As APOE locus variants contribute to both risk of late-onset Alzheimer disease and differences in age-at-onset, it is important to know if other established late-onset Alzheimer disease risk loci also affect age-at-onset in cases.
To investigate the effects of known Alzheimer disease risk loci in modifying age-at-onset, and to estimate their cumulative effect on age-at-onset variation, using data from genome-wide association studies in the Alzheimer’s Disease Genetics Consortium (ADGC).
Design, Setting and Participants
The ADGC comprises 14 case-control, prospective, and family-based datasets with data on 9,162 Caucasian participants with Alzheimer’s occurring after age 60 who also had complete age-at-onset information, gathered between 1989 and 2011 at multiple sites by participating studies. Data on genotyped or imputed single nucleotide polymorphisms (SNPs) most significantly associated with risk at ten confirmed LOAD loci were examined in linear modeling of AAO, and individual dataset results were combined using a random effects, inverse variance-weighted meta-analysis approach to determine if they contribute to variation in age-at-onset. Aggregate effects of all risk loci on AAO were examined in a burden analysis using genotype scores weighted by risk effect sizes.
Main Outcomes and Measures
Age at disease onset abstracted from medical records among participants with late-onset Alzheimer disease diagnosed per standard criteria.
Analysis confirmed association of APOE with age-at-onset (rs6857, P=3.30×10−96), with associations in CR1 (rs6701713, P=7.17×10−4), BIN1 (rs7561528, P=4.78×10−4), and PICALM (rs561655, P=2.23×10−3) reaching statistical significance (P<0.005). Risk alleles individually reduced age-at-onset by 3-6 months. Burden analyses demonstrated that APOE contributes to 3.9% of variation in age-at-onset (R2=0.220) over baseline (R2=0.189) whereas the other nine loci together contribute to 1.1% of variation (R2=0.198).
Conclusions and Relevance
We confirmed association of APOE variants with age-at-onset among late-onset Alzheimer disease cases and observed novel associations with age-at-onset in CR1, BIN1, and PICALM. In contrast to earlier hypothetical modeling, we show that the combined effects of Alzheimer disease risk variants on age-at-onset are on the scale of, but do not exceed, the APOE effect. While the aggregate effects of risk loci on age-at-onset may be significant, additional genetic contributions to age-at-onset are individually likely to be small.
Alzheimer Disease; Alzheimer Disease Genetics; Alzheimer’s Disease - Pathophysiology; Genetics of Alzheimer Disease; Aging
Studies examining whether genetic risk information about common, complex diseases can motivate individuals to improve health behaviors and advance planning have shown mixed results. Examining the influence of different study recruitment strategies may help reconcile inconsistencies.
Secondary analyses were conducted on data from the REVEAL study, a series of randomized clinical trials examining the impact of genetic susceptibility testing for Alzheimer’s disease (AD). We tested whether self-referred participants (SRPs) were more likely than actively recruited participants (ARPs) to report health behavior and advance planning changes after AD risk and APOE genotype disclosure.
Of 795 participants with known recruitment status, 546 (69%) were self-referred and 249 (31%) had been actively recruited. SRPs were younger, less likely to identify as African American, had higher household incomes, and were more attentive to AD than ARPs (all P < 0.01). They also dropped out of the study before genetic risk disclosure less frequently (26% versus 41%, P < 0.001). Cohorts did not differ in their likelihood of reporting a change to at least one health behavior 6 weeks and 12 months after genetic risk disclosure, nor in intentions to change at least one behavior in the future. However, interaction effects were observed where ε4-positive SRPs were more likely than ε4-negative SRPs to report changes specifically to mental activities (38% vs 19%, p < 0.001) and diets (21% vs 12%, p = 0.016) six weeks post-disclosure, whereas differences between ε4-positive and ε4-negative ARPs were not evident for mental activities (15% vs 21%, p = 0.413) or diets (8% versus 16%, P = 0.190). Similarly, ε4-positive participants were more likely than ε4-negative participants to report intentions to change long-term care insurance among SRPs (20% vs 5%, p < 0.001), but not ARPs (5% versus 9%, P = 0.365).
Individuals who proactively seek AD genetic risk assessment are more likely to undergo testing and use results to inform behavior changes than those who respond to genetic testing offers. These results demonstrate how the behavioral impact of genetic risk information may vary according to the models by which services are provided, and suggest that how participants are recruited into translational genomics research can influence findings.
ClinicalTrials.gov NCT00089882 and NCT00462917
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0124-0) contains supplementary material, which is available to authorized users.
Failure to consider lessons from behavioral economics in the case of whole genome sequencing may cause us to run into the ‘last mile problem’ - the failure to integrate newly developed technology, on which billions of dollars have been invested, into society in a way that improves human behavior and decision-making.
Substantial inter-individual variability exists in the disease trajectories of Alzheimer’s disease (AD) patients. Some decline rapidly while others decline slowly and there are no known explanations for this variability. We describe the first genome wide association study to examine rate of cognitive decline in a sample of AD patients with longitudinal measures of cognition.
The discovery sample was 303 AD cases recruited in the AD Neuroimaging Initiative and the replication sample was 323 AD cases from the Religious Orders Study and Rush Memory and Aging Project. In the discovery sample, Alzheimer’s Disease Assessment Scale-cognitive subscale responses were tested for association with genome-wide SNP data using linear regression. We tested the 65 most significant SNPs from the discovery sample for association in the replication sample.
We identified SNPs in the gene SPON1 whose minor alleles were significantly associated with a more rapid rate of decline (rs11023139, P = 7.0 × 10−11) in the discovery sample. A SPON1 SNP 5.5 KB upstream was associated with decline in the replication sample (rs11606345, P=0.002).
SPON1 has not been previously associated with AD risk, but it is plausibly related since the gene product binds to the amyloid precursor protein and inhibits its cleavage by β-secretase. These data suggest that SPON1 may be associated with the differential rate of cognitive decline in AD.
Alzheimer’s disease; GWAS; cognitive decline
Designed in collaboration with 23andMe and Pathway Genomics, the Impact of Personal Genomics (PGen) Study serves as a model for academic-industry partnership and provides a longitudinal dataset for studying psychosocial, behavioral, and health outcomes related to direct-to-consumer personal genomic testing (PGT). Web-based surveys administered at three time points, and linked to individual-level PGT results, provide data on 1,464 PGT customers, of which 71% completed each follow-up survey and 64% completed all three surveys. The cohort includes 15.7% individuals of non-white ethnicity, and encompasses a range of income, education, and health levels. Over 90% of participants agreed to re-contact for future research.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0096-0) contains supplementary material, which is available to authorized users.
Environmental influences on the rate of Alzheimer’s disease (AD) progression have received little attention. Our objective was to test hypotheses concerning associations between caregiver personality traits and the rate of AD progression.
Care receivers (CR) were 161 persons with AD from a population-based dementia progression study; 55 of their caregivers were spouses and 106 were adult children. Cognitive status of the CR was measured with the Mini-Mental State Examination every six months, over an average of 5.6 (range: 1–14) years. Linear mixed models tested rate of cognitive decline as a function of caregiver personality traits from the NEO Five-Factor Inventory.
Significantly faster cognitive decline was observed with higher caregiver Neuroticism overall; however, in stratified models, effects were significant for adult child but not spouse caregivers. Neuroticism facets of depression, anxiety, and vulnerability to stress were significantly associated with faster decline. Higher caregiver Extraversion was associated with slower decline in the CR when caregivers were adult children but not spouses.
For adult child caregivers, caregiver personality traits are associated with rate of cognitive decline in CRs with AD regardless of co-residency. Results suggest that dementia caregiver interventions promoting positive care management strategies and ways to react to caregiving challenges may eventually become an important complement to pharmacologic and other approaches aimed at slower rate of decline in dementia.
caregiver; personality; dementia; cognitive; decline
To inform whether the Alzheimer's Disease Neuroimaging Initiative (ADNI) should change its policy of not returning research results to ADNI participants, we surveyed investigators and research staff about disclosing ADNI biomarker information to research participants, with particular emphasis on amyloid imaging results.
In April 2012, just before Food and Drug Administration approval of the amyloid-binding radiotracer, florbetapir, all ADNI investigators and personnel were recruited to complete an anonymous online survey that contained fixed choice and free-text questions.
Although ADNI participants often requested amyloid imaging results (the proportions of investigators who reported requests from more than half of their participants with normal cognition or mild cognitive impairment were 20% and 22%, respectively), across all diagnostic groups, the majority of ADNI investigators (approximately 90%) did not return amyloid imaging results to ADNI participants. However, the majority of investigators reported that, if the Food and Drug Administration approved florbetapir, they would support the return of amyloid imaging results to participants with mild cognitive impairment and normal cognition, but they emphasized the need for guidance on how to provide these results to participants and for research to assess the value of returning results as well as how returning results will affect study validity and participant well-being.
A majority of ADNI investigators support returning amyloid imaging results to ADNI participants. The findings that they want guidance on how to do this and research on the impact of disclosure suggest how to develop and monitor a disclosure process.
Genome-wide association studies, DNA sequencing studies, and other genomic studies are finding an increasing number of genetic variants associated with clinical phenotypes that may be useful in developing diagnostic, preventive, and treatment strategies for individual patients. However, few common variants have been integrated into routine clinical practice. The reasons for this are several, but two of the most significant are limited evidence about the clinical implications of the variants and a lack of a comprehensive knowledge base that captures genetic variants, their phenotypic associations, and other pertinent phenotypic information that is openly accessible to clinical groups attempting to interpret sequencing data. As the field of medicine begins to incorporate genome-scale analysis into clinical care, approaches need to be developed for collecting and characterizing data on the clinical implications of variants, developing consensus on their actionability, and making this information available for clinical use. The National Human Genome Research Institute (NHGRI) and the Wellcome Trust thus convened a workshop to consider the processes and resources needed to: 1) identify clinically valid genetic variants; 2) decide whether they are actionable and what the action should be; and 3) provide this information for clinical use. This commentary outlines the key discussion points and recommendations from the workshop.
genomic medicine; clinical actionability; database; electronic health records (EHR); pharmacogenomics; DNA sequencing
Deposition of amyloid-β (Aβ) in the cerebral cortex is thought to be a pivotal event in Alzheimer’s disease (AD) pathogenesis with a significant genetic contribution. Molecular imaging can provide an early noninvasive phenotype but small samples have prohibited genome-wide association studies (GWAS) of cortical Aβ load until now. We employed florbetapir (18F) positron emission tomography (PET) imaging to assess brain Aβ levels in vivo for 555 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). More than six million common genetic variants were tested for association to quantitative global cortical Aβ load controlling for age, gender, and diagnosis. Independent genome-wide significant associations were identified on chromosome 19 within APOE (rs429358, p = 5.5 × 10−14) and on chromosome 3 upstream of BCHE (rs509208, p = 2.7 × 10−8) in a region previously associated with serum butyrylcholinesterase activity. Together, these loci explained 15% of the variance in cortical Aβ levels in this sample (APOE 10.7%, BCHE 4.3%). Suggestive associations were identified within ITGA6, near EFNA5, EDIL3, ITGA1, PIK3R1, NFIB, and ARID1B, and between NUAK1 and C12orf75. These results confirm the association of APOE with Aβ deposition and represent the largest known effect of BCHE on an AD-related phenotype. Butyrylcholinesterase has been found in senile plaques and this new association of genetic variation at the BCHE locus with Aβ burden in humans may have implications for potential disease-modifying effects of butyrylcholinesterase-modulating agents in the AD spectrum.
Alzheimer’s disease (AD); amyloid; apolipoprotein E (APOE); butyrylcholinesterase (BCHE); florbetapir (AV-45); genome-wide association study (GWAS)
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.
Alzheimer's disease; Mild cognitive impairment; Amyloid; Tau; Biomarker
Communicating genetic risk information in ways that maximize understanding and promote health is increasingly important given the rapidly expanding availability and capabilities of genomic technologies. A well-developed literature on risk communication in general provides guidance for best practices, including presentation of information in multiple formats, attention to framing effects, use of graphics, sensitivity to the way numbers are presented, parsimony of information, attentiveness to emotions, and interactivity as part of the communication process. Challenges to communicating genetic risk information include deciding how best to tailor it, streamlining the process, deciding what information to disclose, accepting that communications may have limited influence, and understanding the impact of context. Meeting these challenges has great potential for empowering individuals to adopt healthier lifestyles and improve public health, but will require multidisciplinary approaches and collaboration.
risk communication; risk assessment; personalized medicine; genome-wide association; whole-genome sequencing
In clinical exome and genome sequencing, there is potential for the recognition and reporting of incidental or secondary findings unrelated to the indication for ordering the sequencing but of medical value for patient care. The American College of Medical Genetics and Genomics (ACMG) recently published a policy statement on clinical sequencing, which emphasized the importance of disclosing the possibility of such results in pretest patient discussions, clinical testing, and reporting of results. The ACMG appointed a Working Group on Incidental Findings in Clinical Exome and Genome Sequencing to make recommendations about responsible management of incidental findings when patients undergo exome or genome sequencing. This Working Group conducted a year-long consensus process, including review by outside experts, and produced recommendations that have been approved by the ACMG Board. Specific and detailed recommendations, and the background and rationale for these recommendations, are described herein. We recommend that laboratories performing clinical sequencing seek and report mutations of the specified classes or types in the genes listed here. This evaluation and reporting should be performed for all clinical germline (constitutional) exome and genome sequencing, including the ‘normal’ of tumor-normal subtractive analyses in all subjects, irrespective of age, but excluding fetal samples. We recognize that there are insufficient data on clinical utility to fully support these recommendations and we encourage the creation of an ongoing process for updating these recommendations at least annually as further data are collected.
secondary findings; incidental findings; genome; genomic medicine; personalized medicine; whole-exome; whole-genome; sequencing
The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g. APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g. FRMD6) that were later replicated on different data sets. Several other genes (e.g. APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.
Alzheimer’s disease; genetic association study; quantitative traits; neuroimaging; biomarker; cognition
In light of the meeting of the US Food and Drug Administration (FDA) in March 2011 to discuss the regulation of clinical direct-to-consumer (DTC) genetic tests, we have invited five experts to consider the best means of overseeing the ordering and interpretation of these tests. Should these tests be regulated? If so, who, if anyone, should communicate results to consumers?
Introduction: The first regulatory rulings by the U.S. Food and Drug Administration on direct-to-consumer (DTC) genetic testing services are expected soon. As the process of regulating these and other genetic tests moves ahead, it is important to understand the preferences of DTC genetic testing customers about the regulation of these products. Methods: An online survey of customers of three DTC genetic testing companies was conducted 2–8 months after they had received their results. Participants were asked about the importance of regulating the companies selling DTC genetic tests. Results: Most of the 1,046 respondents responded that it would be important to have a nongovernmental (84%) or governmental agency (73%) monitor DTC companies' claims to ensure the consistency with scientific evidence. However, 66% also felt that it was important that DTC tests be available without governmental oversight. Nearly, all customers favored a policy to ensure that insurers and law enforcement officials could not access their information. Discussion: Although many DTC customers want access to genetic testing services without restrictions imposed by the government regulation, most also favor an organization operating alongside DTC companies that will ensure that the claims made by the companies are consistent with sound scientific evidence. This seeming contradiction may indicate that DTC customers want to ensure that they have unfettered access to high-quality information. Additionally, policies to help ensure privacy of data would be welcomed by customers, despite relatively high confidence in the companies.
In this article, we argue that disclosure of incidental findings from whole-genome sequencing has the potential to motivate individuals to change health behaviors through psychological mechanisms that differ from typical risk assessment interventions. Their ability to do so, however, is likely to be highly contingent upon the nature of the incidental findings and how they are disclosed, the context of the disclosure and the characteristics of the patient. Moreover, clinicians need to be aware that behavioral responses may occur in unanticipated ways. This article argues for commentators and policy makers to take a cautious but optimistic perspective while empirical evidence is collected through ongoing research involving whole-genome sequencing and the disclosure of incidental information.
contextual factor; health behavior; incidental finding; whole-genome sequencing
Whole genome sequencing (WGS) is already being used in certain clinical and research settings, but its impact on patient well-being, health-care utilization, and clinical decision-making remains largely unstudied. It is also unknown how best to communicate sequencing results to physicians and patients to improve health. We describe the design of the MedSeq Project: the first randomized trials of WGS in clinical care.
This pair of randomized controlled trials compares WGS to standard of care in two clinical contexts: (a) disease-specific genomic medicine in a cardiomyopathy clinic and (b) general genomic medicine in primary care. We are recruiting 8 to 12 cardiologists, 8 to 12 primary care physicians, and approximately 200 of their patients. Patient participants in both the cardiology and primary care trials are randomly assigned to receive a family history assessment with or without WGS. Our laboratory delivers a genome report to physician participants that balances the needs to enhance understandability of genomic information and to convey its complexity. We provide an educational curriculum for physician participants and offer them a hotline to genetics professionals for guidance in interpreting and managing their patients’ genome reports. Using varied data sources, including surveys, semi-structured interviews, and review of clinical data, we measure the attitudes, behaviors and outcomes of physician and patient participants at multiple time points before and after the disclosure of these results.
The impact of emerging sequencing technologies on patient care is unclear. We have designed a process of interpreting WGS results and delivering them to physicians in a way that anticipates how we envision genomic medicine will evolve in the near future. That is, our WGS report provides clinically relevant information while communicating the complexity and uncertainty of WGS results to physicians and, through physicians, to their patients. This project will not only illuminate the impact of integrating genomic medicine into the clinical care of patients but also inform the design of future studies.
Whole genome sequencing; Genome report; Genomic medicine; Translational genomics; Primary care; Cardiomyopathy genetics
individualized medicine; genomics; primary health care
The goal of the study was to identify and characterize latent profiles (clusters) of cognitive functioning in centenarians and the psychometric properties of cognitive measures within them.
Data were collected from cross-sectional, population-based sample of 244 centenarians (aged 98-108, 15.8% men, 20.5% African-American, 38.0% community-dwelling) from 44 counties in Northern Georgia participating in the Georgia Centenarian Study (2001-2009). Measures included the Mini-Mental State Examination (MMSE), Severe Impairment Battery (SIB), Wechsler Adult Intelligence Scale-III, Similarities sub-test (WAIS), Finger Tapping, Behavioral Dyscontrol Scale (BDS), Controlled Oral Word Association Test (COWAT), and Fuld Object Memory Evaluation (FOME). The Global Deterioration Rating Scale (GDRS) was used to independently evaluate criterion-related validity for distinguishing cognitively normal and impaired groups. Relevant covariates included directly assessed functional status for basic and instrumental activities of daily living (DAFS), race, gender, educational attainment, Geriatric Depression Scale Short Form (GDS), and vision and hearing problems.
Results suggest two distinct classes of cognitive performance in this centenarian sample. Approximately one-third of the centenarians show a pattern of markedly lower cognitive performance on most measures. Group membership is independently well-predicted (AUC=.83) by GDRS scores (sensitivity 67.7%, specificity 82.4%). Membership in the lower cognitive performance group was more likely for individuals who were older, African Americans, had more depressive symptoms, lower plasma folate, carriers of the APOE ε4 allele, facility residents, and individuals who died in the two years following interview.
In a population expected to have high prevalence of dementia, latent subtypes can be distinguished via factor mixture analysis that provide normative values for cognitive functioning. The present study allows estimates for normative cognitive performance in this age group.
As genomic and exomic testing expands in both the research and clinical arenas, determining whether, how, and which incidental findings to return to the ordering clinician and patient becomes increasingly important. Although opinion is varied on what should be returned to consenting patients or research participants, most experts agree that return of medically actionable results should be considered. There is insufficient evidence to fully inform evidence-based clinical practice guidelines regarding return of results from genome-scale sequencing, and thus generation of such evidence is imperative, given the rapidity with which genome-scale diagnostic tests are being incorporated into clinical care. We present an overview of the approaches to incidental findings by members of the Clinical Sequencing Exploratory Research network, funded by the National Human Genome Research Institute, to generate discussion of these approaches by the clinical genomics community. We also report specific lists of “medically actionable” genes that have been generated by a subset of investigators in order to explore what types of findings have been included or excluded in various contexts. A discussion of the general principles regarding reporting of novel variants, challenging cases (genes for which consensus was difficult to achieve across Clinical Sequencing Exploratory Research network sites), solicitation of preferences from participants regarding return of incidental findings, and the timing and context of return of incidental findings are provided.
actionability; actionable genes; clinical sequencing; genomic medicine; incidental findings
The promise of personalized genomics for common complex diseases depends, in part, on the ability to predict genetic risks on the basis of single nucleotide polymorphisms. We examined and compared the methods of three companies (23andMe, deCODEme, and Navigenics) that have offered direct-to-consumer personal genome testing.
We simulated genotype data for 100,000 individuals on the basis of published genotype frequencies and predicted disease risks using the methods of the companies. Predictive ability for six diseases was assessed by the AUC.
AUC values differed among the diseases and among the companies. The highest values of the AUC were observed for age related macular degeneration, celiac disease, and Crohn disease. The largest difference among the companies was found for celiac disease: the AUC was 0.73 for 23andMe and 0.82 for deCODEme. Predicted risks differed substantially among the companies as a result of differences in the sets of single nucleotide polymorphisms selected and the average population risks selected by the companies, and in the formulas used for the calculation of risks.
Future efforts to design predictive models for the genomics of common complex diseases may benefit from understanding the strengths and limitations of the predictive algorithms designed by these early companies.
To examine whether diabetes genetic risk testing and counseling can improve diabetes prevention behaviors.
RESEARCH DESIGN AND METHODS
We conducted a randomized trial of diabetes genetic risk counseling among overweight patients at increased phenotypic risk for type 2 diabetes. Participants were randomly allocated to genetic testing versus no testing. Genetic risk was calculated by summing 36 single nucleotide polymorphisms associated with type 2 diabetes. Participants in the top and bottom score quartiles received individual genetic counseling before being enrolled with untested control participants in a 12-week, validated, diabetes prevention program. Middle-risk quartile participants were not studied further. We examined the effect of this genetic counseling intervention on patient self-reported attitudes, program attendance, and weight loss, separately comparing higher-risk and lower-risk result recipients with control participants.
The 108 participants enrolled in the diabetes prevention program included 42 participants at higher diabetes genetic risk, 32 at lower diabetes genetic risk, and 34 untested control subjects. Mean age was 57.9 ± 10.6 years, 61% were men, and average BMI was 34.8 kg/m2, with no differences among randomization groups. Participants attended 6.8 ± 4.3 group sessions and lost 8.5 ± 10.1 pounds, with 33 of 108 (30.6%) losing ≥5% body weight. There were few statistically significant differences in self-reported motivation, program attendance, or mean weight loss when higher-risk recipients and lower-risk recipients were compared with control subjects (P > 0.05 for all but one comparison).
Diabetes genetic risk counseling with currently available variants does not significantly alter self-reported motivation or prevention program adherence for overweight individuals at risk for diabetes.
Genomic sequencing is becoming accurate, fast, and inexpensive, and is rapidly being incorporated into clinical practice. Incidental findings, which result in large numbers from genomic sequencing, are a potential barrier to the utility of this new technology due to their high prevalence and the lack of evidence or guidelines available to guide their clinical interpretation. This unit reviews the definition, classification, and management of incidental findings from genomic sequencing. The unit focuses on the clinical aspects of handling incidental findings, with an emphasis on the key role of clinical context in defining incidental findings and determining their clinical relevance and utility.