Resource profile and user guide of the Polygenic Index Repository.

paper Cited Public

Authors: Becker, Joel; Burik, Casper A P; Goldman, Grant; Wang, Nancy; Jayashankar, Hariharan; Bennett, Michael; Belsky, Daniel W; Karlsson Linnér, Richard; Ahlskog, Rafael; Kleinman, Aaron; Hinds, David A; 23andMe Research Group; Caspi, Avshalom; Corcoran, David L; Moffitt, Terrie E; Poulton, Richie; Sugden, Karen; Williams, Benjamin S; Harris, Kathleen Mullan; Steptoe, Andrew; Ajnakina, Olesya; Milani, Lili; Esko, Tõnu; Iacono, William G; McGue, Matt; Magnusson, Patrik K E; Mallard, Travis T; Harden, K Paige; Tucker-Drob, Elliot M; Herd, Pamela; Freese, Jeremy; Young, Alexander; Beauchamp, Jonathan P; Koellinger, Philipp D; Oskarsson, Sven; Johannesson, Magnus; Visscher, Peter M; Meyer, Michelle N; Laibson, David; Cesarini, David; Benjamin, Daniel J; Turley, Patrick; Okbay, Aysu
Year: 2021
Journal: Nature human behaviour
PMID: 34140656
DOI: 10.1038/s41562-021-01119-3
PMCID: PMC8678380

Figure 1.

Type of study in presentations at Behavior Genetics Association Annual Meetings.Notes: For a description of the data underlying this figure, see Methods. Out of 1,993 presentations in total (over the 2009–2019 period), the percentages that are in exactly 0, 1, 2, or 3 categories are 26.6%, 67.6%, 5.5%, and 0.2%, respectively.

LLM interpretation

This line graph shows the percentage of presentations by study type at Behavior Genetics Association Annual Meetings from 2009 to 2019. The y-axis represents the percentage of presentations, and the x-axis represents the year. "Twin, Family, and Adoption Studies" (green) consistently represent the highest percentage, fluctuating between approximately 52% and 71%, while "PGI Studies" (red) show a general upward trend and "Candidate-Gene Studies" (blue) show an overall decline over the period.

Figure 2.

Algorithm determining which single-trait and multi-trait PGIs were generated for the Repository.Notes: See Table 1 for the 36 single-trait PGIs and 35 multi-trait PGIs included in the Repository.

LLM interpretation

This figure is a flow diagram illustrating the algorithmic process used to generate single-trait and multi-trait Polygenic Indices (PGIs) from summary statistics of 53 phenotypes. The workflow branches into two paths: a single-trait path involving meta-analysis of available summary statistics and a multi-trait path using joint analysis (MTAG) of target and supplementary phenotypes. Both paths culminate in a calculation of $\mathbb{E}(R^2)$, where a threshold of $\geq 0.01$ determines the final inclusion of 36 single-trait and 35 multi-trait PGIs in the repository.

Figure 3.

Predictive power of Repository single-trait PGIs.Notes: Error bars show 95% confidence intervals from bootstrapping with 1,000 repetitions. Panel (A): Incremental R2 from adding Repository’s single-trait PGI to a regression of the phenotype on 10 principal components of the genetic relatedness matrix for HRS, WLS, Dunedin and E-Risk, and on 20 principal components and 106 genotyping batch dummies for UKB. Prior to the regression, phenotypes are residualized on a second-degree polynomial for age or birth year, sex, and their interactions (see Supplementary Tables 5 and 12). For the sample sizes of the GWAS that the PGIs are based on, see Supplementary Table 8. Panel (B): Difference in incremental R2 between Repository single-trait PGI and PGI constructed from publicly available summary statistics using our Repository pipeline. (Note that the latter do not include PGI directly available from datasets, such as the ones accessible from the HRS website.) If no publicly available summary statistics are available for a phenotype, then the difference in incremental R2 is equal to the incremental R2 of the single-trait PGI and is represented by an open circle. “Cigarettes per Day” in Dunedin was omitted from the Figure because the confidence interval (−5.99% to 0.94%) around the point estimate (−2.38%) required extending the y-axis substantially, making the figure hard to read. For the GWAS sample sizes of the PGIs based on publicly available summary statistics, see Supplementary Table 13.

LLM interpretation

This figure consists of two sets of bar and point plots (Panels A and B) evaluating the predictive power of single-trait Polygenic Indices (PGIs) across various phenotypes and five datasets (HRS, WLS, Dunedin, E-Risk, and UKB). Panel A uses grouped bar charts to show the incremental $R^2$ for phenotypes categorized by Anthropometric, Cognition and Education, Fertility and Sexual Development, Health and Health Behaviors, and Personality and Well-Being, with error bars representing 95% confidence intervals. Panel B uses a point plot to show the difference in incremental $R^2$ ($\Delta$ Incremental $R^2$) between Repository PGIs and those constructed from public summary statistics, with open circles indicating cases where no public statistics were available.

#	Section	Preview
60	Methods — Summary Statistics — Multi-Trait Input GWAS	In a final step, for each of these 37 phenotypes, and for each Repository cohort, we ran a…
61	Methods — Summary Statistics — Multi-Trait Input GWAS	Each MTAG analysis produces multiple output files—one for the target phenotype and one for each of…
62	Methods — Summary Statistics — Multi-Trait Input GWAS	For multi-trait MTAG analyses, in order to understand which traits drive results from using…
63	Methods — Constructing Repository PGIs — Criterion for Inclusion in Repository	The previous section described how we generated single-trait and multi-trait input GWASs from which…
64	Methods — Constructing Repository PGIs — Criterion for Inclusion in Repository	For both single- and multi-trait PGIs, we limited the initial set of PGIs released to those with an…
65	Methods — Constructing Repository PGIs — Criterion for Inclusion in Repository	We calculated the expected predictive power of each PGI (that might potentially be included in the…
66	Methods — Constructing Repository PGIs — Criterion for Inclusion in Repository	We first used the formula above to project the expected predictive power of each potential…
67	Methods — Constructing Repository PGIs — Criterion for Inclusion in Repository	We find that our criterion results in 47 phenotypes with at least one PGI in the Repository (see…
68	Methods — Constructing Repository PGIs — Genotyping and Imputation in Repository Cohorts	Genotyping was performed using a range of commercially available arrays. Cohorts were encouraged to…
69	Methods — Constructing Repository PGIs — Genotype Data QC in Repository Cohorts	We restricted the set of markers to the SNPs present in the third phase of the international HapMap…
70	Methods — Constructing Repository PGIs — Subject-level QC in Repository Cohorts	We restricted the samples to individuals with European ancestries. Exclusion criteria were based on…
71	Methods — Constructing Repository PGIs — Creation of PCs in Repository Cohorts	In the Repository cohorts, before constructing PCs, we removed markers with imputation accuracy less…
72	Methods — Constructing Repository PGIs — Constructing PGIs	All PGIs in the initial release of the Repository were constructed in Plink279 using imputed…
73	Methods — Constructing Repository PGIs — Constructing PGIs	fed into LDpred for LD estimation, there were 1,214,408 SNPs and 14,028 individuals. At the…
74	Methods — Constructing Repository PGIs — Prediction Analyses	We conducted a validation exercise for our PGIs in the HRS (N = 10,978; 57% female; median age =…
75	Methods — Constructing Repository PGIs — Prediction Analyses	in that they were not residualized on the PCs and genotyping batch dummies. Instead, we have…
76	Methods — Constructing Repository PGIs — Prediction Analyses	Supplementary Table 3 shows the results from the prediction analyses. The incremental R2 was…
77	Methods — Measurement-Error-Corrected Estimator	Equation (4) in the main text gives an expression for our measurement-error-corrected estimator, but…
78	Methods — Measurement-Error-Corrected Estimator	To obtain standard errors for αcorr, we calculate (8)Var(αcorr)=CAg^C′, where Ag^≡Varαg^ and…
79	Methods — Estimation of ρ in HRS, WLS and UKB	We estimated the value of ρ for all PGIs satisfying the criterion for inclusion in the Repository…

Citation	PMID	DOI	Status
AbdellaouiA Genetic correlates of social stratification in Great Britain. Nat. Hum. Behav 3, 1332–1342 (2019).3163640710.1038/s41562-019-0757-5	—	—	—
AbecasisGR A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).2098109210.1038/nature09534PMC3042601	—	—	—
AutonA A global reference for human genetic variation. Nature 526, 68–74 (2015).2643224510.1038/nature15393PMC4750478	—	—	—
BarbanN Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat. Genet 48, 1462–1472 (2016).2779862710.1038/ng.3698PMC5695684	—	—	—
BeauchampJP Genetic evidence for natural selection in humans in the contemporary United States. Proc. Natl. Acad. Sci. U. S. A 113, 7774–9 (2016).2740274210.1073/pnas.1600398113PMC4948342	—	—	—
BelskyDW & HardenKP Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down. Curr. Dir. Psychol. Sci 28, 82–90 (2019).10.1177/0963721418807729PMC1108697938736689	—	—	—
BelskyDW The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development. Psychol. Sci 27, 957–972 (2016).2725148610.1177/0956797616643070PMC4946990	—	—	—
BenjaminDJ The Promises and Pitfalls of Genoeconomics. Annu. Rev. Econom 4, 627–662 (2012).2348258910.1146/annurev-economics-080511-110939PMC3592970	—	—	—
Bulik-SullivanBK An atlas of genetic correlations across human diseases and traits. Nat. Genet 47, 1236–1241 (2015).2641467610.1038/ng.3406PMC4797329	—	—	—
CesariniD & VisscherPM Genetics and educational attainment. npj Sci. Learn 2, 4 (2017).3063145110.1038/s41539-017-0005-6PMC6220209	—	—	—
ChangCC Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 1–16 (2015).2572285210.1186/s13742-015-0047-8PMC4342193	—	—	—
DaetwylerHD, VillanuevaB & WoolliamsJA Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach. PLoS One 3, e3395 (2008).1885289310.1371/journal.pone.0003395PMC2561058	—	—	—
DasS Next-generation genotype imputation service and methods. Nat. Genet 48, 1284–1287 (2016).2757126310.1038/ng.3656PMC5157836	—	—	—
DayFR Shared genetic aetiology of puberty timing between sexes and with health-related outcomes. Nat. Commun (2015). doi:10.1038/ncomms9842PMC466760926548314	—	—	—
de MoorMHM Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with Major Depressive Disorder. JAMA Psychiatry 72, 642–650 (2015).2599360710.1001/jamapsychiatry.2015.0554PMC4667957	—	—	—
de MoorMHM Meta-analysis of genome-wide association studies for personality. Mol. Psychiatry 17, 337–349 (2012).2117377610.1038/mp.2010.128PMC3785122	—	—	—
DemontisD Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet 51, 63–75 (2019).3047844410.1038/s41588-018-0269-7PMC6481311	—	—	—
DiPreteTA, BurikCAP & KoellingerPD Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data. Proc. Natl. Acad. Sci 115, E4970 LP–E4979 (2018).2968610010.1073/pnas.1707388115PMC5984483	—	—	—
DohertyA GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nat. Commun 9, 1–8 (2018).3053194110.1038/s41467-018-07743-4PMC6288145	—	—	—
DomingueBW, RehkopfDH, ConleyD & BoardmanJD Geographic Clustering of Polygenic Scores at Different Stages of the Life Course. RSF Russell Sage Found. J. Soc. Sci 4, 137 LP–149 (2018).10.7758/rsf.2018.4.4.08PMC636825430740524	—	—	—
DuncanL & KellerM A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. Am. J. Psychiatry 168, 1041 (2011).2189079110.1176/appi.ajp.2011.11020191PMC3222234	—	—	—
ElliottML A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains. Cereb. Cortex 29, 3496–3504 (2018).10.1093/cercor/bhy219PMC664517930215680	—	—	—
FerreiraMA Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Nat. Genet 49, 1752–1757 (2017).2908340610.1038/ng.3985PMC5989923	—	—	—
FreeseJ The Arrival of Social Science Genomics. Contemp. Sociol. A J. Rev 47, 524–536 (2018).	—	—	—
FurbergH Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat. Genet 42, 441–447 (2010).2041889010.1038/ng.571PMC2914600	—	—	—
GeT, ChenC-Y, NiY, FengY-CA & SmollerJW Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun 10, 1776 (2019).3099244910.1038/s41467-019-09718-5PMC6467998	—	—	—
GreenED & GuyerMS Charting a course for genomic medicine from base pairs to bedside. Nature 470, 204–213 (2011).2130793310.1038/nature09764	—	—	—
HardenKP Genetic associations with mathematics tracking and persistence in secondary school. npj Sci. Learn 5, 1 (2020).3204765110.1038/s41539-020-0060-2PMC7002519	—	—	—
HewittJK Editorial policy on candidate gene association and candidate gene-by-environment interaction studies of complex traits. Behav. Genet 42, 1–2 (2012).2192804610.1007/s10519-011-9504-z	—	—	—
HindsDA A genome-wide association meta-analysis of self-reported allergy identifies shared and allergy-specific susceptibility loci. Nat. Genet (2013). doi:10.1038/ng.2686PMC375340723817569	—	—	—
HowardDM Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci 22, 343–352 (2019).3071890110.1038/s41593-018-0326-7PMC6522363	—	—	—
HughesM Regression dilution in the proportional hazards model. Biometrics 49, 1056–1066 (1993).8117900	—	—	—
HuY GWAS of 89,283 individuals identifies genetic variants associated with self-reporting of being a morning person. Nat. Commun 7, 1–9 (2016).10.1038/ncomms10448PMC474081726835600	—	—	—
HydeCL Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet 48, 1031–6 (2016).2747990910.1038/ng.3623PMC5706769	—	—	—
JonesSE Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat. Commun 10, 1–11 (2019).3069682310.1038/s41467-018-08259-7PMC6351539	—	—	—
Karlsson LinnérR Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat. Genet 51, 245–257 (2019).3064325810.1038/s41588-018-0309-3PMC6713272	—	—	—
KheraAV Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet 50, 1219–1224 (2018).3010476210.1038/s41588-018-0183-zPMC6128408	—	—	—
KongA Selection against variants in the genome associated with educational attainment. Proc. Natl. Acad. Sci 114, E727–E732 (2017).2809641010.1073/pnas.1612113114PMC5293043	—	—	—
KunkleBW Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet 51, 414–430 (2019).3082004710.1038/s41588-019-0358-2PMC6463297	—	—	—
LambertSA The Polygenic Score Catalog: an open database for reproducibility and systematic evaluation. medRxiv 2020.05.20.20108217 (2020). doi:10.1101/2020.05.20.20108217PMC1116530333692568	—	—	—
LeeJJ Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet 50, 1112–1121 (2018).3003839610.1038/s41588-018-0147-3PMC6393768	—	—	—
LiuM Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nature Genetics 51, 237–244 (2019).3064325110.1038/s41588-018-0307-5PMC6358542	—	—	—
LockeAEA Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).2567341310.1038/nature14177PMC4382211	—	—	—
LohP-R Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet 47, 284–290 (2015).2564263310.1038/ng.3190PMC4342297	—	—	—
LohPR Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat. Genet 47, 1385–1392 (2015).2652377510.1038/ng.3431PMC4666835	—	—	—
LoM-T Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat. Genet 49, 152–156 (2016).2791853610.1038/ng.3736PMC5278898	—	—	—
MartinAR Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am. J. Hum. Genet 100, 635–649 (2017).2836644210.1016/j.ajhg.2017.03.004PMC5384097	—	—	—
McCarthyS A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet 48, 1279–1283 (2016).2754831210.1038/ng.3643PMC5388176	—	—	—
NagelM Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet 50, 920–927 (2018).2994208510.1038/s41588-018-0151-7	—	—	—
OkbayA Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet 48, 624–633 (2016).2708918110.1038/ng.3552PMC4884152	—	—	—
OkbayA Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).2722512910.1038/nature17671PMC4883595	—	—	—
PapageorgeNW & ThomK Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study. J. Eur. Econ. Assoc 18, 1351–1399 (2020).3258748310.1093/jeea/jvz072PMC7297142	—	—	—
PasmanJA GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat. Neurosci (2018). doi:10.1038/s41593-018-0206-1PMC638617630150663	—	—	—
PerryJRB Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche. Nature 514, 92–97 (2014).2523187010.1038/nature13545PMC4185210	—	—	—
PickrellJK Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet 48, 709–717 (2016).2718296510.1038/ng.3570PMC5207801	—	—	—
PurcellSM Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).1957181110.1038/nature08185PMC3912837	—	—	—
RietveldCA GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science (80-. ). 340, 1467–1471 (2013).10.1126/science.1235488PMC375158823722424	—	—	—
RietveldCA Replicability and Robustness of Genome-Wide-Association Studies for Behavioral Traits. Psychol. Sci 25, 1975–1986 (2014).2528766710.1177/0956797614545132PMC4375246	—	—	—
RobinsonMR Genetic evidence of assortative mating in humans. Nat. Hum. Behav (2017). doi:10.1038/s41562-016-0016	—	—	—
RosnerB, SpiegelmanD & WilletWC Correction of Logistic Regression Relative Risk Estimates and Confidence Intervals for Random Within-Person Measurement Error. Am. J. Epidemiol 136, 1400–1403 (1992).148896710.1093/oxfordjournals.aje.a116453	—	—	—
Sanchez-RoigeS Genome-wide association study of alcohol use disorder identification test (AUDIT) scores in 20 328 research participants of European ancestry. Addict. Biol 24, 121–131 (2019).2905837710.1111/adb.12574PMC6988186	—	—	—
Sanchez-RoigeS Genome-wide association study of delay discounting in 23,217 adult research participants of European ancestry. Nat. Neurosci 21, 16–20 (2018).2923005910.1038/s41593-017-0032-xPMC6984001	—	—	—
SavageJE Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet 50, 912–919 (2018).2994208610.1038/s41588-018-0152-6PMC6411041	—	—	—
SpearmanC The Proof and Measurement of Association between Two Things. Am. J. Psychol 15, 72–101 (1904).3322052	—	—	—
StergiakouliE Association between polygenic risk scores for attention-deficit hyperactivity disorder and educational and cognitive outcomes in the general population. Int. J. Epidemiol 46, dyw216 (2016).10.1093/ije/dyw216PMC542407627694570	—	—	—
StringerS Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium. Transl. Psychiatry 6, e769 (2016).2702317510.1038/tp.2016.36PMC4872459	—	—	—
The International HapMap 3 Consortium Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).2081145110.1038/nature09298PMC3173859	—	—	—
TrampushJW GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium. Mol. Psychiatry 22, 336–345 (2017).2809356810.1038/mp.2016.244PMC5322272	—	—	—
Tucker-DrobEM Measurement Error Correction of Genome-Wide Polygenic Scores in Prediction Samples. bioRxiv 165472 (2017). doi:10.1101/165472	—	—	—
TurleyP Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet 50, 229–237 (2018).2929238710.1038/s41588-017-0009-4PMC5805593	—	—	—
van den BergSM Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium. Behav. Genet 46, 170–182 (2016).2636257510.1007/s10519-015-9735-5PMC4751159	—	—	—
VilhjálmssonBJ Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. Am. J. Hum. Genet 97, 576–592 (2015).2643080310.1016/j.ajhg.2015.09.001PMC4596916	—	—	—
VisscherPM 10 Years of GWAS Discovery: Biology, Function, and Translation. Am. J. Hum. Genet 101, 5–22 (2017).2868685610.1016/j.ajhg.2017.06.005PMC5501872	—	—	—
WarrierV Genome-wide analyses of self-reported empathy: Correlations with autism, schizophrenia, and anorexia nervosa. Transl. Psychiatry 8, 1–10 (2018).2952700610.1038/s41398-017-0082-6PMC5845860	—	—	—
WinklerTW Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc 9, 1192–1212 (2014).2476278610.1038/nprot.2014.071PMC4083217	—	—	—
WoodAR Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet 46, 1173–1186 (2014).2528210310.1038/ng.3097PMC4250049	—	—	—
WrayNR Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet 50, 668–681 (2018).2970047510.1038/s41588-018-0090-3PMC5934326	—	—	—
WrayNR Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet 14, 507–515 (2013).2377473510.1038/nrg3457PMC4096801	—	—	—
WrayNR, GoddardME & VisscherPM Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 17, 1520–1528 (2007).1778553210.1101/gr.6665407PMC1987352	—	—	—
WrayNR, KemperKE, HayesBJ, GoddardME & VisscherPM Complex trait prediction from genome data: Contrasting EBV in livestock to PRS in humans. Genetics 211, 1131–1141 (2019).3096744210.1534/genetics.119.301859PMC6456317	—	—	—
YangJ Common SNPs explain a large proportion of the heritability for human height. Nat. Genet 42, 565–569 (2010).2056287510.1038/ng.608PMC3232052	—	—	—
YangJ, LeeSH, GoddardME & VisscherPM GCTA: A tool for genome-wide complex trait analysis. Am. J. Hum. Genet 88, 76–82 (2011).2116746810.1016/j.ajhg.2010.11.011PMC3014363	—	—	—
YengoL Imprint of Assortative Mating on the Human Genome. Nat. Hum. Behav 2, 2, 948–954 (2018).3098844610.1038/s41562-018-0476-3PMC6705135	—	—	—
YengoL Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry. Hum. Mol. Genet 27, 3641–3649 (2018).3012484210.1093/hmg/ddy271PMC6488973	—	—	—

In this knowledge base

Title	Year	PMID
Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics.	2023	37713023

External

Title	Authors	Journal	Year	Link
Assessing Orthogonality in Gene-Environment Interaction Studies Using Polygenic Indices.	Slob EAW et al.	—	2026	→
Challenges in clinical translation of polygenic risk score analyses: A systematic review.	Martínez-Minguet D et al.	—	2026	→
Clinical use of polygenic risk scores: current status, barriers and future directions.	Kullo IJ	—	2026	→
Genetic associations with education have increased and are patterned by socioeconomic context: Evidence from 3 studies born 1946-1970.	Morris TT et al.	—	2026	→
Genetic disparities in sleep traits and human capital development: A 25-year study in Finnish population-based cohorts.	Hazak A et al.	—	2026	→
Heterogeneous associations of polygenic indices of 35 traits with mortality: a register-linked population-based follow-up study.	Lahtinen H et al.	—	2026	→
Polygenic Risks for Mood Disorders and Economic Well-being: Study of Finnish Cohorts.	Hazak A et al.	—	2026	→
Scanning the Horizon of Sociogenomics: an Assessment of the Development and Growth of Polygenic Indices for Social and Behavioral Traits.	Canter C et al.	—	2026	→
The Economics and Econometrics of Gene-Environment Interplay.	Biroli P et al.	—	2026	→
Association Between Polygenic Risk Scores and Treatment Response to Antidepressants, Benzodiazepines, and Antihistamines in Anxiety and Depression.	Markant A et al.	—	2025	→
Correcting for volunteer bias in GWAS increases SNP effect sizes and heritability estimates.	van Alten S et al.	—	2025	→
Genetic influences, lifestyle and psychosocial aspects in relation to metabolically healthy obesity and conversion to a metabolically unhealthy state.	Ojalehto Lindfors E et al.	—	2025	→
Genetic predisposition for morningness-eveningness and economic disadvantage: Evidence from Finland over 25 years.	Hazak A et al.	—	2025	→
Genetics and Socioeconomic Status: Some Preliminary Evidence on Mechanisms.	Carvalho LS	—	2025	→
Genotypic and socioeconomic risks for depressive symptoms in two U.S. cohorts spanning early to older adulthood.	Sbarra DA et al.	—	2025	→
Heritability and polygenic load for comorbid anxiety and depression.	Tabrizi F et al.	—	2025	→
Large language models predict cognition and education close to or better than genomics or expert assessment.	Wolfram T	—	2025	→
Lessons in adjusting for genetic confounding in population research on education and health.	Zacher M et al.	—	2025	→
Neighborhood context, genetic influences, and life satisfaction: Evidence from the German twin family panel.	Harerimana NV et al.	—	2025	→
Neuropsychiatric polygenic scores are weak predictors of professional categories.	Voloudakis G et al.	—	2025	→
PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits.	Miao J et al.	—	2025	→
Raising the Floor? Genetic Influences on Educational Attainment Through the Lens of the Evolving Swedish Welfare State.	Pettersson O	—	2025	→
Schizophrenia genetic risk and labour market outcomes in the Finnish general population: Are schizophrenia-related traits penalised or rewarded?	Hazak A et al.	—	2025	→
Social and Behavioral Genomics: On the Ethics of the Research and Its Downstream Applications.	Martschenko DO et al.	—	2025	→
The effect of peers' genetic predisposition to depression on own mental health.	Jeong Y	—	2025	→
Using genomic structural equation modeling to examine the genetic architecture of PTSD and life satisfaction phenotypes.	Cusack SE et al.	—	2025	→
A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology.	Gupta P et al.	—	2024	→
An analysis of the accuracy of retrospective birth location recall using sibling data.	von Hinke S et al.	—	2024	→
Causal interpretations of family GWAS in the presence of heterogeneous effects.	Veller C et al.	—	2024	→
Childhood cognitive ability and self-harm and suicide in later life.	Iveson MH et al.	—	2024	→
Exposure to sugar rationing in the first 1000 days of life protected against chronic disease.	Gracner T et al.	—	2024	→
Gene-environment interaction in expertise acquisition: Practice effects on musical expertise vary by polygenic scores for cognitive performance.	Wesseldijk LW et al.	—	2024	→
Genetic analysis of selection bias in a natural experiment: Investigating in-utero famine effects on elevated body mass index in the Dutch Hunger Winter Families Study.	Zhou J et al.	—	2024	→
Genetic influences on depression and selection into adverse life experiences.	Rauf T et al.	—	2024	→
Genetic links between atopy, allergy, and alopecia areata: insights from a Mendelian randomization study.	Xu W et al.	—	2024	→
Implications of the genomic revolution for education research and policy.	Morris TT et al.	—	2024	→
Individual differences in adolescent self-control: The role of gene-environment interplay.	Willems YE et al.	—	2024	→
Infrastructuring Educational Genomics: Associations, Architectures, and Apparatuses.	Williamson B et al.	—	2024	→
Metabolic-dysfunction associated steatotic liver disease-related diseases, cognition and dementia: A two-sample mendelian randomization study.	Li YS et al.	—	2024	→
Natural Selection Across Three Generations of Americans.	Hugh-Jones D et al.	—	2024	→
Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation.	Burt CH	—	2024	→
Polygenic Scores Clarify the Relationship Between Mental Health and Gender Diversity.	Thomas TR et al.	—	2024	→
Predicting political beliefs with polygenic scores for cognitive performance and educational attainment.	Edwards T et al.	—	2024	→
The Add Health Parent Study: A Biosocial Resource for the Study of Multigenerational Racial/Ethnic Disparities in Alzheimer's Disease and Alzheimer's Disease-Related Dementias.	Perreira KM et al.	—	2024	→
The Legal Uncertainties of Sociogenomic Polygenic Scores.	Callier S et al.	—	2024	→
Using the phenotype differences model to identify genetic effects in samples of partially genotyped sibling pairs.	Trejo S et al.	—	2024	→
Association of Cognitive Polygenic Index and Cognitive Performance with Age in Cognitively Healthy Adults.	Tsapanou A et al.	—	2023	→
Causal interpretations of family GWAS in the presence of heterogeneous effects	Veller C et al.	—	2023	—
Challenges in studying the interplay of genes and environment. A study of childhood financial distress moderating genetic predisposition for peak smoking.	Bierut L et al.	—	2023	→
Cohort profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G).	Koellinger PD et al.	—	2023	→
Genetic associations between alcohol phenotypes and life satisfaction: a genomic structural equation modelling approach.	Bountress KE et al.	—	2023	→
Genetic confounding in bullying research: Causal claims revisited.	Vrijen C et al.	—	2023	→
Genetic propensity, socioeconomic status, and trajectories of depression over a course of 14 years in older adults.	Kosciuszko M et al.	—	2023	→
Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics.	Williams CM et al.	—	2023	→
Implementing Reporting Standards for Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease.	Smith JL et al.	—	2023	→
Maternal genetic risk for depression and child human capital.	Menta G et al.	—	2023	→
Neuroanatomical correlates of genetic risk for obesity in children.	Morys F et al.	—	2023	→
Overcoming attenuation bias in regressions using polygenic indices.	van Kippersluis H et al.	—	2023	→
Partners in Health: Investigating Social Genetic Effects Among Married and Cohabiting Couples.	Otten K et al.	—	2023	→
Polygenic Prediction of Education and Its Role in the Intergenerational Transmission of Education: Cohort Changes Among Finnish Men and Women Born in 1925-1989.	Lahtinen H et al.	—	2023	→
Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics.	Miao J et al.	—	2023	→
Rank concordance of polygenic indices.	Muslimova D et al.	—	2023	→
The Dunedin study after half a century: reflections on the past, and course for the future.	Poulton R et al.	—	2023	→
The ELSI Virtual Forum, 30 Years of the Genome: Integrating and Applying ELSI Research.	Moore CB et al.	—	2023	→
The identification of mediating effects using genome-based restricted maximum likelihood estimation.	Rietveld CA et al.	—	2023	→
The impact of entrepreneurship research on other academic fields.	Thurik AR et al.	—	2023	→
Trends in the ability of socioeconomic position to predict individual body mass index: an analysis of repeated cross-sectional data, 1991-2019.	Wright L et al.	—	2023	→
Wrestling with Public Input on an Ethical Analysis of Scientific Research.	Martschenko DO et al.	—	2023	→
Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility.	Meyer MN et al.	—	2023	→
Assessing the contribution of genetic nurture to refractive error.	Guggenheim JA et al.	—	2022	→
Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists.	Collister JA et al.	—	2022	→
Clinical autism subscales have common genetic liabilities that are heritable, pleiotropic, and generalizable to the general population.	Thomas TR et al.	—	2022	→
High polygenic predisposition for ADHD and a greater risk of all-cause mortality: a large population-based longitudinal study.	Ajnakina O et al.	—	2022	→
Luck, lottery, or legacy? The problem of confounding. A reply to Harden.	Coop G et al.	—	2022	→
Reply to Qiu et al.: Hunting for leadership "causal" genes: Mission possible?	Song Z et al.	—	2022	→
Research Review: A guide to computing and implementing polygenic scores in developmental research.	Allegrini AG et al.	—	2022	→
Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics.	Judd N et al.	—	2022	→
Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research.	Lopez-Pineda A et al.	—	2022	→
A polygenic score for educational attainment partially predicts voter turnout.	Dawes CT et al.	—	2021	→
Discovery and implications of polygenicity of common diseases.	Visscher PM et al.	—	2021	→