLINK software, and the 10 candidate SNPs most closely associated with AERD were selected, based on 100 AERD and 100 ATA subjects. Using multiple logistic regression and receiver-operating characteristic (ROC) curve analysis, eight SNPs were chosen as the best model for distinguishing between AERD and ATA. The relative risk for AERD in each subject was calculated based on the relative risk of each of the eight SNPs. Ten of the original 109,365 SNPs highly associated (filtered with p<0.001 and ORs) with the risk for AERD were selected. A combination model of the eight SNPs among the 10 SNPs showed the highest area under the ROC curve of 0.9. The overall relative risk for AERD based on the eight SNPs was significantly different between the AERD and ATA groups (p=2.802E-21), and the sensitivity and specificity were 78% and 88%, respectively. The candidate set of eight SNPs may be useful in predicting the risk for AERD.

"Is it really worth it to get tested?": primary care patients' impressions of predictive SNP testing for colon cancer

Despite significant progress in genomics research over the past decade, we remain years away from the integration of genomics into routine clinical care. As an initial step toward the implementation of genomic-based medicine, we explored primary care patients' ideas about genomic testing for common complex diseases to help develop future patient education materials and interventions to communicate genomic risk information. We conducted a mixed-methods study with participants from a large primary care clinic. Within four focus groups, we used a semi-structured discussion guide and administered brief pre- and post- discussion quantitative surveys to assess participants' interest, attitudes, and preferences related to testing and receipt of test results. Prior to the discussion, moderators presented a plain-language explanation of DNA and genetics, defined "SNP", and highlighted what is known and unknown about the risks associated with testing for SNPs related to colorectal cancer risk. We used the NVIVO 8 software package to analyze the transcripts from the focus group discussions. The majority of participants (75 %) were "very" or "somewhat interested" in receiving information from a colon cancer SNP test, even after learning about and discussing the small and still clinically uncertain change in risk conferred by SNPs. Reported interest in testing was related to degree of risk conferred, personal risk factors, family history, possible implications for managing health /disease prevention and curiosity about genetic results. Most people (85 %) preferred that genetic information be delivered in person by a healthcare or genetics professional rather than through print materials or a computer. These findings suggest that patients may look to genetic counselors, physicians or other healthcare professionals as gatekeepers of predictive genomic risk information.

The genetics of cancer risk

One hundred years ago, decades before the discovery of the structure of DNA, debate raged regarding how human traits were passed from one generation to the next. Phenotypes, including risk of disease, had long been recognized as having a familial component. Yet it was difficult to reconcile genetic segregation as described by Mendel with observations exhaustively documented by Karl Pearson and others regarding the normal distribution of human characteristics. In 1918, R. A. Fisher published his landmark article, "The Correlation Between Relatives on the Supposition of Mendelian Inheritance," bridging this divide and demonstrating that multiple alleles, all individually obeying Mendel's laws, account for the phenotypic variation observed in nature.Since that time, geneticists have sought to identify the link between genotype and phenotype. Trait-associated alleles vary in their frequency and degree of penetrance. Some minor alleles may approach a frequency of 50% in the human population, whereas others are present within only a few individuals. The spectrum for penetrance is similarly wide. These characteristics jointly determine the segregation pattern of a given trait, which, in turn, determine the method used to map the trait. Until recently, identification of rare, highly penetrant alleles was most practical. Revolutionary studies in genomics reported over the past decade have made interrogation of most of the spectrum of genetic variation feasible.The following article reviews recent discoveries in the genetic basis of inherited cancer risk and how these discoveries inform cancer biology and patient management. Although this article focuses on prostate cancer, the principles are generic for any cancer and, indeed, for any trait.