On the use of population-based registries in the clinical validation of genetic tests for disease susceptibility

Genetic risk estimation in the Coriell Personalized Medicine Collaborative

PURPOSE

Recent genome wide-association studies have identified hundreds of single nucleotide polymorphisms associated with common complex diseases. With the momentum of these discoveries comes a need to communicate this information to individuals.

METHODS

The Coriell Personalized Medicine Collaborative is an observational research study designed to evaluate the utility of personalized genomic information in health care. Participants provide saliva samples for genotyping and complete extensive on-line medical history, family history, and lifestyle questionnaires. Only results for diseases deemed potentially actionable by an independent advisory board are reported.

RESULTS

We present our methodology for developing personalized reports containing risks for both genetic and nongenetic factors. Risk estimates are given as relative risk, derived or reported from representative peer-reviewed publications. Estimates of disease prevalence are also provided. Presenting risk as relative risk allows for consistent reporting across multiple diseases and across genetic and nongenetic factors. Using this approach eliminates the need for assumptions regarding population lifetime risk estimates. Publications used for risk reporting are selected based on the strength of the design and study quality.

CONCLUSION

Coriell Personalized Medicine Collaborative risk reports demonstrate an approach to communicating risk of complex disease via the web that encompasses risks due to genetic variants along with risks caused by family history and lifestyle factors.

Association of A31P and A74T polymorphisms in the myosin binding protein C3 gene and hypertrophic cardiomyopathy in Maine Coon and other breed cats

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is an inherited autosomal dominant trait in cats. The A31P single nucleotide polymorphism (SNP) in the myosin binding protein C 3 gene is thought to be the causative mutation in Maine Coon cats. Additionally, the A74T SNP is offered as a genetic test for HCM.

OBJECTIVES

To evaluate the genetic association between the above-mentioned SNPs and phenotypes.

ANIMALS

Eighty-three Maine Coon cats and 68 cats of other breeds.

METHODS

The study was performed prospectively. Cats were phenotyped as healthy or HCM with echocardiography. Taqman genotyping assays were used for genotyping; results were confirmed by sequencing analysis.

RESULTS

A31P was found in 18/83 (22%) Maine Coon cats. Fifteen of 18 Maine Coons (83%) with the A31P mutation were healthy on echocardiographic examination (mean age 65 months). A74T was present in 28/79 (35%) of Maine Coons and in 42/68 (62%) of other cat breeds. Twenty-two of 28 (79%) of Maine Coons and 21/42 (62%) of other breed cats with the A74T mutation were healthy at a mean age of 72 months and 91 months, respectively. Of 12 Maine Coons with HCM, 9 (75%) were genotype-negative for A31P and 6 (50%) for A74T. Allele frequencies did not differ significantly (P= .47) between phenotype groups. None of the evaluated genetic tests was able to provide useful predictive information of disease outcome.

CONCLUSIONS AND CLINICAL IMPORTANCE

The value of currently available genetic tests is low in the cats of this study. The mutations analyzed appear to have a low penetrance, and even homozygote cats can remain healthy.

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.

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

The brisk discovery of novel inherited disease markers by genome-wide association (GWA) studies has raised expectations for predicting disease risk by analysing multiple common alleles. However, the statistics used during the discovery phase of research (such as odds ratios or p values for association) are not the most appropriate measures for evaluating the predictive value of genetic profiles. We argue that other measures--such as sensitivity, specificity, and positive and negative predictive values--are more useful when proposing a genetic profile for risk prediction.

The impact of diagnostic error on testing genetic association in case-control studies

In case-control studies, subjects in the case group may be recruited from suspected patients who are diagnosed positively with disease. While many statistical methods have been developed for measurement error or misclassification of exposure variables in epidemiological studies, no studies have been reported on the effect of errors in diagnosing disease on testing genetic association in case-control studies. We study the impact of using the original Cochran-Armitage trend test assuming no diagnostic error when, in fact, cases and controls may be clinically diagnosed by an imperfect gold standard or a reference test. The type I error, sample size and asymptotic power of trend tests are examined under a family of genetic models in the presence of diagnostic error. The empirical powers of the trend tests are also compared by simulation studies under various genetic models.

Future of genetics of mood disorders research

This report summarizes the deliberations of a panel with representation from diverse disciplines of relevance to the genetics of mood disorders. The major charge to the panel was to develop a strategic plan to employ the tools of genetics to advance the understanding, treatment, and outcomes for mood disorders. A comprehensive review of the evidence for the role of genetic factors in the etiology of mood disorders was conducted, and the chief impediments for progress in gene identification were identified. The National Institute of Mental Health (NIMH) portfolios in the Genetics Research Branch and the Division of Mental Disorders, Behavioral Sciences, AIDS, and all genetics training activities were reviewed. Despite some promising leads, there are still no confirmed linkage findings for mood disorders. Impediments to gene finding include the lack of phenotypic validity, variation in ascertainment sources and methodology across studies, and genetic complexity. With respect to linkage, the committee recommended that a large-scale, integrated effort be undertaken to examine existing data from linkage and association studies of bipolar disorders using identical phenotypes and statistical methods across studies to determine whether the suggestive linkage findings at some loci can be confirmed. Confirmation would justify more intensive approaches to gene finding. The committee recommended that the NIMH support continued efforts to identify the most heritable subtypes and endophenotypes of major depression using the tools of genetic epidemiology, neuroscience, and behavioral science. The field of genetic epidemiology was identified as an important future direction because population-based, epidemiologic studies of families and unrelated affected individuals assume increasing importance for common chronic diseases. To prepare for shifts to more complex genetic models, the committee recommended that the NIMH develop new interdisciplinary training strategies to prepare for the next generation of genetics research.

Comorbidity of depression with other medical diseases in the elderly

A major factor in the context of evaluating depression in the elderly is the role of medical problems. With aging there is a rapid increase in the prevalence of a number of medical disorders, including cancer, heart disease, Parkinson's disease, Alzheimer's disease, stroke, and arthritis. In this article, we hope to bring clarity to the definition of comorbidity and then discuss a number of medical disorders as they relate to depression. We evaluate medical comorbidity as a risk factor for depression as well as the converse, that is, depression as a risk factor for medical illness. Most of the disorders that we focus on occur in the elderly, with the exception of HIV infection. This review focuses exclusively on unipolar disorder. The review summarizes the current state of the art and also makes recommendations for future directions.

Reporting BRCA test results to primary care physicians

PURPOSE

The main purpose of this study was to determine if comprehension of the cancer risk information presented in a hypothetical report for BRCA1/2 gene analyses was influenced by the format in which the information was presented. A secondary objective was to determine physician characteristics that might influence comprehension of the report.

METHODS

A survey was conducted in which a case vignette describing a young woman at high risk for carrying a BRCA mutation was presented. Survey participants, all primary care practitioners, were asked to interpret a laboratory report that provided the patient's BRCA1/2 test result and accompanying data about the cumulative risk and incidence rates of breast cancer for BRCA1/2 mutation carriers and the general population. These data were presented in the report in either a tabular or a graphic format. The main outcome was measured by the responses to four questions that addressed the probabilistic cancer risk information. Physician predictor variables included medical specialty, practice setting, years in practice, continuing medical education in genetics, and knowledge of cumulative risk.

RESULTS

Knowledge of cumulative risk was the only physician variable that influenced comprehension of the cancer risk information (OR = 31.9; P < 0.001). After adjusting for this variable, the graphic format tended to perform better than the tabular format in conveying breast cancer risk information (OR = 3.1; P = 0.102).

CONCLUSIONS

Many physicians may be unprepared to interpret genetic risk information, due to lack of understanding of basic epidemiologic terms used to express the risk of disease.