Polygenic Score Data (PGS)

Latest ReleaseJuly 2017 (V1.0)
Data AlertsNone reported for this product

Complex health outcomes or behaviors of interest to the research community are often highly polygenic, or reflect the aggregate effect of many different genes so the use of single genetic variants or candidate genes may not capture the dynamic nature of more complex phenotypes. A polygenic score (PGS) aggregates millions of individual loci across the human genome and weights them by the strength of their association to produce a single quantitative measure of genetic risk. This maximizes statistical power when modeling gene-environment (G x E) interactions.

PGS for a variety of phenotypes have been constructed as part of this public data release for HRS respondents who provided salivary DNA between 2006 and 2010. These scores will help harmonize research across studies. PGS for each phenotype are based on a single, replicated genome-wide association study (GWAS). These scores will be updated as sufficiently large GWAS are published for new phenotypes or as meta-analyses for existing phenotypes are updated.

Phenotype of GWAS meta-analysis GWAS meta-analysis citation
Educational attainment (HRS removed) Okbay (SSGAC, 2016) 1
Height Wood (GIANT, 2014) 2
Body Mass Index Locke (GIANT, 2015) 3
Alzheimer's disease Lambert (IGAP, 2013) 4
General Cognition (HRS removed) Davies (CHARGE, 2015) 5
Schizophrenia PGC (2009) 6
Major Depressive Disorder PGC (2013) 7
Attention Deficit Hyperactivity Disorder Neale (PGC, 2010) 8
Bipolar Disorder PGC (2011) 9
Mental Health Cross Disorder PGC (2013) 10
Blood pressure (DBP, SBP, PP, MAP) ICBP (2011) 11
Smoking (ever, cigarettes per day) Furberg (TAG, 2010) 12
Subjective Wellbeing (HRS removed) Okbay (SSGAC, 2016) 13
Neuroticism Okbay (SSGAC, 2016) 13
Longevity (HRS removed) Broer (CHARGE, 2015) 14
Kidney Function (HRS removed) Pattaro (2016) 15


  1. Okbay A, Beauchamp JP, Fontana MA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533(7604):539-542.
  2. Wood AR, Esko T, Yang J, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46(11):1173-1186.
  3. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197-206.
  4. Lambert JC, Ibrahim-Verbaas CA, Harold D, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013;45(12):1452-1458.
  5. Davies G, Armstrong N, Bis JC, et al. Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53949). Mol Psychiatry. 2015;20(2):183-192.
  6. International Schizophrenia C, Purcell SM, Wray NR, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748-752.
  7. Major Depressive Disorder Working Group of the Psychiatric GC, Ripke S, Wray NR, et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry. 2013;18(4):497-511.
  8. Neale BM, Medland SE, Ripke S, et al. Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2010;49(9):884-897.
  9. Psychiatric GCBDWG. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet. 2011;43(10):977-983.
  10. Cross-Disorder Group of the Psychiatric Genomics C. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371-1379.
  11. International Consortium for Blood Pressure Genome-Wide Association S, Ehret GB, Munroe PB, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478(7367):103-109.
  12. Consortium TG. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet. 2010;42(5):441-447.
  13. Okbay A, Baselmans BM, De Neve JE, et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet. 2016;48(6):624-633.
  14. Broer L, Buchman AS, Deelen J, et al. GWAS of longevity in CHARGE consortium confirms APOE and FOXO3 candidacy. J Gerontol A Biol Sci Med Sci. 2015;70(1):110-118.
  15. Pattaro C, Teumer A, Gorski M, et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016;7:10023.