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. 2019 Jul 3;105(1):65-77.
doi: 10.1016/j.ajhg.2019.05.006. Epub 2019 Jun 13.

Genes for Good: Engaging the Public in Genetics Research via Social Media

Katharine Brieger  1 , Gregory J M Zajac  2 , Anita Pandit  3 , Johanna R Foerster  2 , Kevin W Li  2 , Aubrey C Annis  2 , Ellen M Schmidt  4 , Chris P Clark  2 , Karly McMorrow  2 , Wei Zhou  5 , Jingjing Yang  6 , Alan M Kwong  2 , Andrew P Boughton  2 , Jinxi Wu  7 , Chris Scheller  2 , Tanvi Parikh  8 , Alejandro de la Vega  8 , David M Brazel  9 , Maia Frieser  10 , Gianna Rea-Sandin  11 , Lars G Fritsche  2 , Scott I Vrieze  12 , Gonçalo R Abecasis  13
Affiliations

Genes for Good: Engaging the Public in Genetics Research via Social Media

Katharine Brieger et al. Am J Hum Genet. .

Erratum in

  • Genes for Good: Engaging the Public in Genetics Research via Social Media.
    Brieger K, Zajac GJM, Pandit A, Foerster JR, Li KW, Annis AC, Schmidt EM, Clark CP, McMorrow K, Zhou W, Yang J, Kwong AM, Boughton AP, Wu J, Scheller C, Parikh T, de la Vega A, Brazel DM, Frieser M, Rea-Sandin G, Fritsche LG, Vrieze SI, Abecasis GR. Brieger K, et al. Am J Hum Genet. 2019 Aug 1;105(2):441-442. doi: 10.1016/j.ajhg.2019.07.009. Am J Hum Genet. 2019. PMID: 31374205 Free PMC article. No abstract available.

Abstract

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.

Keywords: asthma; body mass index; complex traits; diabetes; direct to participant research; genome-wide association study; genotyping array; participant engagement; population study; social media.

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Figures

Figure 1
Figure 1
Geographic Distribution The geographic distribution of Genes for Good participants as of October 2017. The colors indicate the number of participants who have logged into the app from that county, with darker colors representing higher density.
Figure 2
Figure 2
Relationship between BMI and Diabetes Rates in Participants Is Consistent with that Seen in the General US Population Type 2 diabetes is a phenotype of particular interest because of its increasing prevalence, impact on cardiovascular health, and relatively well-characterized genetics. Here, we have compared the rates of diabetes in Genes for Good participants to the rates found in the nationally representative studies SHIELD and NHANES.
Figure 3
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.
Figure 4
Figure 4
Effect Size Estimates of a GWAS for BMI in Our Study Sample Compared to Findings from a Meta-analysis We compare effect estimates from Genes for Good to published findings from the Locke et al. meta-analysis of BMI GWAS. Specifically, we looked at the top ten reported signals and were able to replicate all of these effects in direction and nominal significance (p < 0.05). The forest plot on the right compares effect size estimates across studies; the dashed lines represent the confidence intervals around the Genes for Good estimates, while the solid lines represent results from Locke et al. Given the relatively small sample size available in this data freeze, our estimates have fairly wide confidence limits. However, Locke’s estimates are completely contained within our limits for eight of ten SNPs. Asterisk indicates imputed variant.
Figure 5
Figure 5
LocusZoom Plot Showing Single-Variant Association Results for BMI in FTO This result is consistent with other studies that reported their strongest evidence for association in this gene. The effect size at the nearby SNP rs1558902 (0.081) was consistent with the effect size (0.081) reported previously in Locke et al.
Figure 6
Figure 6
Prevalence for Self-Reported Type 1 and Type 2 Diabetes across Polygenic Risk Score Quintiles (Five Bins of Equal Sample Size) An increase in the genetic risk score is associated with increasing prevalence of disease. We also evaluated associations between polygenic risk score quintile and type 1 diabetes, type 2 diabetes, and asthma status, adjusted for age and sex. We found that all three self-reported traits were significantly associated with calculated PRS quintile (pT1D = 5.13 × 10−9, pT2D = 7.63 × 10−37, pasthma = 3.17 × 10−26).
Figure 7
Figure 7
Example Health History Result An example of how participants’ results to the personality survey are displayed within the Genes for Good app. The bars show this participant’s percentile scores on the five personality attributes measured by the survey.
Figure 8
Figure 8
Example Daily Tracking Result An example of how participants’ answers to the daily sleep tracking survey are displayed, showing (A) average hours of sleep for this participant, compared to other participants of the same age range and sex, and to all other Genes for Good participants, (B) average hours of sleep reported for different days of the week when this participant has taken the survey, (C) average hours of sleep over the past 7 days, past 30 days, and over all responses from this participant, and (D) average hours of sleep reported for different days of the week for all Genes for Good participants stratified by sex.
See this image and copyright information in PMC

References

    1. Welter D., MacArthur J., Morales J., Burdett T., Hall P., Junkins H., Klemm A., Flicek P., Manolio T., Hindorff L., Parkinson H. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–D1006. - PMC - PubMed
    2. Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., and Parkinson, H. (2014). The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001-D1006. - PMC - PubMed
    1. Stoeklé H.C., Mamzer-Bruneel M.F., Vogt G., Hervé C. 23andMe: a new two-sided data-banking market model. BMC Med. Ethics. 2016;17:19. - PMC - PubMed
    2. Stoekle, H.C., Mamzer-Bruneel, M.F., Vogt, G., and Herve, C. (2016). 23andMe: a new two-sided data-banking market model. BMC Med. Ethics 17, 19. - PMC - PubMed
    1. Royal C.D., Novembre J., Fullerton S.M., Goldstein D.B., Long J.C., Bamshad M.J., Clark A.G. Inferring genetic ancestry: opportunities, challenges, and implications. Am. J. Hum. Genet. 2010;86:661–673. - PMC - PubMed
    2. Royal, C.D., Novembre, J., Fullerton, S.M., Goldstein, D.B., Long, J.C., Bamshad, M.J., and Clark, A.G. (2010). Inferring genetic ancestry: opportunities, challenges, and implications. Am. J. Hum. Genet. 86, 661-673. - PMC - PubMed
    1. Agurs-Collins T., Ferrer R., Ottenbacher A., Waters E.A., O’Connell M.E., Hamilton J.G. Public Awareness of Direct-to-Consumer Genetic Tests: Findings from the 2013 U.S. Health Information National Trends Survey. J. Cancer Educ. 2015;30:799–807. - PMC - PubMed
    2. Agurs-Collins, T., Ferrer, R., Ottenbacher, A., Waters, E.A., O’Connell, M.E., and Hamilton, J.G. (2015). Public Awareness of Direct-to-Consumer Genetic Tests: Findings from the 2013 U.S. Health Information National Trends Survey. J. Cancer Educ. 30, 799-807. - PMC - PubMed
    1. Pedersen E.R., Kurz J. Using Facebook for Health-related Research Study Recruitment and Program Delivery. Curr Opin Psychol. 2016;9:38–43. - PMC - PubMed
    2. Pedersen, E.R., and Kurz, J. (2016). Using Facebook for Health-related Research Study Recruitment and Program Delivery. Curr Opin Psychol 9, 38-43. - PMC - PubMed

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