Objective. Advances in genome-wide association studies have made possible the return of genetic risk results for complex diseases. Two concerns about these results are: a) negative psychological consequences; and b) viewing probabilistic results as deterministic, leading to misinterpretation and inappropriate decisions. The present study evaluates these concerns through a meta-analytic review of existing literature. Methods. Seventeen genetic testing studies of complex disease, including 1,171 participants and reporting 195 effects, 104 of which were unadjusted for covariates, were meta-analyzed under a random effects model. Diseases included Alzheimer’s, cardiovascular and coronary heart disease, lung cancer, melanoma, thrombophilia, and type II diabetes. Six domains of behavioral/psychological reactions were examined. Results. Carriers showed significantly increased self-reported behavior change compared to non-carriers when assessed six months or later after results return (Hedge’s g = .36, p = .019). Conclusions. Return of genetic testing results for complex disease does not strongly impact self-reported negative behavior or psychological function of at-risk individuals. Return of results does appear to moderately increase self-reported healthy behavior in carriers, although research on objectively observed behavioral change is needed. This is a growing area of research, with preliminary results suggesting potential positive implications of genetic testing for complex disease on behavior change.

Insights into individual differences in gene expression and its heritability (h 2) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52,844 genes measured in whole blood in the largest twin RNA-Seq sample to date (1497 individuals including 459 monozygotic twin pairs and 150 dizygotic twin pairs) from classical twin modeling and identity-by-state-based approaches. We estimated for each gene h 2 total , composed of cisheritability (h 2 cis , the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and transheritability (h 2 res , the residual variance explained by all other genome-wide variants). Mean h 2 total was 0.26, which was significantly higher than heritability estimates earlier found in a microarray-based study using largely overlapping (>60%) RNA samples (mean h 2 = 0.14, p = 6.15 × 10 −258). Mean h 2 cis was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, ρ = 0.76, p < 10 −308) and with estimates from earlier RNA-Seq-based studies. Mean h 2 res was 0.20 and correlated with the beta of the corresponding trans-eQTL (ρ = 0.04, p < 1.89 × 10 −3) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 × 10 −15), many other immune system pathways, and genes identified in genome-wide association studies for various traits including behavioral disorders and cancer. This study provides a thorough characterization of cisand trans-h 2 estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies.

Figure 3. Eye Color Distribution Distribution of eye color among participants with different genotypes at rs12913832 (the top signal when performing GWAS using blue eye color in Genes for Good participants), a marker in HERC2 known to play a role in eye color determination.

The Genes for Good study uses social media to engage a large, diverse participant pool in genetics research and education. Health history and daily tracking surveys are administered through a Facebook application, and participants who complete a minimum number of surveys are mailed a saliva sample kit (''spit kit'') to collect DNA for genotyping. As of March 2019, we engaged >80,000 individuals, sent spit kits to >32,000 individuals who met minimum participation requirements, and collected >27,000 spit kits. Participants come from all 50 states and include a diversity of ancestral backgrounds. Rates of important chronic health indicators are consistent with those estimated for the general U.S. population using more traditional study designs. However, our sample is younger and contains a greater percentage of females than the general population. As one means of verifying data quality, we have replicated genome-wide association studies (GWASs) for exemplar traits, such as asthma, diabetes, body mass index (BMI), and pigmentation. The flexible framework of the web application makes it relatively simple to add new questionnaires and for other researchers to collaborate. We anticipate that the study sample will continue to grow and that future analyses may further capitalize on the strengths of the longitudinal data in combination with genetic information.

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