Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk.

Polygenic risk scores (PRSs) for a variety of diseases have recently been shown to have relative risks that depend on age, and genetic relative risks decrease with increasing age. A refined understanding of the age dependency of PRSs for a disease is important for personalized risk predictions and risk stratification. To further evaluate how the PRS relative risk for prostate cancer depends on age, we refined analyses for a validated PRS for prostate cancer by using 64,274 prostate cancer cases and 46,432 controls of diverse ancestry (82.8% European, 9.8% African American, 3.8% Latino, 2.8% Asian, and 0.8% Ghanaian). Our strategy applied a novel weighted proportional hazards model to case-control data to fully utilize age to refine how the relative risk decreased with age. We found significantly greater relative risks for younger men (age 30-55 years) compared with older men (70-88 years) for both relative risk per standard deviation of the PRS and dichotomized according to the upper 90th percentile of the PRS distribution. For the largest European ancestral group that could provide reliable resolution, the log-relative risk decreased approximately linearly from age 50 to age 75. Despite strong evidence of age-dependent genetic relative risk, our results suggest that absolute risk predictions differed little from predictions that assumed a constant relative risk over ages, from short-term to long-term predictions, simplifying implementation of risk discussions into clinical practice.

Clinical and genetic contributions to medical comorbidity in bipolar disorder: a study using electronic health records-linked biobank data.

Bipolar disorder is a chronic and complex polygenic disease with high rates of comorbidity. However, the independent contribution of either diagnosis or genetic risk of bipolar disorder to the medical comorbidity profile of individuals with the disease remains unresolved. Here, we conducted a multi-step phenome-wide association study (PheWAS) of bipolar disorder using phenomes derived from the electronic health records of participants enrolled in the Mayo Clinic Biobank and the Mayo Clinic Bipolar Disorder Biobank. First, we explored the conditions associated with a diagnosis of bipolar disorder by conducting a phenotype-based PheWAS followed by LASSO-penalized regression to account for correlations within the phenome. Then, we explored the conditions associated with bipolar disorder polygenic risk score (BD-PRS) using a PRS-based PheWAS with a sequential exclusion approach to account for the possibility that diagnosis, instead of genetic risk, may drive such associations. 53,386 participants (58.7% women) with a mean age at analysis of 67.8 years (SD = 15.6) were included. A bipolar disorder diagnosis (n = 1479) was associated with higher rates of psychiatric conditions, injuries and poisonings, endocrine/metabolic and neurological conditions, viral hepatitis C, and asthma. BD-PRS was associated with psychiatric comorbidities but, in contrast, had no positive associations with general medical conditions. While our findings warrant confirmation with longitudinal-prospective studies, the limited associations between bipolar disorder genetics and medical conditions suggest that shared environmental effects or environmental consequences of diagnosis may have a greater impact on the general medical comorbidity profile of individuals with bipolar disorder than its genetic risk.

Myocardial Recovery in Recent Onset Dilated Cardiomyopathy: Role of CDCP1 and Cardiac Fibrosis.

BACKGROUND: Dilated cardiomyopathy (DCM) is a major cause of heart failure and carries a high mortality rate. Myocardial recovery in DCM-related heart failure patients is highly variable, with some patients having little or no response to standard drug therapy. A genome-wide association study may agnostically identify biomarkers and provide novel insight into the biology of myocardial recovery in DCM. METHODS: A genome-wide association study for change in left ventricular ejection fraction was performed in 686 White subjects with recent-onset DCM who received standard pharmacotherapy. Genome-wide association study signals were subsequently functionally validated and studied in relevant cellular models to understand molecular mechanisms that may have contributed to the change in left ventricular ejection fraction. RESULTS: The genome-wide association study identified a highly suggestive locus that mapped to the 5'-flanking region of the CDCP1 (CUB [complement C1r/C1s, Uegf, and Bmp1] domain containing protein 1) gene (rs6773435; P=7.12×10-7). The variant allele was associated with improved cardiac function and decreased CDCP1 transcription. CDCP1 expression was significantly upregulated in human cardiac fibroblasts (HCFs) in response to the PDGF (platelet-derived growth factor) signaling, and knockdown of CDCP1 significantly repressed HCF proliferation and decreased AKT (protein kinase B) phosphorylation. Transcriptomic profiling after CDCP1 knockdown in HCFs supported the conclusion that CDCP1 regulates HCF proliferation and mitosis. In addition, CDCP1 knockdown in HCFs resulted in significantly decreased expression of soluble ST2 (suppression of tumorigenicity-2), a prognostic biomarker for heart failure and inductor of cardiac fibrosis. CONCLUSIONS: CDCP1 may play an important role in myocardial recovery in recent-onset DCM and mediates its effect primarily by attenuating cardiac fibrosis.

The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people.

BACKGROUND: Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation. METHODS: Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry. RESULTS: In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity. CONCLUSIONS: This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Multivariate generalized linear model for genetic pleiotropy.

When a single gene influences more than one trait, known as pleiotropy, it is important to detect pleiotropy to improve the biological understanding of a gene. This can lead to improved screening, diagnosis, and treatment of diseases. Yet, most current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or fewer traits are associated with a genetic variant. We recently developed statistical methods to analyze pleiotropy for quantitative traits having a multivariate normal distribution. We now extend this approach to traits that can be modeled by generalized linear models, such as analysis of binary, ordinal, or quantitative traits, or a mixture of these types of traits. Based on methods from estimating equations, we developed a new test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that $k+1$ traits are associated, given that the null of $k$ associated traits was rejected. This provides a testing framework to determine the number of traits associated with a genetic variant, as well as which traits, while accounting for correlations among the traits. By simulations, we illustrate the Type-I error rate and power of our new methods, describe how they are influenced by sample size, the number of traits, and the trait correlations, and apply the new methods to a genome-wide association study of multivariate traits measuring symptoms of major depression. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.

Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD.

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) represents the most common pathological subtype of FTLD. We established the international FTLD-TDP whole-genome sequencing consortium to thoroughly characterize the known genetic causes of FTLD-TDP and identify novel genetic risk factors. Through the study of 1131 unrelated Caucasian patients, we estimated that C9orf72 repeat expansions and GRN loss-of-function mutations account for 25.5% and 13.9% of FTLD-TDP patients, respectively. Mutations in TBK1 (1.5%) and other known FTLD genes (1.4%) were rare, and the disease in 57.7% of FTLD-TDP patients was unexplained by the known FTLD genes. To unravel the contribution of common genetic factors to the FTLD-TDP etiology in these patients, we conducted a two-stage association study comprising the analysis of whole-genome sequencing data from 517 FTLD-TDP patients and 838 controls, followed by targeted genotyping of the most associated genomic loci in 119 additional FTLD-TDP patients and 1653 controls. We identified three genome-wide significant FTLD-TDP risk loci: one new locus at chromosome 7q36 within the DPP6 gene led by rs118113626 (p value = 4.82e - 08, OR = 2.12), and two known loci: UNC13A, led by rs1297319 (p value = 1.27e - 08, OR = 1.50) and HLA-DQA2 led by rs17219281 (p value = 3.22e - 08, OR = 1.98). While HLA represents a locus previously implicated in clinical FTLD and related neurodegenerative disorders, the association signal in our study is independent from previously reported associations. Through inspection of our whole-genome sequence data for genes with an excess of rare loss-of-function variants in FTLD-TDP patients (n ≥ 3) as compared to controls (n = 0), we further discovered a possible role for genes functioning within the TBK1-related immune pathway (e.g., DHX58, TRIM21, IRF7) in the genetic etiology of FTLD-TDP. Together, our study based on the largest cohort of unrelated FTLD-TDP patients assembled to date provides a comprehensive view of the genetic landscape of FTLD-TDP, nominates novel FTLD-TDP risk loci, and strongly implicates the immune pathway in FTLD-TDP pathogenesis.

The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression.

Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (PT) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.

Metformin pharmacogenomics: a genome-wide association study to identify genetic and epigenetic biomarkers involved in metformin anticancer response using human lymphoblastoid cell lines.

Metformin is currently considered as a promising anticancer agent in addition to its anti-diabetic effect. To better individualize metformin therapy and explore novel molecular mechanisms in cancer treatment, we conducted a pharmacogenomic study using 266 lymphoblastoid cell lines (LCLs). Metformin cytotoxicity assay was performed using the MTS assay. Genome-wide association (GWA) analyses were performed in LCLs using 1.3 million SNPs, 485k DNA methylation probes, 54k mRNA expression probe sets, and metformin cytotoxicity (IC50s). Top candidate genes were functionally validated using siRNA screening, followed by MTS assay in breast cancer cell lines. Further study of one top candidate, STUB1, was performed to elucidate the mechanisms by which STUB1 might contribute to metformin action. GWA analyses in LCLs identified 198 mRNA expression probe sets, 12 SNP loci, and 5 DNA methylation loci associated with metformin IC50 with P-values <10−4 or <10−5. Integrated SNP/methylation loci-expression-IC50 analyses found 3 SNP loci or 5 DNA methylation loci associated with metformin IC50 through trans-regulation of expression of 11 or 26 genes with P-value <10−4. Functional validation of top 61 candidate genes in 4 IPA networks indicated down regulation of 14 genes significantly altered metformin sensitivity in two breast cancer cell lines. Mechanistic studies revealed that the E3 ubiquitin ligase, STUB1, could influence metformin response by facilitating proteasome-mediated degradation of cyclin A. GWAS using a genomic data-enriched LCL model system, together with functional and mechanistic studies using cancer cell lines, help us to identify novel genetic and epigenetic biomarkers involved in metformin anticancer response.

Genomewide association study reveals novel genetic loci associated with change in renal function in heart transplant recipients.

BACKGROUND: Renal dysfunction occurs commonly after heart transplantation (HTx) with wide inter-individual variability but whether a genetic predisposition exists in these patients is unknown. Genomewide association studies (GWAS) have not been performed to assess the association of genetic variation with change in renal function after HTx. METHODS: Clinical and demographic data of patients who underwent HTx and provided blood samples and consent for genetic analysis were included. Genotyping was performed using Illumina Infinium Human CoreExome v1.0 analysis kit. A GWAS utilizing linear regression models was performed with estimated glomerular filtration rate (eGFR) at 1 year as the phenotype after adjusting for baseline eGFR prior to HTx and conversion from calcineurin inhibitor to sirolimus as primary immunosuppression therapy. RESULTS: A total of 251 HTx recipients were genotyped for 314,903 single nucleotide polymorphisms (SNPs). The mean (SD) age was 50 (12.5) years; most patients were of European origin (n = 243, 96.8%) and males (n = 179, 71.3%). After adjustment for potential confounders, two variants, rs17033285 (P = 4.3 × 10-7 ) and rs4917601 (P = 6.46 × 10-7 ), in a long non-coding RNA (lncRNA) gene LINC01121 and a pseudogene BTBD7P2, were identified to have a significant association with change in GFR at 1 year after HTx. CONCLUSIONS: Our first of its kind GWAS demonstrates that genetic variation affects renal function after HTx independent of other risk factors. Agnostic genetic approaches such as these may lead to identification of novel biological pathways such as the role of lncRNAs in the development of renal dysfunction post-HTx.

SLCO1B1 polymorphisms and plasma estrone conjugates in postmenopausal women with ER+ breast cancer: genome-wide association studies of the estrone pathway.

BACKGROUND: Estrone (E1), the major circulating estrogen in postmenopausal women, promotes estrogen-receptor positive (ER+) breast tumor growth and proliferation. Two major reactions contribute to E1 plasma concentrations, aromatase (CYP19A1) catalyzed E1 synthesis from androstenedione and steroid sulfatase (STS) catalyzed hydrolysis of estrone conjugates (E1Cs). E1Cs have been associated with breast cancer risk and may contribute to tumor progression since STS is expressed in breast cancer where its activity exceeds that of aromatase. METHODS: We performed genome-wide association studies (GWAS) to identify SNPs associated with variation in plasma concentrations of E1Cs, E1, and androstenedione in 774 postmenopausal women with resected early-stage ER+ breast cancer. Hormone concentrations were measured prior to aromatase inhibitor therapy. RESULTS: Multiple SNPs in SLCO1B1, a gene encoding a hepatic influx transporter, displayed genome-wide significant associations with E1C plasma concentrations and with the E1C/E1 ratio. The top SNP for E1C concentrations, rs4149056 (p = 3.74E-11), was a missense variant that results in reduced transporter activity. Patients homozygous for the variant allele had significantly higher average E1C plasma concentrations than did other patients. Furthermore, three other SLCO1B1 SNPs, not in LD with rs4149056, were associated with both E1C concentrations and the E1C/E1 ratio and were cis-eQTLs for SLCO1B3. GWAS signals of suggestive significance were also observed for E1, androstenedione, and the E1/androstenedione ratio. CONCLUSION: These results suggest a mechanism for genetic variation in E1C plasma concentrations as well as possible SNP biomarkers to identify ER+ breast cancer patients for whom STS inhibitors might be of clinical value.

Discovery of genetic biomarkers contributing to variation in drug response of cytidine analogues using human lymphoblastoid cell lines.

BACKGROUND: Two cytidine analogues, gemcitabine and cytosine arabinoside (AraC), are widely used in the treatment of a variety of cancers with a large individual variation in response. To identify potential genetic biomarkers associated with response to these two drugs, we used a human lymphoblastoid cell line (LCL) model system with extensive genomic data, including 1.3 million SNPs and 54,000 basal expression probesets to perform genome-wide association studies (GWAS) with gemcitabine and AraC IC50 values. RESULTS: We identified 11 and 27 SNP loci significantly associated with gemcitabine and AraC IC50 values, respectively. Eleven candidate genes were functionally validated using siRNA knockdown approach in multiple cancer cell lines. We also characterized the potential mechanisms of genes by determining their influence on the activity of 10 cancer-related signaling pathways using reporter gene assays. Most SNPs regulated gene expression in a trans manner, except 7 SNPs in the PIGB gene that were significantly associated with both the expression of PIGB and gemcitabine cytotoxicity. CONCLUSION: These results suggest that genetic variation might contribute to drug response via either cis- or trans- regulation of gene expression. GWAS analysis followed by functional pharmacogenomics studies might help identify novel biomarkers contributing to variation in response to these two drugs and enhance our understanding of underlying mechanisms of drug action.

Identification of a novel percent mammographic density locus at 12q24.

Percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer and has a heritable component that remains largely unidentified. We performed a three-stage genome-wide association study (GWAS) of percent mammographic density to identify novel genetic loci associated with this trait. In stage 1, we combined three GWASs of percent density comprised of 1241 women from studies at the Mayo Clinic and identified the top 48 loci (99 single nucleotide polymorphisms). We attempted replication of these loci in 7018 women from seven additional studies (stage 2). The meta-analysis of stage 1 and 2 data identified a novel locus, rs1265507 on 12q24, associated with percent density, adjusting for age and BMI (P = 4.43 × 10(-8)). We refined the 12q24 locus with 459 additional variants (stage 3) in a combined analysis of all three stages (n = 10 377) and confirmed that rs1265507 has the strongest association in the 12q24 region (P = 1.03 × 10(-8)). Rs1265507 is located between the genes TBX5 and TBX3, which are members of the phylogenetically conserved T-box gene family and encode transcription factors involved in developmental regulation. Understanding the mechanism underlying this association will provide insight into the genetics of breast tissue composition.

Citalopram and escitalopram plasma drug and metabolite concentrations: genome-wide associations.

AIMS: Citalopram (CT) and escitalopram (S-CT) are among the most widely prescribed selective serotonin reuptake inhibitors used to treat major depressive disorder (MDD). We applied a genome-wide association study to identify genetic factors that contribute to variation in plasma concentrations of CT or S-CT and their metabolites in MDD patients treated with CT or S-CT. METHODS: Our genome-wide association study was performed using samples from 435 MDD patients. Linear mixed models were used to account for within-subject correlations of longitudinal measures of plasma drug/metabolite concentrations (4 and 8 weeks after the initiation of drug therapy), and single-nucleotide polymorphisms (SNPs) were modelled as additive allelic effects. RESULTS: Genome-wide significant associations were observed for S-CT concentration with SNPs in or near the CYP2C19 gene on chromosome 10 (rs1074145, P = 4.1 × 10(-9) ) and with S-didesmethylcitalopram concentration for SNPs near the CYP2D6 locus on chromosome 22 (rs1065852, P = 2.0 × 10(-16) ), supporting the important role of these cytochrome P450 (CYP) enzymes in biotransformation of citalopram. After adjustment for the effect of CYP2C19 functional alleles, the analyses also identified novel loci that will require future replication and functional validation. CONCLUSIONS: In vitro and in vivo studies have suggested that the biotransformation of CT to monodesmethylcitalopram and didesmethylcitalopram is mediated by CYP isozymes. The results of our genome-wide association study performed in MDD patients treated with CT or S-CT have confirmed those observations but also identified novel genomic loci that might play a role in variation in plasma levels of CT or its metabolites during the treatment of MDD patients with these selective serotonin reuptake inhibitors.

Genetic correlations of alcohol consumption and alcohol use disorder with sex hormone levels in females and males.

Background: Alcohol consumption behaviors and alcohol use disorder risk and presentation differ by sex, and these complex traits are associated with blood concentrations of the steroid sex hormones, testosterone and estradiol, and their regulatory binding proteins, sex hormone binding globulin (SHBG) and albumin. Genetic variation is associated with alcohol consumption and alcohol use disorder, as well as levels of steroid sex hormones and their binding proteins. Methods: To assess the contribution of genetic factors to previously described phenotypic associations between alcohol-use traits and sex-hormone levels, we estimated genetic correlations (rg) using summary statistics from prior published, large sample size genome-wide association studies (GWAS) of alcohol consumption, alcohol dependence, testosterone, estradiol, SHBG, and albumin. Results: For alcohol consumption, we observed positive genetic correlation (i.e. genetic effects in the same direction) with total testosterone in males (rg = 0.084, p = 0.007) and trends toward positive genetic correlation with bioavailable testosterone (rg = 0.060, p = 0.084) and SHBG in males (rg = 0.056, p = 0.086) and with albumin in a sex-combined cohort (rg = 0.082, p = 0.015); however in females, we observed positive genetic correlation with SHBG (rg = 0.089, p = 0.004) and a trend toward negative genetic correlation (i.e. genetic effects in opposite directions) with bioavailable testosterone (rg = -0.064, p = 0.032). For alcohol dependence, we observed a trend toward negative genetic correlation with total testosterone in females (rg = -0.106, p = 0.024) and positive genetic correlation with BMI-adjusted SHBG in males (rg = 0.119, p = 0.017). Several of these genetic correlations differed between females and males and were not in the same direction as the corresponding phenotypic associations. Conclusions: Findings suggest that shared genetic effects may contribute to positive associations of alcohol consumption with albumin in both sexes, as well as positive associations between alcohol consumption and bioavailable testosterone and between alcohol dependence and SHBG in males. However, relative contributions of heritable and environmental factors to associations between alcohol-use traits and sex-hormone levels may differ by sex, with genetic factors contributing more in males and environmental factors contributing more in females.

Pharmacogenomic overlap between antidepressant treatment response in major depression & antidepressant associated treatment emergent mania in bipolar disorder.

There is increasing interest in individualizing treatment selection for more than 25 regulatory approved treatments for major depressive disorder (MDD). Despite an inconclusive efficacy evidence base, antidepressants (ADs) are prescribed for the depressive phase of bipolar disorder (BD) with oftentimes, an inadequate treatment response and or clinical concern for mood destabilization. This study explored the relationship between antidepressant response in MDD and antidepressant-associated treatment emergent mania (TEM) in BD. We conducted a genome-wide association study (GWAS) and polygenic score analysis of TEM and tested its association in a subset of BD-type I patients treated with SSRIs or SNRIs. Our results did not identify any genome-wide significant variants although, we found that a higher polygenic score (PGS) for antidepressant response in MDD was associated with higher odds of TEM in BD. Future studies with larger transdiagnostic depressed cohorts treated with antidepressants are encouraged to identify a neurobiological mechanism associated with a spectrum of depression improvement from response to emergent mania.

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder.

Post-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.

FKBP5 genetic variation: association with selective serotonin reuptake inhibitor treatment outcomes in major depressive disorder.

OBJECTIVES: FKBP51 (51 kDa immunophilin) acts as a modulator of the glucocorticoid receptor and a negative regulator of the Akt pathway. Genetic variation in FKBP5 plays a role in antidepressant response. The aim of this study was to comprehensively assess the role of genetic variation in FKBP5, identified by both Sanger and Next Generation DNA resequencing, as well as genome-wide single nucleotide polymorphisms (SNPs) associated with FKBP5 expression in the response to the selective serotonin reuptake inhibitor (SSRI) treatment of major depressive disorder. METHODS: We identified 657 SNPs in FKBP5 by Next Generation sequencing of 96 DNA samples from white patients, and 149 SNPs were selected for the genotyping together with 235 SNPs that were trans-associated with variation in FKBP5 expression in lymphoblastoid cells. A total of 529 DNA samples from the Mayo Clinic PGRN-SSRI Pharmacogenomic trial for which genome-wide SNPs had already been obtained were genotyped for these 384 SNPs, and associations with treatment outcomes were determined. The most significant SNPs were genotyped using 96 DNA samples from white non-Hispanic patients of the NIMH-supported Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study to attempt replication, followed by functional genomic studies. RESULTS: Genotype-phenotype association analysis indicated that rs352428 was associated with both 8-week treatment response in the Mayo study (odds ratio=0.49; P=0.003) and 6-week response in the STAR*D replication study (odds ratio=0.74; P=0.05). The electrophoresis mobility shift assay and the reporter gene assay confirmed the possible role of this SNP in transcription regulation. CONCLUSION: This comprehensive FKBP5 sequence study provides insight into the role of common genetic polymorphisms that might influence SSRI treatment outcomes in major depressive disorder patients.

Radiation pharmacogenomics: a genome-wide association approach to identify radiation response biomarkers using human lymphoblastoid cell lines.

Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels and 1.3 million genome-wide single nucleotide polymorphism (SNP) markers from both Affymetrix and Illumina platforms were assayed for all 277 human LCLs. MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium] assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression, and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. A total of 27 loci, each containing at least two SNPs within 50 kb with P-values less than 10(-4) were associated with radiation AUC. A total of 270 expression probe sets were associated with radiation AUC with P < 10(-3). The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes, which were also associated with radiation AUC (P < 10(-3)). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52, and DEPDC1B each significantly altered radiation sensitivity in at least two cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation.

Myocardial Fibrosis and Cardiomyopathy Risk: A Genetic Link in the MESA.

BACKGROUND: Common genetic variants are associated with risk for hypertrophic cardiomyopathy and dilated cardiomyopathy and with left ventricular (LV) traits. Whether these variants are associated with myocardial fibrosis, an important pathophysiological mediator of cardiomyopathy, is unknown. METHODS: Multi-Ethnic Study of Atherosclerosis participants with T1-mapping cardiac magnetic resonance imaging in-whom extracellular volume was assessed, and genotyping information was available were included (N=1255). Log extracellular volume (%) was regressed on 50 candidate single nucleotide polymorphisms (previously identified to be associated with hypertrophic cardiomyopathy, dilated cardiomyopathy, and LV traits) adjusting for age, sex, diabetes, blood pressure, and principal components of ancestry. Ancestry-specific results were pooled by fixed-effect meta-analyses. Gene knockdown experiments were performed in human cardiac fibroblasts. RESULTS: The SMARCB1 rs2186370 intronic variant (minor allele frequency: 0.18 in White and 0.50 in Black participants), previously identified as a risk variant for dilated cardiomyopathy and hypertrophic cardiomyopathy, was significantly associated with increased extracellular volume (P=0.0002) after adjusting for confounding clinical variables. The SMARCB1 rs2070458 locus previously associated with increased LV wall thickness and mass was similarly significantly associated with increased extracellular volume (P=0.0002). The direction of effect was similar in all 4 ancestry groups, but the effect was strongest in Black participants. The variants are strong expression quantitative loci in human LV tissue and associated with genotype-dependent decreased expression of SMARCB1 (P=7.3×10-22). SMARCB1 knockdown in human cardiac fibroblasts resulted in increased TGF (transforming growth factor)-ß1-mediated α-smooth muscle actin and collagen expression. CONCLUSIONS: Common genetic variation in SMARCB1 previously associated with risk for cardiomyopathies and increased LV wall thickness is associated with increased cardiac magnetic resonance imaging-based myocardial fibrosis and increased TGF-ß1 mediated myocardial fibrosis in vitro. Whether these findings suggest a pathophysiologic link between myocardial fibrosis and cardiomyopathy risk remains to be proven.