About

Rachel L. Kember, M.Sc., Ph.D., is an Assistant Professor of Psychiatry at the University of Pennsylvania's Perelman School of Medicine. She is a member of the Penn Center for Global Genomics & Health Equity Research, a Health Science Specialist at the Cpl. Michael J. Crescenz Veterans Affairs Medical Center, and a Senior Fellow at the Penn Institute for Biomedical Informatics. Her research focuses on identifying the complex phenotypic and genomic interactions that lead to substance use and psychiatric diseases, utilizing computational genetics methods in electronic health record datasets linked to biobanks. Her work aims to advance understanding of the genetic underpinnings of substance use disorders and psychiatric conditions, contributing to the development of precision medicine approaches.

Research topics

• Biology
• Genetics
• Medicine
• Psychiatry
• Clinical psychology
• Evolutionary biology
• Psychology
• Internal medicine
• Computational biology
• Developmental psychology
• Environmental health
• Medical emergency
• Bioinformatics

Selected publications

• Sex and the Genome: Divergent Genetic Architecture Across the Human Lifespan

  Annual Review of Biomedical Data Science · 2026-05-19

  article

  Males and females share nearly identical genomes yet exhibit profound differences in disease susceptibility, progression, and treatment response across the lifespan. These sex differences arise from evolutionary conflicts in which genetic variants confer opposing fitness effects between sexes, maintained by balancing selection. This review examines how sex shapes complex trait architecture from early childhood through postreproductive life. Early childhood reveals intrinsic genetic sexual dimorphism before hormonal activation. During reproductive years, sex-specific metabolic programming manifests through divergent adipose distribution and cardiometabolic risk, while autoimmune diseases demonstrate a fourfold female bias driven by incomplete X-inactivation. Postreproductive phases reveal antagonistic pleiotropy, in which variants that are beneficial for early reproduction increase late-life disease vulnerability. Systematic exclusion of X chromosome data from ∼75% of genome-wide association studies has obscured critical therapeutic targets. Sex-stratified genomic analyses consistently uncover effect sizes and risk loci that are invisible in sex-combined models. We argue that precision medicine must incorporate sex as a fundamental axis of genomic stratification across development, rather than treating it as a covariate to be adjusted away.

  PublisherDOI

• Opioid use disorder and dementia risk: evidence from observational and genetic analyses in diverse ancestry cohorts

  Alzheimer s & Dementia · 2026-05-01

  articleOpen access

  INTRODUCTION: Opioid use disorder (OUD) may adversely affect brain health, but its role in dementia risk remains poorly understood. METHODS: We investigated associations between OUD and dementia using observational data from 222,518 participants (European and African ancestry) in the Million Veteran Program and Mendelian randomization (MR) using genome-wide association study summary statistics from 6,066,918 individuals. Polygenic risk score (PRS) analyses were conducted in 229 opioid-naïve Lifebrain consortium participants with longitudinal magnetic resonance imaging data. RESULTS: OUD was associated with increased risk of all-cause dementia (hazard ratio = 1.56, 95% confidence interval [CI]: 1.39 to 1.76), Alzheimer's disease, and vascular dementia. MR supported a potential causal link between genetic liability to OUD and dementia (inverse variance weighted odds ratio = 1.77, 95% CI: 1.43 to 2.19). Genetic variation in the μ-opioid receptor gene was also associated with dementia risk. No PRS associations were found with brain structure. DISCUSSION: These findings suggest a potential causal role for OUD in dementia, implicating μ-opioid receptor pathways in neurodegeneration.

  PublisherDOI

• The cell-type–specific genetic architecture of chronic pain in brain and dorsal root ganglia

  Journal of Clinical Investigation · 2025-10-07 · 1 citations

  articleOpen access

  Chronic pain is a complex clinical problem comprising multiple conditions that may share a common genetic profile. GWAS have identified many risk loci whose cell-type context remains unclear. Here, we integrated GWAS data on chronic pain with single-cell RNA-Seq (scRNA-Seq) data from human brain and dorsal root ganglia (hDRG) and single-cell chromatin accessibility data from human brain and mouse dorsal horn. Pain-associated variants were enriched in glutamatergic neurons, mainly in the prefrontal cortex, hippocampal CA1-3, and amygdala. In hDRG, the hPEP.TRPV1/A1.2 neuronal subtype showed robust enrichment. Chromatin accessibility analyses revealed variant enrichment in excitatory and inhibitory neocortical neurons in the brain and in midventral neurons and oligodendrocyte precursor cells in the mouse dorsal horn. Gene-level heritability in the brain highlighted roles for kinase activity, GABAergic synapses, axon guidance, and neuron projection development. In hDRG, implicated genes were related to glutamatergic signaling and neuronal projection. In cervical DRG of patients with acute versus chronic pain, scRNA-Seq data from neuronal or non-neuronal cells were enriched for chronic pain-associated genes (e.g., EFNB2, GABBR1, NCAM1, SCN11A). This cell-type-specific genetic architecture of chronic pain across central and PNS circuits provides a foundation for targeted translational research.

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• T23. MULTIVARIATE, MULTIOMIC ANALYSIS IN 799,429 INDIVIDUALS IDENTIFIES 134 LOCI ASSOCIATED WITH SOMATOFORM TRAITS

  European Neuropsychopharmacology · 2025-10-01

  articleOpen access

  Somatoform traits, which manifest as persistent physical symptoms without a clear medical cause, are prevalent and pose significant challenges to clinical practice. Persistent physical symptoms have complex etiologies and often co-occur with one another and with psychiatric disorders. Their co-occurrence with psychiatric disorders has led to their inclusion in empirically based models of psychopathology, including the Hierarchical Taxonomy of Psychopathology (HiTOP). Understanding the genetic basis of these traits could improve diagnostic and therapeutic approaches and inform psychiatric nosology models. With publicly available summary statistics, we conducted a multivariate genome-wide association study (GWAS) and multiomic analysis of four somatoform traits—fatigue, irritable bowel syndrome, pain intensity, and health satisfaction—in an effective sample size of 799,429 individuals genetically similar to European reference panels. Additionally, we conducted drug repurposing analyses to identify potential druggable targets and Mendelian randomization analyses to identify potential causal effects of gut microbiota abundance on somatoform traits. Using genomic structural equation modeling, GWAS identified 134 loci significantly associated with a somatoform common factor, including 44 loci not significant in the input GWAS and 8 novel loci for somatoform traits. Gene-property analyses highlighted an enrichment of genes involved in synaptic transmission and enriched gene expression in 12 brain tissues. Six genes, including members of the CD300 family, had putatively causal effects mediated by protein abundance. There was substantial polygenic overlap (76-83%) between the somatoform and externalizing, internalizing, and general psychopathology factors. Somatoform polygenic scores were associated most strongly with obesity, Type 2 diabetes, tobacco use disorder, and mood/anxiety disorders in independent biobanks. Drug repurposing analyses suggested potential therapeutic targets, including MEK inhibitors. Mendelian randomization indicated potentially protective effects of gut microbiota, including Ruminococcus bromii. Consistent with emerging medical and genetic knowledge, somatoform traits have a shared etiology and considerable polygenic overlap with psychopathology. Given the high polygenic and phenotypic overlap with psychopathology, our results support the inclusion of a proposed somatoform spectrum in the HiTOP framework. These findings underscore the need for treatment approaches that recognize the interconnectedness of physical and mental health. The biological insights from drug repurposing and Mendelian randomization analyses could provide promising avenues for treatment development.

  PublisherDOI

• 64. LEVERAGING TRANS-ANCESTRY GENOMICS TO IMPROVE PRECISION PSYCHIATRY FOR SEVERE MENTAL ILLNESS IN DIVERSE POPULATIONS

  European Neuropsychopharmacology · 2025-10-01

  articleOpen accessSenior author

  Bipolar Disorder (BD), Schizophrenia (SCZ), and Major Depressive Disorder (MDD) - collectively referred to as severe mental illness (SMI) - are disabling psychiatric conditions with complex genetic and environmental causes. Hospital-affiliated biobanks that collect patient genetic and electronic health record (EHR) data, such as the diverse U.S.-based All of Us (AoU) cohort and Penn Medicine Biobank (PMBB), provide an opportunity to examine how SMI coincides with other health conditions and environmental factors. We aim to utilize genomic, EHR, and survey data from AoU and PMBB to identify risk factors for SMI. We will then use these variables to develop an algorithm for SMI prediction that will reduce time to diagnosis and treatment initiation. Polygenic scores (PGS) are a promising tool for SMI prediction due to the large genetic component of these conditions. Within the past two years, the largest multi-ancestry genome-wide association studies (GWAS) of BD and MDD to date were published. Additionally, in early 2025 AoU doubled the number of samples with genomic data. As the first step of our project, we used the results from these GWAS to test the performance of PGS for BD, SCZ, and MDD in the new AoU data. We used PRS-CSx on the ∼414,000 individuals with genomic data in AoU v8 to calculate PGS in African (AFR; N=84,148), Admixed American (AMR; N = 79,106), East Asian (EAS; N = 10,099), European (EUR; N=234,353) Middle Eastern (MID; N = 1,545), and South Asian (SAS; N = 5,579) individuals for BD (AFR=2,470; AMR=1,315; EAS=72; EUR=5,269; MID = 21; SAS = 41), SCZ (AFR=2,052; AMR=676; EAS=39; EUR=1,573; MID = 14; SAS = 24), and MDD (AFR=6,996; AMR=5,479; EAS=374; EUR=22,075; MID = 116; SAS = 234). For each phenotype and ancestry group, we calculated three PGS: a within-ancestry PGS derived from matched ancestry summary statistics; a EUR-PGS derived from EUR summary statistics; and a trans-ancestry PGS derived from matched ancestry and EUR summary statistics. Each PRS was tested for association with its matching phenotype via logistic regression, correcting for age, sex, and the top ten within-ancestry PCs. We found that within-ancestry PGS were significantly predictive for nearly all phenotypes for AFR (OR ranges 1.04-1.18; p value range 3.12e-09 - 1.2e-03), AMR (OR range 1.04-1.09; p value range 3.9e-03 - 3.3e-02), and EUR samples (OR range 1.36-1.91; p value range 0.00 - 1.84e-93). To our knowledge, this is the first time that PGS derived from AMR SMI summary statistics have significantly predicted these phenotypes in AMR samples. The EUR-PGS were significantly predictive for all three phenotypes in all ancestry groups (OR range 1.13-3.65; p value range 0.00 - 2.07e-02), with the only exceptions being BD in EAS and MID samples, and SCZ in SAS samples. To our knowledge, this is the first time that SMI phenotypes have been significantly predicted by PGS in SAS and MID samples, although sample sizes were modest. Finally, trans-ancestry PRS had stronger associations with SMI phenotypes in AFR samples. Our analyses demonstrate the benefit of increasing diversity of patients in genetic studies of SMI. We demonstrate that PGS can modestly predict SMI certain phenotypes in ancestry groups with limited representation in biobanks (i.e. MID and SAS). Our ongoing analyses include use of machine learning algorithms to identify EHR variables that help predict SMI development, and integration of our PGS models into these algorithms to improve precision psychiatry.

  PublisherDOI

• Genotype‐by‐sex interaction analyses for alcohol use disorder across biobanks

  Alcohol Clinical and Experimental Research · 2025-09-29 · 1 citations

  articleOpen access

  Abstract Background Alcohol use and alcohol use disorder (AUD) are significant contributors to morbidity and mortality, with different prevalences between males and females. Despite the established genetic contribution to AUD, sex as a biological variable and the interplay with genetic factors in the disorder remain largely unexplored. This study aimed to address the key question as to how genetic variations interact with biological sex to influence the AUD risk. Methods We conducted genome‐wide genotype‐by‐sex (G × S) interaction analyses using multiancestry datasets from the Million Veteran Program (MVP) and UK Biobank (UKB). In total, 1,039,476 participants were analyzed, comprising 150,429 AUD cases and 889,046 controls. AUD cases were defined using ICD‐9/10 codes in the MVP and using ICD‐10 codes (field ID 41270) along with self‐reported history of alcohol addiction (field ID 20406) in the UKB. Results In single‐ancestry analyses, we identified two loci in African ancestry samples with lead single‐nucleotide polymorphisms (SNPs) rs2183020 ( p = 1.82 × 10 −8 ) and rs9304803 ( p = 4.66 × 10 −8 ), and one locus in Admixed American ancestry samples with lead SNP rs9527196 ( p = 2.83 × 10 −8 ). The cross‐ancestry meta‐analysis identified one additional locus with lead SNP rs62446539 ( p = 3.95 × 10 −8 ). The deep learning method predicted that rs9304803 has B‐cell type‐specific enhancer activity. Rs2183020 and rs9304803 exhibited expression quantitative trait locus (eQTL) effects on multiple genes across various tissues, including the brain. Further experiments in ethanol‐exposed human neurons confirmed expression changes in several of these genes. Phenome‐wide association analyses revealed significant associations between rs2183020 and weight/body mass index, and between rs9304803 and prothrombin time (measured as international normalized ratio). Conclusions We believe this is the first genome‐wide G × S study of AUD, providing novel insights into the genetic basis of sex differences in AUD and advancing our understanding of its biological underpinnings.

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• 55. DISSECTING THE ANCESTRY-SPECIFIC GENETIC ARCHITECTURE OF ALCOHOL CONSUMPTION IN LATIN AMERICANS

  European Neuropsychopharmacology · 2025-10-01

  articleOpen access

  Genome-wide association studies (GWAS) have made substantial contributions to our understanding of the genetic factors that influence alcohol consumption. However, most efforts have been made in populations of European ancestry, resulting in less representation of other populations, with Latin American (LA) populations among the least represented, comprising less than 2% of the total GWAS participants. LA populations are characterized by varying degrees of genetic admixture from Indigenous American, European, and African ancestries, which creates challenges when modeling the genetic architecture of complex traits. However, recently developed GWAS approaches, such as Tractor, that leverage local ancestry information (defined as the genetic ancestry of an individual at a particular genomic location) could help overcome this challenge. This study, led by members of the Latin American Genomics Consortium (LAGC), is a meta-analysis of GWAS studies of self-reported alcohol consumption in 465,516 individuals from cohorts based in Latin American countries and the United States (US). We also conducted a local ancestry-aware GWAS on 11,655 LA individuals using Tractor. We replicated well-known genetic associations for alcohol consumption in genes that include the ADH locus (lead variant rs1229984, p-value = 1.12e-203) and others associated with psychiatric and behavioral traits, such as CADM2. We also identified a signal in the ALDH2 locus, previously associated only in East Asian populations. Using the local ancestry-aware GWAS, we identified associations in genes previously associated with the number of drinks consumed per week (Drkwk) by the GWAS and Sequencing Consortium of Alcohol and Nicotine use consortium (GSCAN), rs1874323 (p-value = 2.5860e-08) in the MAGI1 gene, and rs6833926 (p-value = 3.00e-08) in the ARAP2 gene. We also identified potential novel associations in the SLIT3 gene (rs73805262, p-value = 9.95e-09 and rs115143510, p-value = 1.23e-08), as well as one intergenic variant (rs3929849, p-value = 2.3930e-09) among segments of African-like ancestry. We also identified associations in intergenic regions of American-like descent (rs4130378, p-value = 9.673e-09; rs536315876, p-value = 4.3640e-09; rs115675116, p-value = 4.3190e-09). Nevertheless, given the small sample size in the local ancestry-aware GWAS, these results required further replication. We also compared the association of the polygenic risk score (PRS) derived from the European population using GSCAN (PRS-EUR) data with the PRS from our large-scale meta-analysis (PRS-LA), examining the relationship with drinks consumed per week in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) cohort, identifying a heterogeneity in the transferability of the PRS across geographical LA subgroups. We identified that both the PRS-EUR and PRS-La were associated with Drkwk in individuals from Puerto Rico (PRS-EUR, p-value = 7.40e-03; PRS-LA, p-value = 1.40e-02); meanwhile, only the PRS-EUR was associated in individuals from Mexico (p-value = 7.41e-3) and Cuba (p-value = 1.31e-02), and only the PRS-LA was associated in individuals from South America (p-value = 3.23e-02) Our study contributes to current efforts to elucidate the genetic architecture of alcohol consumption in Latin American populations, implicating novel genes (such as WRN) and revealing varying performance of PRS across different geographical subgroups.

  PublisherDOI

• Etiological basis for chronic pain genetic variation in brain and dorsal root ganglia cell types

  medRxiv · 2025-07-03 · 1 citations

  preprintOpen access

  ABSTRACT Chronic pain is a complex clinical problem comprising multiple conditions that may share a common genetic profile. Genome-wide association studies (GWAS) have identified many risk loci whose cell-type context remains unclear. Here, we integrated GWAS data on chronic pain ( N = 1,235,695) with single-cell RNA sequencing (scRNA-seq) data from human brain and dorsal root ganglia (hDRG), and single-cell chromatin accessibility data from human brain and mouse dorsal horn. Pain-associated variants were enriched in glutamatergic neurons; mainly in prefrontal cortex, hippocampal CA1-3, and amygdala. In hDRG, the hPEP.TRPV1/A1.2 neuronal subtype showed robust enrichment. Chromatin accessibility analyses revealed variant enrichment in excitatory and inhibitory neocortical neurons in brain and in midventral neurons and oligodendrocyte precursor cells in the mouse dorsal horn. Gene-level heritability in the brain highlighted roles for kinase activity, GABAergic synapses, axon guidance, and neuron projection development. In hDRG, implicated genes related to glutamatergic signaling and neuronal projection. In cervical DRG of patients with acute or chronic pain ( N = 12), scRNA-seq data from neuronal or non-neuronal cells were enriched for chronic pain-associated genes (e.g., EFNB2 , GABBR1 , NCAM1 , SCN11A ). This cell-type-specific genetic architecture of chronic pain across central and peripheral nervous system circuits provides a foundation for targeted translational research.

  PublisherOA PDFDOI

• Moderation of treatment outcomes by polygenic risk for alcohol‐related traits in placebo‐controlled trials of topiramate

  Alcohol Clinical and Experimental Research · 2025-05-30 · 1 citations

  articleOpen access

  BACKGROUND: In two 12-week, randomized, placebo-controlled trials (RCTs) in individuals with alcohol use disorder (AUD), topiramate significantly reduced heavy drinking days (HDDs), and alcohol-related problems. In a secondary analysis of those findings, we examined four broad measures of genetic risk-polygenic scores (PGS)-of problematic alcohol use (PAU), drinks per week (DPW), and time to relapse to any drinking (TR) and heavy drinking (THR) as moderators of topiramate's effect on HDDs and alcohol-related problems. METHODS: We analyzed data from 285 individuals with AUD (65.6% male) of European-like ancestry, who were treated with either topiramate (49.1%) or placebo (50.9%). All patients underwent genome-wide array genotyping, and PGS were calculated using summary statistics from genome-wide association studies of PAU, DPW, and TR and THR (two time-to-event outcomes among patients treated in AUD pharmacotherapy trials). We hypothesized an interaction effect in which greater genetic risk-particularly for PAU-would be associated with a greater therapeutic response to topiramate than placebo. RESULTS: As shown previously, topiramate significantly reduced both HDDs (odds ratio [OR] = 0.50, p < 0.001) and Short Index of Problems (SIP) scores (b = -3.04, p < 0.001) more than placebo. There were nonsignificant associations of higher PGS with more HDDs (OR = 1.17, 95% CI = 0.98-1.41, p = 0.091) and a greater reduction in HDDs in the topiramate group (OR = 0.80, 95% CI = 0.62-1.03, p = 0.089). There were also significant interaction effects with treatment on SIP score by PGS for PAU (b = -1.64, SE = 0.78, p = 0.033), TR (b = -2.16, SE = 0.72, p = 0.003), and TRH (b = -2.17, SE = 0.72, p = 0.003). CONCLUSIONS: These findings provide proof of principle for the use of alcohol-related PGS as moderators of the effects of topiramate for treating AUD. Larger RCTs of topiramate are needed to provide adequate statistical power to validate this pharmacogenetic approach to precision AUD treatment.

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• 26. X, Y, AND WHY: UNPACKING SEX DIFFERENCES IN ALCOHOL USE AND DEPRESSION

  European Neuropsychopharmacology · 2025-10-01

  articleSenior author
  PublisherDOI

Frequent coauthors

• Henry R. Kranzler

  Washington University in St. Louis

  305 shared

• Joel Gelernter

  263 shared

• Amy C. Justice

  175 shared

• Sylvanus Toikumo

  146 shared

• Emily E. Hartwell

  University of Pennsylvania

  132 shared

• Scott M. Damrauer

  128 shared

• Hang Zhou

  Sun Yat-sen University

  118 shared

• Ke Xu

  Yale University

  106 shared

Labs

• Rachel L. Kember LabPI