Browsing by Author "Zhao, Hongyu"
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Item An epigenetic clock for gestational age based on human umbilical vein endothelial cells from a diverse population of newborns(Research Square, 2023-06-30) Peng, Gang; Sosnowski, David W.; Murphy, Susan K.; Johnson, Sara B.; Skaar, David; Schleif, William S.; Hernandez, Raquel G.; Monforte, Hector; Zhao, Hongyu; Hoyo, Cathrine; Medical and Molecular Genetics, School of MedicineBackground: Epigenetic clocks are emerging as a useful tool in many areas of research. Many epigenetic clocks have been developed for adults; however, there are fewer clocks focused on newborns and most are trained using blood from European ancestry populations. In this study, we built an epigenetic clock based on primary human umbilical vein endothelial cells from a racially and ethnically diverse population. Results: Using human umbilical vein endothelial cell [HUVEC]-derived DNA, we calculated epigenetic gestational age using 83 CpG sites selected through elastic net regression. In this study with newborns from different racial/ethnic identities, epigenetic gestational age and clinical gestational age were more highly correlated (r = 0.85), than epigenetic clocks built from adult and other pediatric populations. The correlation was also higher than clocks based on blood samples from newborns with European ancestry. We also found that birth weight was positively associated with epigenetic gestational age acceleration (EGAA), while NICU admission was associated with lower EGAA. Newborns self-identified as Hispanic or non-Hispanic Black had lower EGAA than self-identified as non-Hispanic White. Conclusions: Epigenetic gestational age can be used to estimate clinical gestational age and may help index neonatal development. Caution should be exercised when using epigenetic clocks built from adults with children, especially newborns. We highlight the importance of cell type-specific epigenetic clocks and general pan tissue epigenetic clocks derived from a large racially and ethnically diverse population.Item Association of Epigenetic Age Acceleration With Risk Factors, Survival, and Quality of Life in Patients With Head and Neck Cancer(Elsevier, 2021) Xiao, Canhua; Miller, Andrew H.; Peng, Gang; Levine, Morgan E.; Conneely, Karen N.; Zhao, Hongyu; Eldridge, Ronald C.; Wommack, Evanthia C.; Jeon, Sangchoon; Higgins, Kristin A.; Shin, Dong M.; Saba, Nabil F.; Smith, Alicia K.; Burtness, Barbara; Park, Henry S.; Irwin, Melinda L.; Ferrucci, Leah M.; Ulrich, Bryan; Qian, David C.; Beitler, Jonathan J.; Bruner, Deborah W.; Medical and Molecular Genetics, School of MedicinePurpose: Epigenetic age acceleration (EAA) is robustly linked with mortality and morbidity. This study examined risk factors of EAA and its association with overall survival (OS), progression-free survival (PFS), and quality of life (QOL) in patients with head and neck cancer (HNC) receiving radiation therapy. Methods and materials: Patients without distant metastasis were enrolled and followed before and at the end of radiation therapy and at 6 and 12 months after radiation therapy. EAA was calculated with DNAmPhenoAge at all 4 time points. Risk factors included demographic characteristics, lifestyle, clinical characteristics, treatment-related symptoms, and blood biomarkers. Survival data were collected until August 2020, and QOL was measured using Functional Assessment of Cancer Therapy-HNC. Results: Increased comorbidity, symptoms unrelated to human papilloma virus, and more severe treatment-related symptoms were associated with higher EAA (P = .03 to P < .001). A nonlinear association (quadratic) between body mass index (BMI) and EAA was observed: decreased BMI (<35 kg/m2; P = .04) and increased BMI (≥35 kg/m2; P = .01) were linked to higher EAA. Increased EAA (per year) was associated with worse OS (hazard ratio [HR], 1.11 [95% confidence interval {CI}, 1.03-1.18; P = .004]; HR, 1.10 [95% CI, 1.01-1.19; P = .02] for EAA at 6 and 12 months after treatment, respectively) and PFS (HR, 1.10 [95% CI, 1.02-1.19; P = .02]; HR, 1.14 [95% CI, 1.06-1.23; P < .001]; and HR, 1.08 [95% CI, 1.02-1.14; P = .01]) for EAA before, immediately after, and 6 months after radiation therapy, respectively) and QOL over time (β = -0.61; P = .001). An average of 3.25 to 3.33 years of age acceleration across time, which was responsible for 33% to 44% higher HRs of OS and PFS, was observed in those who died or developed recurrence compared with those who did not (all P < .001). Conclusions: Compared with demographic and lifestyle factors, clinical characteristics were more likely to contribute to faster biological aging in patients with HNC. Acceleration in epigenetic age resulted in more aggressive adverse events, including OS and PFS. EAA could be considered as a marker for cancer outcomes, and decelerating aging could improve survival and QOL.Item Association of Maternal Age and Blood Markers for Metabolic Disease in Newborns(MDPI, 2023-12-20) Xie, Yuhan; Peng, Gang; Zhao, Hongyu; Scharfe, Curt; Medical and Molecular Genetics, School of MedicinePregnancy at an advanced maternal age is considered a risk factor for adverse maternal, fetal, and neonatal outcomes. Here we investigated whether maternal age could be associated with differences in the blood levels of newborn screening (NBS) markers for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP). Population-level NBS data from screen-negative singleton infants were examined, which included blood metabolic markers and covariates such as age at blood collection, birth weight, gestational age, infant sex, parent-reported ethnicity, and maternal age at delivery. Marker levels were compared between maternal age groups (age range: 1544 years) using effect size analyses, which controlled for differences in group sizes and potential confounding from other covariates. We found that 13% of the markers had maternal age-related differences, including newborn metabolites with either increased (Tetradecanoylcarnitine [C14], Palmitoylcarnitine [C16], Stearoylcarnitine [C18], Oleoylcarnitine [C18:1], Malonylcarnitine [C3DC]) or decreased (3-Hydroxyisovalerylcarnitine [C5OH]) levels at an advanced maternal age (≥35 years, absolute Cohen’s d > 0.2). The increased C3DC levels in this group correlated with a higher false-positive rate in newborn screening for malonic acidemia (p-value < 0.001), while no significant difference in screening performance was seen for the other markers. Maternal age is associated with inborn metabolic differences and should be considered together with other clinical variables in genetic disease screening.Item Correlating genomic copy number alterations with clinicopathologic findings in 75 cases of hepatocellular carcinoma(Springer Nature, 2021-06-08) Peng, Gang; Chai, Hongyan; Ji, Weizhen; Lu, Yufei; Wu, Shengming; Zhao, Hongyu; Li, Peining; Hu, Qiping; Medical and Molecular Genetics, School of MedicineBackground: Oligonucleotide array comparative genomic hybridization (aCGH) analysis has been used for detecting somatic copy number alterations (CNAs) in various types of tumors. This study aimed to assess the clinical utility of aCGH for cases of hepatocellular carcinoma (HCC) and to evaluate the correlation between CNAs and clinicopathologic findings. Methods: aCGH was performed on 75 HCC cases with paired DNA samples from tumor and adjacent nontumor tissues. Survival outcomes from these cases were analyzed based on Barcelona-Clinic Liver Cancer Stage (BCLC), Edmondson-Steiner grade (E-S), and recurrence status. Correlation of CNAs with clinicopathologic findings was analyzed by Wilcoxon rank test and clustering vs. K means. Results: The survival outcomes indicated that BCLC stages and recurrence status could be predictors and E-S grades could be a modifier for HCC. The most common CNAs involved gains of 1q and 8q and a loss of 16q (50%), losses of 4q and 17p and a gain of 5p (40%), and losses of 8p and 13q (30%). Analyses of genomic profiles and clusters identified that losses of 4q13.2q35.2 and 10q22.3q26.13 seen in cases of stage A, grade III and nonrecurrence were likely correlated with good survival, while loss of 1p36.31p22.1 and gains of 2q11.2q21.2 and 20p13p11.1 seen in cases of stage C, grade III and recurrence were possibly correlated with worst prognosis. Conclusions: These results indicated that aCGH analysis could be used to detect recurrent CNAs and involved key genes and pathways in patients with HCC. Further analysis on a large case series to validate the correlation of CNAs with clinicopathologic findings of HCC could provide information to interpret CNAs and predict prognosis.Item dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening(MDPI, 2022-08-29) Peng, Gang; Zhang, Yunxuan; Zhao, Hongyu; Scharfe, Curt; Medical and Molecular Genetics, School of MedicineThe Recommended Uniform Screening Panel (RUSP) contains more than forty metabolic disorders recommended for inclusion in universal newborn screening (NBS). Tandem-mass-spectrometry-based screening of metabolic analytes in dried blood spot samples identifies most affected newborns, along with a number of false positive results. Due to their influence on blood metabolite levels, continuous and categorical covariates such as gestational age, birth weight, age at blood collection, sex, parent-reported ethnicity, and parenteral nutrition status have been shown to reduce the accuracy of screening. Here, we developed a database and web-based tools (dbRUSP) for the analysis of 41 NBS metabolites and six variables for a cohort of 500,539 screen-negative newborns reported by the California NBS program. The interactive database, built using the R shiny package, contains separate modules to study the influence of single variables and joint effects of multiple variables on metabolite levels. Users can input an individual's variables to obtain metabolite level reference ranges and utilize dbRUSP to select new candidate markers for the detection of metabolic conditions. The open-source format facilitates the development of data mining algorithms that incorporate the influence of covariates on metabolism to increase accuracy in genetic disease screening.Item Estimation on risk of spontaneous abortions by genomic disorders from a meta‐analysis of microarray results on large case series of pregnancy losses(Wiley, 2023) Peng, Gang; Zhou, Qinghua; Chai, Hongyan; Wen, Jiadi; Zhao, Hongyu; Taylor, Hugh S.; Jiang, Yong-Hui; Li, Peining; Medical and Molecular Genetics, School of MedicineA meta-analysis on seven large case series (>1000 cases) of chromosome microarray analysis (CMA) on products of conceptions (POC) evaluated the diagnostic yields of genomic disorders and syndromic pathogenic copy number variants (pCNVs) from a collection of 35,130 POC cases. CMA detected chromosomal abnormalities and pCNVs in approximately 50% and 2.5% of cases, respectively. The genomic disorders and syndromic pCNVs accounted for 31% of the detected pCNVs, and their incidences in POC ranged from 1/750 to 1/12,000. The newborn incidences of these genomic disorders and syndromic pCNVs were estimated in a range of 1/4000 to 1/50,000 live births from population genetic studies and diagnostic yields of a large case series of 32,587 pediatric patients. The risk of spontaneous abortion (SAB) for DiGeorge syndrome (DGS), Wolf-Hirschhorn syndrome (WHS), and William-Beuren syndrome (WBS) was 42%, 33%, and 21%, respectively. The estimated overall risk of SAB for major genomic disorders and syndromic pCNVs was approximately 38%, which was significantly lower than the 94% overall risk of SAB for chromosomal abnormalities. Further classification on levels of risk of SAB to high (>75%), intermediate (51%-75%), and low (26%-50%) for known chromosomal abnormalities, genomic disorders, and syndromic pCNVs could provide evidence-based interpretation in prenatal diagnosis and genetic counseling.Item Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder(Springer Nature, 2024) Nievergelt, Caroline M.; Maihofer, Adam X.; Atkinson, Elizabeth G.; Chen, Chia-Yen; Choi, Karmel W.; Coleman, Jonathan R. I.; Daskalakis, Nikolaos P.; Duncan, Laramie E.; Polimanti, Renato; Aaronson, Cindy; Amstadter, Ananda B.; Andersen, Soren B.; Andreassen, Ole A.; Arbisi, Paul A.; Ashley-Koch, Allison E.; Austin, S. Bryn; Avdibegoviç, Esmina; Babić, Dragan; Bacanu, Silviu-Alin; Baker, Dewleen G.; Batzler, Anthony; Beckham, Jean C.; Belangero, Sintia; Benjet, Corina; Bergner, Carisa; Bierer, Linda M.; Biernacka, Joanna M.; Bierut, Laura J.; Bisson, Jonathan I.; Boks, Marco P.; Bolger, Elizabeth A.; Brandolino, Amber; Breen, Gerome; Bressan, Rodrigo Affonseca; Bryant, Richard A.; Bustamante, Angela C.; Bybjerg-Grauholm, Jonas; Bækvad-Hansen, Marie; Børglum, Anders D.; Børte, Sigrid; Cahn, Leah; Calabrese, Joseph R.; Caldas-de-Almeida, Jose Miguel; Chatzinakos, Chris; Cheema, Sheraz; Clouston, Sean A. P.; Colodro-Conde, Lucía; Coombes, Brandon J.; Cruz-Fuentes, Carlos S.; Dale, Anders M.; Dalvie, Shareefa; Davis, Lea K.; Deckert, Jürgen; Delahanty, Douglas L.; Dennis, Michelle F.; Desarnaud, Frank; DiPietro, Christopher P.; Disner, Seth G.; Docherty, Anna R.; Domschke, Katharina; Dyb, Grete; Džubur Kulenović, Alma; Edenberg, Howard J.; Evans, Alexandra; Fabbri, Chiara; Fani, Negar; Farrer, Lindsay A.; Feder, Adriana; Feeny, Norah C.; Flory, Janine D.; Forbes, David; Franz, Carol E.; Galea, Sandro; Garrett, Melanie E.; Gelaye, Bizu; Gelernter, Joel; Geuze, Elbert; Gillespie, Charles F.; Goleva, Slavina B.; Gordon, Scott D.; Goçi, Aferdita; Grasser, Lana Ruvolo; Guindalini, Camila; Haas, Magali; Hagenaars, Saskia; Hauser, Michael A.; Heath, Andrew C.; Hemmings, Sian M. J.; Hesselbrock, Victor; Hickie, Ian B.; Hogan, Kelleigh; Hougaard, David Michael; Huang, Hailiang; Huckins, Laura M.; Hveem, Kristian; Jakovljević, Miro; Javanbakht, Arash; Jenkins, Gregory D.; Johnson, Jessica; Jones, Ian; Jovanovic, Tanja; Karstoft, Karen-Inge; Kaufman, Milissa L.; Kennedy, James L.; Kessler, Ronald C.; Khan, Alaptagin; Kimbrel, Nathan A.; King, Anthony P.; Koen, Nastassja; Kotov, Roman; Kranzler, Henry R.; Krebs, Kristi; Kremen, William S.; Kuan, Pei-Fen; Lawford, Bruce R.; Lebois, Lauren A. M.; Lehto, Kelli; Levey, Daniel F.; Lewis, Catrin; Liberzon, Israel; Linnstaedt, Sarah D.; Logue, Mark W.; Lori, Adriana; Lu, Yi; Luft, Benjamin J.; Lupto, Michelle K.; Luykx, Jurjen J.; Makotkine, Iouri; Maples-Keller, Jessica L.; Marchese, Shelby; Marmar, Charles; Martin, Nicholas G.; Martínez-Levy, Gabriela A.; McAloney, Kerrie; McFarlane, Alexander; McLaughlin, Katie A.; McLean, Samuel A.; Medland, Sarah E.; Mehta, Divya; Meyers, Jacquelyn; Michopoulos, Vasiliki; Mikita, Elizabeth A.; Milani, Lili; Milberg, William; Miller, Mark W.; Morey, Rajendra A.; Morris, Charles Phillip; Mors, Ole; Mortensen, Preben Bo; Mufford, Mary S.; Nelson, Elliot C.; Nordentoft, Merete; Norman, Sonya B.; Nugent, Nicole R.; O'Donnell, Meaghan; Orcutt, Holly K.; Pan, Pedro M.; Panizzon, Matthew S.; Pathak, Gita A.; Peters, Edward S.; Peterson, Alan L.; Peverill, Matthew; Pietrzak, Robert H.; Polusny, Melissa A.; Porjesz, Bernice; Powers, Abigail; Qin, Xue-Jun; Ratanatharathorn, Andrew; Risbrough, Victoria B.; Roberts, Andrea L.; Rothbaum, Alex O.; Rothbaum, Barbara O.; Roy-Byrne, Peter; Ruggiero, Kenneth J.; Rung, Ariane; Runz, Heiko; Rutten, Bart P. F.; Saenz de Viteri, Stacey; Salum, Giovanni Abrahão; Sampson, Laura; Sanchez, Sixto E.; Santoro, Marcos; Seah, Carina; Seedat, Soraya; Seng, Julia S.; Shabalin, Andrey; Sheerin, Christina M.; Silove, Derrick; Smith, Alicia K.; Smoller, Jordan W.; Sponheim, Scott R.; Stein, Dan J.; Stensland, Synne; Stevens, Jennifer S.; Sumner, Jennifer A.; Teicher, Martin H.; Thompson, Wesley K.; Tiwari, Arun K.; Trapido, Edward; Uddin, Monica; Ursano, Robert J.; Valdimarsdóttir, Unnur; Van Hooff, Miranda; Vermetten, Eric; Vinkers, Christiaan H.; Voisey, Joanne; Wang, Yunpeng; Wang, Zhewu; Waszczuk, Monika; Weber, Heike; Wendt, Frank R.; Werge, Thomas; Williams, Michelle A.; Williamson, Douglas E.; Winsvold, Bendik S.; Winternitz, Sherry; Wolf, Christiane; Wolf, Erika J.; Xia, Yan; Xiong, Ying; Yehuda, Rachel; Young, Keith A.; Young, Ross McD.; Zai, Clement C.; Zai, Gwyneth C.; Zervas, Mark; Zhao, Hongyu; Zoellner, Lori A.; Zwart, John-Anker; deRoon-Cassini, Terri; van Rooij, Sanne J. H.; van den Heuvel, Leigh L.; AURORA Study; Estonian Biobank Research Team; FinnGen Investigators; HUNT All-In Psychiatry; Stein, Murray B.; Ressler, Kerry J.; Koenen, Karestan C.; Biochemistry and Molecular Biology, School of MedicinePost-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.Item Metabolic diversity in human populations and correlation with genetic and ancestral geographic distances(Elsevier, 2022) Peng, Gang; Pakstis, Andrew J.; Gandotra, Neeru; Cowan, Tina M.; Zhao, Hongyu; Kidd, Kenneth K.; Scharfe, Curt; Medical and Molecular Genetics, School of MedicineDNA polymorphic markers and self-defined ethnicity groupings are used to group individuals with shared ancient geographic ancestry. Here we studied whether ancestral relationships between individuals could be identified from metabolic screening data reported by the California newborn screening (NBS) program. NBS data includes 41 blood metabolites measured by tandem mass spectrometry from singleton babies in 17 parent-reported ethnicity groupings. Ethnicity-associated differences identified for 71% of NBS metabolites (29 of 41, Cohen's d > 0.5) showed larger differences in blood levels of acylcarnitines than of amino acids (P < 1e-4). A metabolic distance measure, developed to compare ethnic groupings based on metabolic differences, showed low positive correlation with genetic and ancient geographic distances between the groups' ancestral world populations. Several outlier group pairs were identified with larger genetic and smaller metabolic distances (Black versus White) or with smaller genetic and larger metabolic distances (Chinese versus Japanese) indicating the influence of genetic and of environmental factors on metabolism. Using machine learning, comparison of metabolic profiles between all pairs of ethnic groupings distinguished individuals with larger genetic distance (Black versus Chinese, AUC = 0.96), while genetically more similar individuals could not be separated metabolically (Hispanic versus Native American, AUC = 0.51). Additionally, we identified metabolites informative for inferring metabolic ancestry in individuals from genetically similar populations, which included biomarkers for inborn metabolic disorders (C10:1, C12:1, C3, C5OH, Leucine-Isoleucine). This work sheds new light on metabolic differences in healthy newborns in diverse populations, which could have implications for improving genetic disease screening.Item Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals(Springer Nature, 2023) Zhou, Hang; Kember, Rachel L.; Deak, Joseph D.; Xu, Heng; Toikumo, Sylvanus; Yuan, Kai; Lind, Penelope A.; Farajzadeh, Leila; Wang, Lu; Hatoum, Alexander S.; Johnson, Jessica; Lee, Hyunjoon; Mallard, Travis T.; Xu, Jiayi; Johnston, Keira J. A.; Johnson, Emma C.; Galimberti, Marco; Dao, Cecilia; Levey, Daniel F.; Overstreet, Cassie; Byrne, Enda M.; Gillespie, Nathan A.; Gordon, Scott; Hickie, Ian B.; Whitfield, John B.; Xu, Ke; Zhao, Hongyu; Huckins, Laura M.; Davis, Lea K.; Sanchez-Roige, Sandra; Madden, Pamela A. F.; Heath, Andrew C.; Medland, Sarah E.; Martin, Nicholas G.; Ge, Tian; Smoller, Jordan W.; Hougaard, David M.; Børglum, Anders D.; Demontis, Ditte; Krystal, John H.; Gaziano, J. Michael; Edenberg, Howard J.; Agrawal, Arpana; Million Veteran Program; Justice, Amy C.; Stein, Murray B.; Kranzler, Henry R.; Gelernter, Joel; Biochemistry and Molecular Biology, School of MedicineProblematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.Item Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression Among Adults in the US Veterans Affairs Health Care System(American Medical Association, 2022-09-14) Bigdeli, Tim B.; Voloudakis, Georgios; Barr, Peter B.; Gorman, Bryan R.; Genovese, Giulio; Peterson, Roseann E.; Burstein, David E.; Velicu, Vlad I.; Li, Yuli; Gupta, Rishab; Mattheisen, Manuel; Tomasi, Simone; Rajeevan, Nallakkandi; Sayward, Frederick; Radhakrishnan, Krishnan; Natarajan, Sundar; Malhotra, Anil K.; Shi, Yunling; Zhao, Hongyu; Kosten, Thomas R.; Concato, John; O'Leary, Timothy J.; Przygodzki, Ronald; Gleason, Theresa; Pyarajan, Saiju; Brophy, Mary; Huang, Grant D.; Muralidhar, Sumitra; Gaziano, J. Michael; Aslan, Mihaela; Fanous, Ayman H.; Harvey, Philip D.; Roussos, Panos; Cooperative Studies Program (CSP); Million Veteran Program (MVP); Psychiatry, School of MedicineImportance: Serious mental illnesses, including schizophrenia, bipolar disorder, and depression, are heritable, highly multifactorial disorders and major causes of disability worldwide. Objective: To benchmark the penetrance of current neuropsychiatric polygenic risk scores (PRSs) in the Veterans Health Administration health care system and to explore associations between PRS and broad categories of human disease via phenome-wide association studies. Design, setting, and participants: Extensive Veterans Health Administration's electronic health records were assessed from October 1999 to January 2021, and an embedded cohort of 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder were found. The performance of schizophrenia, bipolar disorder, and major depression PRSs were compared in participants of African or European ancestry in the Million Veteran Program (approximately 400 000 individuals), and associations between PRSs and 1650 disease categories based on ICD-9/10 billing codes were explored. Last, genomic structural equation modeling was applied to derive novel PRSs indexing common and disorder-specific genetic factors. Analysis took place from January 2021 to January 2022. Main outcomes and measures: Diagnoses based on in-person structured clinical interviews were compared with ICD-9/10 billing codes. PRSs were constructed using summary statistics from genome-wide association studies of schizophrenia, bipolar disorder, and major depression. Results: Of 707 299 enrolled study participants, 459 667 were genotyped at the time of writing; 84 806 were of broadly African ancestry (mean [SD] age, 58 [12.1] years) and 314 909 were of broadly European ancestry (mean [SD] age, 66.4 [13.5] years). Among 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder, 8962 (95.6%) were correctly identified using ICD-9/10 codes (2 or more). Among those of European ancestry, PRSs were robustly associated with having received a diagnosis of schizophrenia (odds ratio [OR], 1.81 [95% CI, 1.76-1.87]; P < 10-257) or bipolar disorder (OR, 1.42 [95% CI, 1.39-1.44]; P < 10-295). Corresponding effect sizes in participants of African ancestry were considerably smaller for schizophrenia (OR, 1.35 [95% CI, 1.29-1.42]; P < 10-38) and bipolar disorder (OR, 1.16 [95% CI, 1.11-1.12]; P < 10-10). Neuropsychiatric PRSs were associated with increased risk for a range of psychiatric and physical health problems. Conclusions and relevance: Using diagnoses confirmed by in-person structured clinical interviews and current neuropsychiatric PRSs, the validity of an electronic health records-based phenotyping approach in US veterans was demonstrated, highlighting the potential of PRSs for disentangling biological and mediated pleiotropy.