There is currently great interest in using genetic risk estimates for common disease in personalized healthcare. Here we assess melanoma risk-related preventive behavioral change in the context of the Coriell Personalized Medicine Collaborative (CPMC). As part of on-going reporting activities within the project, participants received a personalized risk assessment including information related to their own self-reported family history of melanoma and a genetic risk variant showing a moderate effect size (1.7, 3.0 respectively for heterozygous and homozygous individuals). Participants who opted to view their report were sent an optional outcome survey assessing risk perception and behavioral change in the months that followed. Participants that report family history risk, genetic risk, or both risk factors for melanoma were significantly more likely to increase skin cancer preventive behaviors when compared to participants with neither risk factor (ORs = 2.04, 2.79, 4.06 and p-values = 0.02, 2.86 × 10⁻⁵, 4.67 × 10⁻⁵, respectively), and we found the relationship between risk information and behavior to be partially mediated by anxiety. Genomic risk assessments appear to encourage positive behavioral change in a manner that is complementary to family history risk information and therefore may represent a useful addition to standard of care for melanoma prevention.

Sleep is critical to health and functionality, and several studies have investigated the inherited component of insomnia and other sleep disorders using genome‐wide association studies (GWAS). However, genome‐wide studies focused on sleep duration are less common. Here, we used data from participants in the Coriell Personalized Medicine Collaborative (CPMC) (n = 4,401) to examine putative associations between self‐reported sleep duration, demographic and lifestyle variables, and genome‐wide single nucleotide polymorphism (SNP) data to better understand genetic contributions to variation in sleep duration. We employed stepwise ordered logistic regression to select our model and retained the following predictive variables: age, gender, weight, physical activity, physical activity at work, smoking status, alcohol consumption, ethnicity, and ancestry (as measured by principal components analysis) in our association testing. Several of our strongest candidate genes were previously identified in GWAS related to sleep duration (TSHZ2, ABCC9, FBXO15) and narcolepsy (NFATC2, SALL4). In addition, we have identified novel candidate genes for involvement in sleep duration including SORCS1 and ELOVL2. Our results demonstrate that the self‐reported data collected through the CPMC are robust, and our genome‐wide association analysis has identified novel candidate genes involved in sleep duration. More generally, this study contributes to a better understanding of the complexity of human sleep. © 2015 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Here we describe a new public pharmacogenetic (PGx) annotation database of a large (n = 3,202) and diverse biospecimen collection of 1000 Genomes Project cell lines and DNAs. The database is searchable with a user friendly, web-based tool (www.coriell.org/StarAllele/Search). This resource leverages existing whole genome sequencing data and PharmVar annotations to characterize *alleles for each biospecimen in the collection. This new tool is designed to facilitate in vitro functional characterization of *allele haplotypes and diplotypes as well as support clinical PGx assay development, validation, and implementation.

There has been very limited study of patients with chronic disease receiving potentially actionable genomic based results or the utilization of genetic counselors in the online result delivery process. We conducted a randomized controlled trial on 199 patients with chronic disease each receiving eight personalized and actionable complex disease reports online. Primary study aims were to assess the impact of in‐person genomic counseling on 1) causal attribution of disease risk, 2) personal awareness of disease risk, and 3) perceived risk of developing a particular disease. Of 98 intervention arm participants (mean age = 57.8; 39% female) randomized for in‐person genomic counseling, 76 (78%) were seen. In contrast, control arm participants (n= 101; mean age = 58.5; 54% female) were initially not offered genomic counseling as part of the study protocol but were able to access in‐person genomic counseling, if they requested it, 3‐months post viewing of at least one test report and post‐completion of the study‐specific follow‐up survey. A total of 64 intervention arm and 59 control arm participants completed follow‐up survey measures. We found that participants receiving in‐person genomic counseling had enhanced objective understanding of the genetic variant risk contribution for multiple complex diseases. Genomic counseling was associated with lowered participant causal beliefs in genetic influence across all eight diseases, compared to control participants. Our findings also illustrate that for the majority of diseases under study, intervention arm participants believed they knew their genetic risk status better than control arm subjects. Disease risk was modified for the majority during genomic counseling, due to the assessment of more comprehensive family history. In conclusion, for patients receiving personalized and actionable genomic results through a web portal, genomic counseling enhanced their objective understanding of the genetic variant risk contribution to multiple common diseases. These results support the development of additional genomic counseling interventions to ensure a high level of patient comprehension and improve patient‐centered health outcomes.

Genomic applications raise multiple challenges including the optimization of genomic counseling (GC) services as part of the results delivery process. More information on patients’ motivations, preferences, and informational needs are essential to guide the development of new, more efficient practice delivery models that capitalize on the existing strengths of a limited genetic counseling workforce. Semi‐structured telephone interviews were conducted with a subset of counselees from the Coriell Personalized Medicine Collaborative following online receipt of multiple personalized genomic test reports. Participants previously had either in‐person GC (chronic disease cohort, n = 20; mean age 60 years) or telephone GC (community cohort, n = 31; mean age 46.8 years). Transcripts were analyzed using a Grounded Theory framework. Major themes that emerged from the interviews include 1) primary reasons for seeking GC were to clarify results, put results into perspective relative to other health‐related concerns, and to receive personalized recommendations; 2) there is need for a more participant driven approach in terms of mode of GC communication (in‐person, phone, video), and refining the counseling agenda pre‐session; and 3) there was strong interest in the option of follow up GC. By clarifying counselees’ expectations, views and desired outcomes, we have uncovered a need for a more participant‐driven GC model when potentially actionable genomic results are received online.