The genetic architecture of multimodal human brain age.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

Frequencies of pharmacogenomic alleles across biogeographic groups in a large-scale biobank.

Pharmacogenomics (PGx) is an integral part of precision medicine and contributes to the maximization of drug efficacy and reduction of adverse drug event risk. Accurate information on PGx allele frequencies improves the implementation of PGx. Nonetheless, curating such information from published allele data is time and resource intensive. The limited number of allelic variants in most studies leads to an underestimation of certain alleles. We applied the Pharmacogenomics Clinical Annotation Tool (PharmCAT) on an integrated 200K UK Biobank genetic dataset (N = 200,044). Based on PharmCAT results, we estimated PGx frequencies (alleles, diplotypes, phenotypes, and activity scores) for 17 pharmacogenes in five biogeographic groups: European, Central/South Asian, East Asian, Afro-Caribbean, and Sub-Saharan African. PGx frequencies were distinct for each biogeographic group. Even biogeographic groups with similar proportions of phenotypes were driven by different sets of dominant PGx alleles. PharmCAT also identified "no-function" alleles that were rare or seldom tested in certain groups by previous studies, e.g., SLCO1B1∗31 in the Afro-Caribbean (3.0%) and Sub-Saharan African (3.9%) groups. Estimated PGx frequencies are disseminated via the PharmGKB (The Pharmacogenomics Knowledgebase: www.pharmgkb.org). We demonstrate that genetic biobanks such as the UK Biobank are a robust resource for estimating PGx frequencies. Improving our understanding of PGx allele and phenotype frequencies provides guidance for future PGx studies and clinical genetic test panel design, and better serves individuals from wider biogeographic backgrounds.

A genotype-first approach identifies high incidence of NF1 pathogenic variants with distinct disease associations.

Loss of function variants in the NF1 gene cause neurofibromatosis type 1 (NF1), a genetic disorder characterized by complete penetrance, prevalence of 1 in 3,000, characteristic physical exam findings, and a substantially increased risk for malignancy. However, our understanding of the disorder is entirely based on patients ascertained through phenotype-first approaches. Leveraging a genotype-first approach in two large patient cohorts, we demonstrate unexpectedly high prevalence (1 in 450-750) of NF1 pathogenic variants. Half were identified in individuals lacking clinical features of NF1, with many appearing to have post-zygotic mosaicism for the identified variant. Incidentally discovered variants were not associated with classic NF1 features but were associated with an increased incidence of malignancy compared to a control population. Our findings suggest that NF1 pathogenic variants are substantially more common than previously thought, often characterized by somatic mosaicism and reduced penetrance, and are important contributors to cancer risk in the general population.

The Genetic Architecture of Multimodal Human Brain Age.

The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.

Genome-Wide Association Study of Breast Density among Women of African Ancestry.

Breast density, the amount of fibroglandular versus fatty tissue in the breast, is a strong breast cancer risk factor. Understanding genetic factors associated with breast density may help in clarifying mechanisms by which breast density increases cancer risk. To date, 50 genetic loci have been associated with breast density, however, these studies were performed among predominantly European ancestry populations. We utilized a cohort of women aged 40-85 years who underwent screening mammography and had genetic information available from the Penn Medicine BioBank to conduct a Genome-Wide Association Study (GWAS) of breast density among 1323 women of African ancestry. For each mammogram, the publicly available "LIBRA" software was used to quantify dense area and area percent density. We identified 34 significant loci associated with dense area and area percent density, with the strongest signals in GACAT3, CTNNA3, HSD17B6, UGDH, TAAR8, ARHGAP10, BOD1L2, and NR3C2. There was significant overlap between previously identified breast cancer SNPs and SNPs identified as associated with breast density. Our results highlight the importance of breast density GWAS among diverse populations, including African ancestry populations. They may provide novel insights into genetic factors associated with breast density and help in elucidating mechanisms by which density increases breast cancer risk.

The genetic and phenotypic correlates of mtDNA copy number in a multi-ancestry cohort.

Mitochondrial DNA copy number (mtCN) is often treated as a proxy for mitochondrial (dys-) function and disease risk. Pathological changes in mtCN are common symptoms of rare mitochondrial disorders, but reported associations between mtCN and common diseases vary across studies. To understand the biology of mtCN, we carried out genome- and phenome-wide association studies of mtCN in 30,666 individuals from the Penn Medicine BioBank (PMBB)-a diverse cohort of largely African and European ancestry. We estimated mtCN in peripheral blood using exome sequence data, taking cell composition into account. We replicated known genetic associations of mtCN in the PMBB and found that their effects are highly correlated between individuals of European and African ancestry. However, the heritability of mtCN was much higher among individuals of largely African ancestry (h2=0.3) compared with European ancestry individuals(h2=0.1). Admixture mapping suggests that there are undiscovered variants underlying mtCN that are differentiated in frequency between individuals with African and European ancestry. We show that mtCN is associated with many health-related phenotypes. We discovered robust associations between mtDNA copy number and diseases of metabolically active tissues, such as cardiovascular disease and liver damage, that were consistent across African and European ancestry individuals. Other associations, such as epilepsy and prostate cancer, were only discovered in either individuals with European or African ancestry but not both. We show that mtCN-phenotype associations can be sensitive to blood cell composition and environmental modifiers, explaining why such associations are inconsistent across studies. Thus, mtCN-phenotype associations must be interpreted with care.

Inference of Causal Relationships Between Genetic Risk Factors for Cardiometabolic Phenotypes and Female-Specific Health Conditions.

Background Cardiometabolic diseases are highly comorbid, but their relationship with female-specific or overwhelmingly female-predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross-trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross-trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score-based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross-trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female-specific health conditions.

Pharmacogenetics of Dolutegravir Plasma Exposure Among Southern Africans With Human Immunodeficiency Virus.

BACKGROUND: Dolutegravir is a component of preferred antiretroviral therapy (ART) regimens. We characterized the pharmacogenetics of dolutegravir exposure after ART initiation in the ADVANCE trial in South Africa. METHODS: Genome-wide genotyping followed by imputation was performed. We developed a population pharmacokinetic model for dolutegravir using nonlinear mixed-effects modeling. Linear regression models examined associations with unexplained variability in dolutegravir area under the concentration-time curve (AUCVAR). RESULTS: Genetic associations were evaluable in 284 individuals. Of 9 polymorphisms previously associated with dolutegravir pharmacokinetics, the lowest P value with AUCVAR was UGT1A1 rs887829 (P = 1.8 × 10-4), which was also associated with log10 bilirubin (P = 8.6 × 10-13). After adjusting for rs887829, AUCVAR was independently associated with rs28899168 in the UGT1A locus (P = .02), as were bilirubin concentrations (P = 7.7 × 10-8). In the population pharmacokinetic model, rs887829 T/T and C/T were associated with 25.9% and 10.8% decreases in dolutegravir clearance, respectively, compared with C/C. The lowest P value for AUCVAR genome-wide was CAMKMT rs343942 (P = 2.4 × 10-7). CONCLUSIONS: In South Africa, rs887829 and rs28899168 in the UGT1A locus were independently associated with dolutegravir AUCVAR. The novel rs28899168 association warrants replication. This study enhances understanding of dolutegravir pharmacogenetics in Africa.

Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank.

PURPOSE: Genome-wide association studies have identified hundreds of single nucleotide variations (formerly single nucleotide polymorphisms) associated with several cancers, but the predictive ability of polygenic risk scores (PRSs) is unclear, especially among non-Whites. METHODS: PRSs were derived from genome-wide significant single-nucleotide variations for 15 cancers in 20,079 individuals in an academic biobank. We evaluated the improvement in discriminatory accuracy by including cancer-specific PRS in patients of genetically-determined African and European ancestry. RESULTS: Among the individuals of European genetic ancestry, PRSs for breast, colon, melanoma, and prostate were significantly associated with their respective cancers. Among the individuals of African genetic ancestry, PRSs for breast, colon, prostate, and thyroid were significantly associated with their respective cancers. The area under the curve of the model consisting of age, sex, and principal components was 0.621 to 0.710, and it increased by 1% to 4% with the inclusion of PRS in individuals of European genetic ancestry. In individuals of African genetic ancestry, area under the curve was overall higher in the model without the PRS (0.723-0.810) but increased by <1% with the inclusion of PRS for most cancers. CONCLUSION: PRS moderately increased the ability to discriminate the cancer status in individuals of European but not African ancestry. Further large-scale studies are needed to identify ancestry-specific genetic factors in non-White populations to incorporate PRS into cancer risk assessment.

Genetic liability for substance use associated with medical comorbidities in electronic health records of African- and European-ancestry individuals.

Polygenic risk scores (PRS) represent an individual's summed genetic risk for a trait and can serve as biomarkers for disease. Less is known about the utility of PRS as a means to quantify genetic risk for substance use disorders (SUDs) than for many other traits. Nonetheless, the growth of large, electronic health record-based biobanks makes it possible to evaluate the association of SUD PRS with other traits. We calculated PRS for smoking initiation, alcohol use disorder (AUD), and opioid use disorder (OUD) using summary statistics from the Million Veteran Program sample. We then tested the association of each PRS with its primary phenotype in the Penn Medicine BioBank (PMBB) using all available genotyped participants of African or European ancestry (AFR and EUR, respectively) (N = 18,612). Finally, we conducted phenome-wide association analyses (PheWAS) separately by ancestry and sex to test for associations across disease categories. Tobacco use disorder was the most common SUD in the PMBB, followed by AUD and OUD, consistent with the population prevalence of these disorders. All PRS were associated with their primary phenotype in both ancestry groups. PheWAS results yielded cross-trait associations across multiple domains, including psychiatric disorders and medical conditions. SUD PRS were associated with their primary phenotypes; however, they are not yet predictive enough to be useful diagnostically. The cross-trait associations of the SUD PRS are indicative of a broader genetic liability. Future work should extend findings to additional population groups and for other substances of abuse.

A Genome-First Approach to Rare Variants in Dominant Postlingual Hearing Loss Genes in a Large Adult Population.

OBJECTIVE: To investigate the importance of rare variants in adult-onset hearing loss. STUDY DESIGN: Genomic association study. SETTING: Large biobank from tertiary care center. METHODS: We investigated rare variants (minor allele frequency <5%) in 42 autosomal dominant (DFNA) postlingual hearing loss (HL) genes in 16,657 unselected individuals in the Penn Medicine Biobank. We determined the prevalence of known pathogenic and predicted deleterious variants in subjects with audiometric-proven sensorineural hearing loss. We scanned across known postlingual DFNA HL genes to determine those most significantly contributing to the phenotype. We replicated findings in an independent cohort (UK Biobank). RESULTS: While rare individually, when considering the accumulation of variants in all postlingual DFNA genes, more than 90% of participants carried at least 1 rare variant. Rare variants predicted to be deleterious were enriched in adults with audiometric-proven hearing loss (pure-tone average >25 dB; P = .015). Patients with a rare predicted deleterious variant had an odds ratio of 1.27 for HL compared with genotypic controls (P = .029). Gene burden in DIABLO, PTPRQ, TJP2, and POU4F3 were independently associated with sensorineural hearing loss. CONCLUSION: Although prior reports have focused on common variants, we find that rare predicted deleterious variants in DFNA postlingual HL genes are enriched in patients with adult-onset HL in a large health care system population. We show the value of investigating rare variants to uncover hearing loss phenotypes related to implicated genes.

SARS-CoV-2 Seropositivity and Seroconversion in Patients Undergoing Active Cancer-Directed Therapy.

PURPOSE: Multiple studies have demonstrated the negative impact of cancer care delays during the COVID-19 pandemic, and transmission mitigation techniques are imperative for continued cancer care delivery. We aimed to gauge the effectiveness of these measures at the University of Pennsylvania. METHODS: We conducted a longitudinal study of SARS-CoV-2 antibody seropositivity and seroconversion in patients presenting to infusion centers for cancer-directed therapy between May 21, 2020, and October 8, 2020. Participants completed questionnaires and had up to five serial blood collections. RESULTS: Of 124 enrolled patients, only two (1.6%) had detectable SARS-CoV-2 antibodies on initial blood draw, and no initially seronegative patients developed newly detectable antibodies on subsequent blood draw(s), corresponding to a seroconversion rate of 0% (95% CI, 0.0 TO 4.1%) over 14.8 person-years of follow up, with a median of 13 health care visits per patient. CONCLUSION: These results suggest that patients with cancer receiving in-person care at a facility with aggressive mitigation efforts have an extremely low likelihood of COVID-19 infection.

Rates of COVID-19-Related Outcomes in Cancer Compared With Noncancer Patients.

Cancer patients are a vulnerable population postulated to be at higher risk for severe coronavirus disease 2019 (COVID-19) infection. Increased COVID-19 morbidity and mortality in cancer patients may be attributable to age, comorbidities, smoking, health care exposure, and cancer treatments, and partially to the cancer itself. Most studies to date have focused on hospitalized patients with severe COVID-19, thereby limiting the generalizability and interpretability of the association between cancer and COVID-19 severity. We compared outcomes of SARS-CoV-2 infection in 323 patients enrolled in a population-based study before the pandemic (n = 67 cancer patients; n = 256 noncancer patients). After adjusting for demographics, smoking status, and comorbidities, a diagnosis of cancer was independently associated with higher odds of hospitalization (odds ratio = 2.16, 95% confidence interval = 1.12 to 4.18) and 30-day mortality (odds ratio = 5.67, 95% confidence interval = 1.49 to 21.59). These associations were primarily driven by patients with active cancer. These results emphasize the critical importance of preventing SARS-CoV-2 exposure and mitigating infection in cancer patients.

SARS-CoV-2 seropositivity and seroconversion in patients undergoing active cancer-directed therapy.

Multiple studies have demonstrated the negative impact of cancer care delays during the COVID-19 pandemic, and transmission mitigation techniques are imperative for continued cancer care delivery. To gauge the effectiveness of these measures at the University of Pennsylvania, we conducted a longitudinal study of SARS-CoV-2 antibody seropositivity and seroconversion in patients presenting to infusion centers for cancer-directed therapy between 5/21/2020 and 10/8/2020. Participants completed questionnaires and had up to five serial blood collections. Of 124 enrolled patients, only two (1.6%) had detectable SARS-CoV-2 antibodies on initial blood draw, and no initially seronegative patients developed newly detectable antibodies on subsequent blood draw(s), corresponding to a seroconversion rate of 0% (95%CI 0.0-4.1%) over 14.8 person-years of follow up, with a median of 13 healthcare visits per patient. These results suggest that cancer patients receiving in-person care at a facility with aggressive mitigation efforts have an extremely low likelihood of COVID-19 infection.

Multi-Trait Genome-Wide Association Study of Atherosclerosis Detects Novel Pleiotropic Loci.

Although affecting different arterial territories, the related atherosclerotic vascular diseases coronary artery disease (CAD) and peripheral artery disease (PAD) share similar risk factors and have shared pathobiology. To identify novel pleiotropic loci associated with atherosclerosis, we performed a joint analysis of their shared genetic architecture, along with that of common risk factors. Using summary statistics from genome-wide association studies of nine known atherosclerotic (CAD, PAD) and atherosclerosis risk factors (body mass index, smoking initiation, type 2 diabetes, low density lipoprotein, high density lipoprotein, total cholesterol, and triglycerides), we perform 15 separate multi-trait genetic association scans which resulted in 25 novel pleiotropic loci not yet reported as genome-wide significant for their respective traits. Colocalization with single-tissue eQTLs identified candidate causal genes at 14 of the detected signals. Notably, the signal between PAD and LDL-C at the PCSK6 locus affects PCSK6 splicing in human liver tissue and induced pluripotent derived hepatocyte-like cells. These results show that joint analysis of related atherosclerotic disease traits and their risk factors allowed identification of unified biology that may offer the opportunity for therapeutic manipulation. The signal at PCSK6 represent possible shared causal biology where existing inhibitors may be able to be leveraged for novel therapies.

Polygenic Risk of Psychiatric Disorders Exhibits Cross-trait Associations in Electronic Health Record Data From European Ancestry Individuals.

BACKGROUND: Prediction of disease risk is a key component of precision medicine. Common traits such as psychiatric disorders have a complex polygenic architecture, making the identification of a single risk predictor difficult. Polygenic risk scores (PRSs) denoting the sum of an individual's genetic liability for a disorder are a promising biomarker for psychiatric disorders, but they require evaluation in a clinical setting. METHODS: We developed PRSs for 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, cross disorder, attention-deficit/hyperactivity disorder, and anorexia nervosa) and 17 nonpsychiatric traits in more than 10,000 individuals from the Penn Medicine Biobank with accompanying electronic health records. We performed phenome-wide association analyses to test their association across disease categories. RESULTS: Four of the 6 psychiatric PRSs were associated with their primary phenotypes (odds ratios from 1.2 to 1.6). Cross-trait associations were identified both within the psychiatric domain and across trait domains. PRSs for coronary artery disease and years of education were significantly associated with psychiatric disorders, largely driven by an association with tobacco use disorder. CONCLUSIONS: We demonstrated that the genetic architecture of electronic health record-derived psychiatric diagnoses is similar to ascertained research cohorts from large consortia. Psychiatric PRSs are moderately associated with psychiatric diagnoses but are not yet clinically predictive in naïve patients. Cross-trait associations for these PRSs suggest a broader effect of genetic liability beyond traditional diagnostic boundaries. As identification of genetic markers increases, including PRSs alongside other clinical risk factors may enhance prediction of psychiatric disorders and associated conditions in clinical registries.

Rates of COVID-19-related Outcomes in Cancer compared to non-Cancer Patients.

Cancer patients are a vulnerable population postulated to be at higher risk for severe COVID-19 infection. Increased COVID-19 morbidity and mortality in cancer patients may be attributable to age, comorbidities, smoking, healthcare exposure, and cancer treatments, and partially to the cancer itself. Most studies to date have focused on hospitalized patients with severe COVID-19, thereby limiting the generalizability and interpretability of the association between cancer and COVID-19 severity. We compared outcomes of SARS-CoV-2 infection in 323 patients enrolled prior to the pandemic in a large academic biobank (n=67 cancer patients and n=256 non-cancer patients). After adjusting for demographics, smoking status, and comorbidities, a diagnosis of cancer was independently associated with higher odds of hospitalization (OR 2.16, 95% CI 1.12-4.18) and 30-day mortality (OR 5.67, CI 1.49-21.59). These associations were primarily driven by patients with active cancer. These results emphasize the critical importance of preventing SARS-CoV-2 exposure and mitigating infection in cancer patients.