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Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations
Open accessAbstract
Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.
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“Previous studies have found that population diversity may improve fine-mapping resolution38,39”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [39]
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“Genetic risk prediction attenuates with increasing divergence between the discovery and target populations, indicating that polygenic risk scores (PRSs) based on Eurocentric GWASs are not equally predictive when applied to non-European populations (Duncan et al., 2019; Martin et al., 2019).”
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“In response to the lack of diversity in the HGP, the International HapMap project (2002 – 2010) and the 1000 Genomes Project (2008-2015) were established to provide a more comprehensive and diverse representation of the human genome [16, 19].”
BackgroundMethodFlorio, A. D., Yang, J. M. K., Crawford, K., Bergink, V., Leonenko, G., Pardiñas, A. F., Escott‐Price, V., Gordon‐Smith, K., Owen, M. J., Craddock, N. J., et al. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00253-4 · Cited by 35
Spracklen, C. N., Iyengar, A. K., Vadlamudi, S., Raulerson, C. K., Jackson, A., Brotman, S. M., Wu, Y., Cannon, M. E., Davis, J. P., Crain, A. T., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1009019 · Cited by 22
“Finally, the prediction accuracy of polygenic risk scores relies on the full set of variants that affect a trait, and including variants from multiple association signals at a locus can improve prediction accuracy[30].”
BackgroundChen, Y., Liu, S., Ren, Z., Wang, F., Liang, Q., Jiang, Y. W., Dai, R., Duan, F., Han, C., Ning, Z., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.09.001 · Cited by 27
Self-citationDecoding the genetic landscape of autism: A comprehensive review
Al‐Beltagi, M., Saeed, N. K., Bediwy, A. S., Bediwy, E. A., & Elbeltagi, R. M. (2024) · World Journal of Clinical Pediatrics · doi:10.5409/wjcp.v13.i3.98468 · Cited by 23
Genetic ancestry and diagnostic yield of exome sequencing in a diverse population
Mavura, Y., Sahin‐Hodoglugil, N., Hodoğlugil, U., Kvale, M. N., Martin, P.-M., Ziffle, J. V., Devine, W. P., Ackerman, S., Koenig, B. A., Kwok, P., et al. (2024) · npj Genomic Medicine · doi:10.1038/s41525-023-00385-6 · Cited by 20
“Some studies have suggested that diagnostic yield from ES and other genetic tests is lower in non-white race/ethnicity groups, such as African American, or Native American 3,9 possibly due to underrepresentation of data from non-white populations in genetic variant databases 2,3,10 .”
BackgroundImputation Accuracy Across Global Human Populations
Cahoon, J. L., Rui, X.-Y., Tang, E., Simons, C., Langie, J., Chen, M.-H., Lo, Y.-C., & Chiang, C. W. K. (2023) · bioRxiv · doi:10.1101/2023.05.22.541241 · Cited by 22
Actionable Solutions to Achieve Health Equity in Chronic Liver Disease
Jones, P. D., Lai, J., Bajaj, J., & Kanwal, F. (2023) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2023.03.043 · Cited by 22
“4 There are widening racial and ethnic disparities across many CLDs and their outcomes.”
BackgroundThe legacy of the Human Genome Project
Rood, J., & Regev, A. (2021) · Science · doi:10.1126/science.abl5403 · Cited by 28
Temporal and genomic analysis of additive genetic variance in breeding programmes
Lara, L. A. D. C., Pocrnić, I., Oliveira, T. D. P., Gaynor, R. C., & Gorjanc, G. (2021) · Heredity · doi:10.1038/s41437-021-00485-y · Cited by 44
“Estimation of background-specific effects is an active research area in genetics with growing datasets across various populations (e.g., Peterson et al., 2019; van den Berg et al., 2020).”
BackgroundWeissman, M. M. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.0954 · Cited by 27
Yuan, S., Chen, J., Ruan, X., Li, Y., Abramowitz, S. A., Wang, L., Jiang, F., Xiong, Y., Levin, M. G., Voight, B. F., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61720-2 · Cited by 25
Self-citationPolygenic risk scores in pharmacogenomics: opportunities and challenges—a mini review
Simona, A., Song, W., Bates, D. W., & Samer, C. F. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1217049 · Cited by 26
“First, a significant well-established bias resides in the predominance of European ancestry in GWAS effect size estimation, limiting the generalizability of PRS results to other populations that are already underserved (Martin et al., 2019; Tata et al., 2020; Peterson et al., 2019; Popejoy and Fullerton, 2016).”
BackgroundUsing DNA to predict intelligence
von Stumm, S., & Plomin, R. J. (2021) · Intelligence · doi:10.1016/j.intell.2021.101530 · Cited by 33
Strawbridge, R. J., Johnston, K. J. A., Bailey, M. E. S., Baldassarre, D., Cullen, B., Eriksson, P. O., DeFaire, U., Ferguson, A., Gigante, B., Giral, P., et al. (2021) · Scientific Reports · doi:10.1038/s41598-020-79964-x · Cited by 20
Self-citationZonca, V., Marizzoni, M., Saleri, S., Zajkowska, Z., Manfro, P. H., Souza, L., Viduani, A. C., Sforzini, L., Swartz, J. R., Fisher, H. L., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02959-z · Cited by 25
“…acknowledge the lack of studies in non-European countries also with regards to genome-wide association studies (GWAS), as almost all GWAS have been conducted primarily in populations of European descendants, whereas it is essential that ancestrally diverse populations become better represented [9].”
ResultMendelian randomization studies on coronary artery disease: a systematic review and meta-analysis
Silva, S., Fatumo, S., & Nitsch, D. (2024) · Systematic Reviews · doi:10.1186/s13643-023-02442-8 · Cited by 24
“…and prevalence of CAD between ancestries has resulted in the widespread acknowledgement that increasing diversity amongst study participants is essential in order to continue advancing our understanding of the genetic architecture and pathophysiology of CAD from a global perspective [7, 29, 67].”
BackgroundRaghavan, S., Huang, J., Tcheandjieu, C., Huffman, J. E., LITKOWSKI, E. M., Liu, C., Ho, Y.-L. A., Hunter-Zinck, H., Zhao, H., Marouli, E., et al. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010193 · Cited by 24
Icick, R., Forget, B., Cloëz-Tayarani, I., Pons, S., Maskos, U., & Besson, M. (2020) · Neuropharmacology · doi:10.1016/j.neuropharm.2020.108234 · Cited by 31
Genome‐wide association studies: utility and limitations for research in physiology
Ciochetti, N. P., Lugli‐Moraes, B., da Silva, B. S., & Rovaris, D. L. (2023) · The Journal of Physiology · doi:10.1113/jp284241 · Cited by 25
“Finally, the most critical current concern related to GWAS is the eurocentric bias observed in the available studies (Gurdasani et al., 2019; Hindorff et al., 2018; Martin et al., 2019; Mills & Rahal, 2019; Peterson et al., 2019).”
Latin American Trans‐ancestry INitiative for OCD genomics (LATINO): Study protocol
Crowley, J. J., Cappi, C., Ochoa-Panaifo, M. E., Frederick, R. M., Kook, M., Wiese, A. D., Rancourt, D., Atkinson, E. G., Giusti‐Rodríguez, P., Anderberg, J. L., et al. (2023) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32962 · Cited by 21
“Furthermore, admixed populations, such as Latino/Latina (hereafter Latino) populations, constitute more than a third of the U.S. population and yet are markedly understudied in medical and psychiatric genetics (Peterson et al., 2019).”
BackgroundSelf-citationKosciuszko, M., Steptoe, A., & Ajnakina, O. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02367-9 · Cited by 23
“Even though this type of labelling of ancestral populations oversimplifies the complexity of human genetic variation, accounting for systematic differences in allele frequencies is necessary for genetic analyses [32].”
BackgroundMethodOptimal strategies for learning multi-ancestry polygenic scores vary across traits
Lehmann, B., Mackintosh, M. E., McVean, G., & Holmes, C. C. (2023) · Nature Communications · doi:10.1038/s41467-023-38930-7 · Cited by 20
Hodonsky, C. J., Baldassari, A., Bien, S. A., Raffield, L. M., Highland, H. M., Sitlani, C. M., Wojcik, G. L., Tao, R., Graff, M., Tang, W., et al. (2020) · BMC Genomics · doi:10.1186/s12864-020-6626-9 · Cited by 24
Cross-ancestry genomic research: time to close the gap
Atkinson, E. G., Bianchi, S. B., Ye, G. Y., Martínez-Magaña, J. J., Tietz, G. E., Montalvo-Ortiz, J. L., Giusti-Rodriguez, P., Palmer, A. A., & Sanchez-Roige, S. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01365-7 · Cited by 19
“Beyond these efforts to generate more diverse data and enhance methods for its analysis, other challenges remain to increase equity in genomics research [5, 9, 10, 24].”
BackgroundShared genetic architectures of educational attainment in East Asian and European populations
Chen, T.-T., Kim, J., Lam, M., Chuang, Y.-F., Chiu, Y.-L., Lin, S.-C., Jung, S.-H., Kim, B., Kim, S., Cho, C., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01781-9 · Cited by 18
“https://doi.org/10.1038/s41562-023-01781-9 Previous studies have found that population diversity may improve fine-mapping resolution 38,39 by capitalizing on increased GWAS sample sizes and LD differences across ancestries.”
InfluentialSelf-citationMulti-trait GWAS for diverse ancestries: mapping the knowledge gap
Troubat, L., Fettahoglu, D., Henches, L., Aschard, H., & Julienne, H. (2024) · BMC Genomics · doi:10.1186/s12864-024-10293-3 · Cited by 19
“Increasing the sampling of non-European ancestries is paramount, but computational method development is also an area of improvement that is less often discussed [10, 11].”
BackgroundCheng, X., Du, F., Long, X., & Huang, J.-Z. (2023) · Genes · doi:10.3390/genes14101859 · Cited by 18
“One of the pitfalls of GWAS is that the result is easily confounded by different an-cestries of selected samples, case–control imbalance, various sequencing methods, and linkage disequilibrium, leading to poor stability and transferability across different study groups [37–39].”
BackgroundPhenotype and genetic analysis of data collected within the first year of NeuroDev
Kipkemoi, P., Kim, H. A., Christ, B. U., O’Heir, E., Allen, J., Austin-Tse, C. A., Baxter, S. M., Brand, H., Bryant, S., Buser, N., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.06.010 · Cited by 22
“People of African ancestry have been grossly underrepresented in genetic studies, across domains and disciplines.(35,36) In aggregate, this is most visible through the constitution of large genetic databases such as gnomAD, in which only 14% of individuals (N z 27,000) have some African ancestry.”
Self-citationThe phenotype-genotype reference map: Improving biobank data science through replication
Bastarache, L. A., Delozier, S., Pandit, A., He, J., Lewis, A. J., Annis, A. C., LeFaive, J., Denny, J. C., Carroll, R. J., Altman, R. B., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.07.012 · Cited by 30
“Moreover, the PGRM may be useful for identifying cross-ancestry associations, which prior research suggests aremore likely to be causal.(50) A full vetting of the cross-ancestry replicability is beyond the scope of this paper, but these results shows that the PGRM can be useful in exploring this important topic furthe…”
BackgroundIntegration of evidence across human and model organism studies: A meeting report
Palmer, R. H. C., Johnson, E. C., Won, H., Polimanti, R. A., Kapoor, M., Chitre, A. S., Bogue, M. A., Benca‐Bachman, C. E., Parker, C. C., Verma, A., et al. (2021) · Genes Brain & Behavior · doi:10.1111/gbb.12738 · Cited by 17
“In short, recent insights from past studies highlight how gaps in our understanding could be addressed using large and genetically diverse samples (is being achieved for nicotine and alcohol, but not other substances), better phenotyping, new computational methods, and long-read sequencing technologies to capture and…”
BackgroundSelf-citationA comprehensive evaluation of polygenic score methods across cohorts in psychiatric disordersPreprint
Ni, G.-Y., Zeng, J., Revez, J., Wang, Y., Ge, T., Restaudi, R., Kiewa, J., Nyholt, D., Coleman, J., Smoller, J., et al. (2020) · medRxiv · doi:10.1101/2020.09.10.20192310 · Cited by 18
Self-citationPornpattananangkul, N., Riglin, L., Anney, R., Wang, Y., Barch, D., Thapar, A., & Stringaris, A. (2020) · medRxiv · doi:10.1101/2020.06.08.20123877 · Cited by 18
“Finally, the generalizability of the findings is limited to children of European ancestry due to the lack of summary statistics from well-powered GWASs for major psychiatric disorders done in non-European participants 57 .”
Hemispheric Asymmetry in the Genetic Overlap between Schizophrenia and White Matter Microstructure
Zhang, Y., Liu, M., Wang, S., Zhang, W., Dong, H., Qian, Q., Wu, Y., Wu, Q., Xu, J., Zhai, Y., et al. (2025) · Cyborg and Bionic Systems · doi:10.34133/cbsystems.0451 · Cited by 18
“1, residual long-range LD or subtle population stratification may still inflate test statistics, generating spurious enrichment patterns and false positives [ 95 ].”
García‐Abadillo, J., Barba, P., Carvalho, T., Sosa-Zuniga, V., Lozano, R. D., Carvalho, H. F., Garcia-Rojas, M., Salazar, E., & y Sánchez, J. I. (2023) · Horticulture Research · doi:10.1093/hr/uhad283 · Cited by 21
“Exploring trait genetic architecture requires diverse individuals representing existing trait variability [50].”
BackgroundStein, M. B., Jain, S., Parodi, L., Choi, K. W., Maihofer, A. X., Nelson, L. D., Mukherjee, P., Sun, X., He, F., Okonkwo, D. O., et al. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02313-9 · Cited by 20
“PRS analyses were conducted only in the European ancestry subsamples because of the unavailability of reference GWAS data for other populations [30, 31].”
MethodGenomic and neuroimaging approaches to bipolar disorder
Kohshour, M. O., Papiol, S., Ching, C. R. K., & Schulze, T. G. (2022) · BJPsych Open · doi:10.1192/bjo.2021.1082 · Cited by 28
Shared genetic influences on depression and menopause symptoms
Meijsen, J. J., Shen, H., Vemuri, M., Rasgon, N., Koenen, K. C., & Duncan, L. E. (2021) · Psychological Medicine · doi:10.1017/s0033291721004037 · Cited by 19
“Consistent with current best practices for the analysis of multi-ancestry data (Peterson et al., 2019), we then extracted unrelated individuals of European (n = 187 465), South-Asian (n = 3713), African (n = 3582), and East-Asian (n = 1190) ancestry for ancestry-specific GWAS.”
BackgroundSelf-citationHow does genetic variation modify ND-CNV phenotypes?
Dinneen, T. J., Ghrálaigh, F. N., Walsh, R., Lopez, L. M., & Gallagher, L. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.07.006 · Cited by 22
Wen, S., Kuri-Morales, P. A., Hu, F., Nag, A., Tachmazidou, I., Deevi, S. V. V., Taiy, H., Smith, K. R., Loesch, D. P., Burren, O. S., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02085-6 · Cited by 18
“The study of non-European cohorts provides opportunities for novel discovery and orthogonal validation of risk variants and loci and for improving our understanding of disease etiology, while ensuring that the benefits of genomic research are broadly applicable 2 .”
Lam, W. K., Lau, C. S., Luk, H., Au, L. W. C., Chan, G. C. P., Chan, Y., Cheng, S. S. W., Cheng, T. H. T., Cheung, L. L., Cheung, Y., et al. (2025) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2025.101473 · Cited by 10
“1,4,43,44 The identi fi cation of unique and prevalent variants from Southeast Asia in this study contributes to genomic diversity by increasing the inclusiveness of Chinese genomic data.”
Park, J. K., & Lu, C. Y. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13040573 · Cited by 23
“Accumulating functional evidence regarding variant–disease relationships may also improve the clinical utility of PGS, as well as their accuracy in diverse genetic ancestries [68].”
BackgroundGenome-Wide Associations and Confirmatory Meta-Analyses in Diabetic Retinopathy
Yu, X.-T., & Rong, S.-S. (2023) · Genes · doi:10.3390/genes14030653 · Cited by 16
“Additionally, the standardization of SNP definitions and frequency databases can improve the accuracy of genetic association analyses [67].”
BackgroundAyorech, Z., Baldwin, J. R., Pingault, J., Rimfeld, K., & Plomin, R. J. (2023) · Scientific Reports · doi:10.1038/s41598-022-25374-0 · Cited by 22
Exome/Genome-Wide Testing in Newborn Screening: A Proportionate Path Forward
Rahimzadeh, V. N., Friedman, J. M., de Wert, G. M. W. R., & Knoppers, B. M. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.865400 · Cited by 19
“Variant interpretation upon which positive predictive values for ES/GS are measured has been established almost exclusively from individuals of European descent (Popejoy and Fullerton, 2016; Peterson et al., 2019).”
BackgroundMagwai, T., & Xulu, K. R. (2022) · Genes · doi:10.3390/genes13020300 · Cited by 20
Mid-pass whole genome sequencing enables biomedical genetic studies of diverse populations
Emde, A., Phipps‐Green, A. J., Cadzow, M. J., Gallagher, C. S., Major, T. J., Merriman, M. E., Topless, R., Takei, R., Dalbeth, N., Murphy, R., et al. (2021) · BMC Genomics · doi:10.1186/s12864-021-07949-9 · Cited by 22
“genome-wide datasets to be dramatically reduced [12].”
González, S. R., Strizich, G., Isasi, C. R., Hua, S., Comas, B., Sofer, T., Thyagarajan, B., Perreira, K. M., Talavera, G. A., Daviglus, M. L., et al. (2021) · Ethnicity & Disease · doi:10.18865/ed.31.4.547 · Cited by 15
Applying an equity lens to pharmacogenetic research and translation to under‐represented populations
Luczak, T., Stenehjem, D., & Brown, J. T. (2021) · Clinical and Translational Science · doi:10.1111/cts.13110 · Cited by 16
Biological insights into schizophrenia from ancestrally diverse populations
Bigdeli, T. B., Chatzinakos, C., Bendl, J. M., Barr, P. B., Venkatesh, S., Gorman, B. R., Clarence, T., Genovese, G., Iyegbe, C. O., Peterson, R. E., et al. (2026) · Nature · doi:10.1038/s41586-025-10000-6 · Cited by 21
Self-citationEvaluating NetMHCpan performance on non-European HLA alleles not present in training data
Atkins, T. K., Solanki, A., Vasmatzis, G., Cornette, J. L., & Riedel, M. D. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1288105 · Cited by 16
Rizig, M., Bandres-Ciga, S., Makarious, M., Ojo, O., Crea, P. W., Abiodun, O., Levine, K., Abubakar, S., Achoru, C., Vitale, D., et al. (2023) · medRxiv · doi:10.1101/2023.05.05.23289529 · Cited by 15
“…the Broad Institute's joint discovery pipeline and retained only the high-quality variants flagged as “PASS” after variant quality score recalibration, with a call rate > 0.95, genotype quality >20, read depth >5, and heterozygous allele balance between 0.25 and 0.75 as described previously 38 .”
InfluentialAhern, J., Thompson, W. K., Fan, C. C., & Loughnan, R. J. (2023) · Behavior Genetics · doi:10.1007/s10519-023-10139-w · Cited by 19
Advancement in Human Face Prediction Using DNA
Alshehhi, A., Almarzooqi, A., Alhammadi, K., Werghi, N., Tay, G. K., & AlSafar, H. S. (2023) · Genes · doi:10.3390/genes14010136 · Cited by 18
“Studying samples from under-represented population groups can improve our knowledge of genetic structures and extend the applicability of forensic and medical findings [31].”
BackgroundThe next 10 years of behavioural genomic research
Plomin, R. J. (2022) · JCPP Advances · doi:10.1002/jcv2.12112 · Cited by 19
“Another direction for research during the next 10 years is to create more predictive polygenic scores by moving beyond Eurocentric samples to more diverse samples (Peterson et al., 2019).”
BackgroundKuo, S. I., Poore, H. E., Barr, P. B., Chirico, I. S., Alıev, F., Bucholz, K. K., Chan, G., Kamarajan, C., Kramer, J., McCutcheon, V. V., et al. (2022) · Development and Psychopathology · doi:10.1017/s0954579422000700 · Cited by 23
“…limitation of this approach, particularly in view of prior evidence that PGS are most predictive when individuals in the discovery GWAS and the target sample are matched on ancestral background (Martin et al., 2017; Peterson et al., 2019); the Discussion section further elaborates on these issues.”
BackgroundMacDermod, C. M., Pettie, M. A., Carrino, E. A., Garcia, S. C., Padalecki, S., Finch, J. E., Sanzari, C. M., Kennedy, H., Pawar, P. S., Mcgough, M. M., et al. (2022) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32906 · Cited by 19
“Underrepresentation of non-European ancestry populations in psychiatric genetics research is well documented and a major challenge for the field (Duncan et al., 2019; Peterson et al., 2019); although, the down-stream implications on health disparities are underappreciated.”
BackgroundHarrison, H., Li, N., Saunders, C. L., Rossi, S., Dennis, J., Griffin, S. J., Stewart, G. D., & Usher-Smith, J. (2022) · BJU International · doi:10.1111/bju.15752 · Cited by 15
Sequencing-based genome-wide association studies reporting standards
McMahon, A., Lewis, E., Buniello, A., Cerezo, M., Hall, P., Sollis, E., Parkinson, H., Hindorff, L. A., Harris, L. W., & MacArthur, J. A. L. (2021) · Cell Genomics · doi:10.1016/j.xgen.2021.100005 · Cited by 28
“30 31–33 The inclusion of diverse ancestral backgrounds in genomics studies is recognized as important, 34,35 but analysis of array-based GWASs has highlighted the extreme bias toward samples of European origin.”
The practical utility of genetic screening in school settings
Shero, J. A., van Dijk, W., Edwards, A. A., Schatschneider, C. W., Solari, E. J., & Hart, S. A. (2021) · npj Science of Learning · doi:10.1038/s41539-021-00090-y · Cited by 17
Self-citationTowards a consensus roadmap for a new diagnostic framework for mental disorders
Kas, M. J., Hyman, S. E., Williams, L. M., Hidalgo‐Mazzei, D., Huys, Q. J. M., Hotopf, M. H., Cuthbert, B. N., Lewis, C. M., Picker, L. D., Lalousis, P. A., et al. (2024) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2024.08.515 · Cited by 14
“Despite this progress, much further work is needed in population of diverse ances-tries to ensure that genetic contributions are captured worldwide (Peterson et al., 2019).”
BackgroundPolygenic scores and their applications in kidney disease
Khan, A., & Kiryluk, K. (2024) · Nature Reviews Nephrology · doi:10.1038/s41581-024-00886-2 · Cited by 14
“Therefore, ancestry and relatedness must be carefully considered and addressed in GWAS analyses 13,14 .”
Tian, J., Zhang, M., Zhang, F., Gao, K., Lu, Z., Cai, Y., Chen, C., Ning, C., Li, Y. X., Qian, S., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01355-y · Cited by 12
“As existing GWASs were predominantly conducted in European populations [33, 34], the poor transferability of PRS across populations has impeded its clinical implementation and raised health disparity concerns.”
BackgroundSelf-citationInborn Errors of Immunity in Hidradenitis Suppurativa Pathogenesis and Disease Burden
Colvin, A., & Petukhova, L. (2023) · Journal of Clinical Immunology · doi:10.1007/s10875-023-01518-3 · Cited by 17
Mueller, S. H., Lai, A. G., Valkovskaya, M., Michailidou, K., Bolla, M. K., Wang, Q., Dennis, J., Lush, M. J., Abu-Ful, Z., Ahearn, T. U., et al. (2023) · Genome Medicine · doi:10.1186/s13073-022-01152-5 · Cited by 16
“The inclusion of samples of non-European ancestry in genetic studies can advance our understanding of genetic disease landscapes [8].”
BackgroundSelf-citationFragoso-Bargas, N., Elliott, H. R., Lee-Ødegård, S., Opsahl, J. O., Sletner, L., Jenum, A. K., Drevon, C. A., Qvigstad, E., Moen, G.-H., Birkeland, K. I., et al. (2022) · Diabetes · doi:10.2337/db22-0504 · Cited by 18
“Although genetic variants can have differential effects across populations (33), it has been suggested that genetic variants associated with complex traits in more than one ancestry tend to have consistent direction of effects (34,35) and are more likely to be causal or in linkage disequilibrium with causal variants (…”
BackgroundSystematic review of genome-wide association studies of anxiety disorders and neuroticism
van der Walt, K., Campbell, M. L., Stein, D. J., & Dalvie, S. (2022) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2022.2099970 · Cited by 16
“…raise concern over the persistent lack of ancestral diversity in GWAS, which limits the accuracy and broader relevance of findings, and threatens to exacerbate existing healthcare disparities as genomic medicine advances (Need and Goldstein 2009; Popejoy and Fullerton 2016; Peterson et al. 2019).”
Background
Metadata
- Authors
- Peterson, R. E., Kuchenbaecker, K., Walters, R. K., Chen, C.-Y., Popejoy, A. B., Periyasamy, S., Lam, M., Iyegbe, C., Strawbridge, R. J., Brick, L., Carey, C. E., Martin, A. R., Meyers, J. L., Su, J., Chen, J., Edwards, A. C., Kalungi, A., Koen, N., Majara, L., Schwarz, E., Smoller, J. W., Stahl, E. A., Sullivan, P. F., Vassos, E., Mowry, B., Prieto, M. L., Cuellar-Barboza, A., Bigdeli, T. B., Edenberg, H. J., Huang, H., & Duncan, L. E.
- Affiliations
- Virginia Commonwealth University (US)
- University College London (GB)
- Broad Institute (US)
- Stanford Medicine (US)
- The University of Queensland (AU)
- King's College London (GB)
- Karolinska Institutet (SE)
- Brown University (US)
- SUNY Downstate Health Sciences University (US)
- Arizona State University (US)
- Heidelberg University (DE)
- Massachusetts General Hospital (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of North Carolina at Chapel Hill (US)
- Mayo Clinic (US)
- Indiana University School of Medicine (US)
- Corresponding author
- Peterson, Roseann E.
- ORCID
- Peterson, Roseann E.
- Kuchenbaecker, Karoline
- Walters, Raymond K.
- Chen, Chia-Yen
- Popejoy, Alice B.
- Periyasamy, Sathish
- Lam, Max
- Iyegbe, Conrad
- Strawbridge, Rona J.
- Brick, Leslie
- Carey, Caitlin E.
- Martin, Alicia R.
- Meyers, Jacquelyn L.
- Su, Jinni
- Chen, Junfang
- Edwards, Alexis C.
- Kalungi, Allan
- Koen, Nastassja
- Majara, Lerato
- Schwarz, Emanuel
- Smoller, Jordan W.
- Stahl, Eli A.
- Sullivan, Patrick F.
- Vassos, Evangelos
- Mowry, Bryan
- Prieto, Miguel L.
- Bigdeli, Tim B.
- Edenberg, Howard J.
- Huang, Hailiang
- Duncan, Laramie E.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 179
- Issue
- 3
- Pages
- 589-603
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6939869
- OpenAlex
- W2970480306
- Cited by
- 797
- References
- 89
- Citations by year
- 2026: 110 · 2025: 134 · 2024: 153 · 2023: 158 · 2022: 117 · 2021: 82 · 2020: 41 · 2019: 2
- FWCI
- 8.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, other-oa)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- SUNY Digital Repository Support (State University of New York System) (submittedVersion, cc-by-nc-nd)
- IUScholarWorks (Indiana University) (submittedVersion)
- Funders
- Vetenskapsrådet (D0886501)
- UK Research and Innovation (MR/S003061/1, K99MH117229)
- National Alliance for Research on Schizophrenia and Depression (22604)
- National Institutes of Health (K01 MH113848, MH109532, MH109536, MH109539, U01 MH109528, MH109501, MH109514, MH109499)
- European Commission
- Horizon 2020
- Royal Irish Academy (610307)
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University
- Wellcome Trust (212360/Z/18/Z, 5U01MH109539)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Social Sciences › Anthropology › Race, Genetics, and Society
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- genome-wide association study, population diversity, admixed populations, genetic ancestry, genetic architecture, methodological considerations, underrepresented groups, genetic research ethics
- MeSH terms
- Data Accuracy, Human Genetics, Genetics, Population, Humans, Pedigree, Genetic Variation, Genome-Wide Association Study, Genotyping Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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