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Polygenic Risk Scores (PRS) have huge potential to contribute to biomedical research and to a future of precision medicine, but to date their calculation relies largely on Europeanancestry GWAS data. This global bias makes most PRS substantially less accurate in individuals of non-European ancestry. Here we presentBridgePRS, a novel Bayesian PRS method that leverages shared genetic effects across ancestries to increase the accuracy of PRS in non-European populations. The performance ofBridgePRSis evaluated in simulated data and real UK Biobank (UKB) data across 19 traits in African, South Asian and East Asian ancestry individuals, using both UKB and Biobank Japan GWAS summary statistics.BridgePRSis compared to the leading alternative,PRS-CSx, and two single-ancestry PRS methods adapted for trans-ancestry prediction. PRS trained in the UK Biobank are then validated out-of-cohort in the independent Mount Sinai (New York) BioMeBiobank. Simulations reveal thatBridgePRSperformance, relative toPRS-CSx, increases as uncertainty increases: with lower heritability, higher polygenicity, greater between-population genetic diversity, and when causal variants are not present in the data. Our simulation results are consistent with real data analyses in whichBridgePRShas better predictive accuracy in African ancestry samples, especially in out-of-cohort prediction (into BioMe), which shows a 60% boost in meanR2compared toPRS-CSx(P= 2×10−6).BridgePRSperforms the full PRS analysis pipeline, is computationally efficient, and is a powerful method for deriving PRS in diverse and under-represented ancestry populations.

Polygenic risk scores (PRSs) aggregate the effects of genetic variants across the genome and are used to predict risk of complex diseases, such as obesity. Current PRSs only include common variants (minor allele frequency (MAF) ≥1%), whereas the contribution of rare variants in PRSs to predict disease remains unknown. Here, we examine whether augmenting the standard common variant PRS (PRScommon) with a rare variant PRS (PRSrare) improves prediction of obesity. We used genome-wide genotyped and imputed data on 451,145 European-ancestry participants of the UK Biobank, as well as whole exome sequencing (WES) data on 184,385 participants. We performed single variant analyses (for both common and rare variants) and gene-based analyses (for rare variants) for association with BMI (kg/m2), obesity (BMI ≥ 30 kg/m2), and extreme obesity (BMI ≥ 40 kg/m2). We built PRSscommon and PRSsrare using a range of methods (Clumping+Thresholding [C+T], PRS-CS, lassosum, gene-burden test). We selected the best-performing PRSs and assessed their performance in 36,757 European-ancestry unrelated participants with whole genome sequencing (WGS) data from the Trans-Omics for Precision Medicine (TOPMed) program. The best-performing PRScommon explained 10.1% of variation in BMI, and 18.3% and 22.5% of the susceptibility to obesity and extreme obesity, respectively, whereas the best-performing PRSrare explained 1.49%, and 2.97% and 3.68%, respectively. The PRSrare was associated with an increased risk of obesity and extreme obesity (ORobesity = 1.37 per SDPRS, Pobesity = 1.7x10-85; ORextremeobesity = 1.55 per SDPRS, Pextremeobesity = 3.8x10-40), which was attenuated, after adjusting for PRScommon (ORobesity = 1.08 per SDPRS, Pobesity = 9.8x10-6; ORextremeobesity= 1.09 per SDPRS, Pextremeobesity = 0.02). When PRSrare and PRScommon are combined, the increase in explained variance attributed to PRSrare was small (incremental Nagelkerke R2 = 0.24% for obesity and 0.51% for extreme obesity). Consistently, combining PRSrare to PRScommon provided little improvement to the prediction of obesity (PRSrare AUC = 0.591; PRScommon AUC = 0.708; PRScombined AUC = 0.710). In summary, while rare variants show convincing association with BMI, obesity and extreme obesity, the PRSrare provides limited improvement over PRScommon in the prediction of obesity risk, based on these large populations.

BackgroundPolygenic risk score (PRS) analyses are now routinely applied in biomedical research, with great hope that they will aid in our understanding of disease aetiology and contribute to personalized medicine. The continued growth of multi-cohort genome-wide association studies (GWASs) and large-scale biobank projects has provided researchers with a wealth of GWAS summary statistics and individual-level data suitable for performing PRS analyses. However, as the size of these studies increase, the risk of inter-cohort sample overlap and close relatedness increases. Ideally sample overlap would be identified and removed directly, but this is typically not possible due to privacy laws or consent agreements. This sample overlap, whether known or not, is a major problem in PRS analyses because it can lead to inflation of type 1 error and, thus, erroneous conclusions in published work.ResultsHere, for the first time, we report the scale of the sample overlap problem for PRS analyses by generating known sample overlap across sub-samples of the UK Biobank data, which we then use to produce GWAS and target data to mimic the effects of inter-cohort sample overlap. We demonstrate that inter-cohort overlap results in a significant and often substantial inflation in the observed PRS-trait association, coefficient of determination (R2) and false-positive rate. This inflation can be high even when the absolute number of overlapping individuals is small if this makes up a notable fraction of the target sample. We develop and introduce EraSOR (Erase Sample Overlap and Relatedness), a software for adjusting inflation in PRS prediction and association statistics in the presence of sample overlap or close relatedness between the GWAS and target samples. A key component of the EraSOR approach is inference of the degree of sample overlap from the intercept of a bivariate LD score regression applied to the GWAS and target data, making it powered in settings where both have sample sizes over 1,000 individuals. Through extensive benchmarking using UK Biobank and HapGen2 simulated genotype-phenotype data, we demonstrate that PRSs calculated using EraSOR-adjusted GWAS summary statistics are robust to inter-cohort overlap in a wide range of realistic scenarios and are even robust to high levels of residual genetic and environmental stratification.ConclusionThe results of all PRS analyses for which sample overlap cannot be definitively ruled out should be considered with caution given high type 1 error observed in the presence of even low overlap between base and target cohorts. Given the strong performance of EraSOR in eliminating inflation caused by sample overlap in PRS studies with large (>5k) target samples, we recommend that EraSOR be used in all future such PRS studies to mitigate the potential effects of inter-cohort overlap and close relatedness.

Background: Education and cognition demonstrate consistent inverse associations with Alzheimer's disease (AD). The biological underpinnings, however, remain unclear. Blood metabolites reflect the end point of biological processes and are accessible and malleable. Identifying metabolites with etiological relevance to AD and disentangling how these relate to cognitive factors along the AD causal pathway could, therefore, offer unique insights into underlying causal mechanisms. Methods: Using data from the largest metabolomics genome-wide association study (N ≈ 24,925) and three independent AD cohorts (N = 4725), cross-trait polygenic scores were generated and meta-analyzed. Metabolites genetically associated with AD were taken forward for causal analyses. Bidirectional two-sample Mendelian randomization interrogated univariable causal relationships between 1) metabolites and AD; 2) education and cognition; 3) metabolites, education, and cognition; and 4) education, cognition, and AD. Mediating relationships were computed using multivariable Mendelian randomization. Results: Thirty-four metabolites were genetically associated with AD at p < .05. Of these, glutamine and free cholesterol in extra-large high-density lipoproteins demonstrated a protective causal effect (glutamine: 95% confidence interval [CI], 0.70 to 0.92; free cholesterol in extra-large high-density lipoproteins: 95% CI, 0.75 to 0.92). An AD-protective effect was also observed for education (95% CI, 0.61 to 0.85) and cognition (95% CI, 0.60 to 0.89), with bidirectional mediation evident. Cognition as a mediator of the education-AD relationship was stronger than vice versa, however. No evidence of mediation via any metabolite was found. Conclusions: Glutamine and free cholesterol in extra-large high-density lipoproteins show protective causal effects on AD. Education and cognition also demonstrate protection, though education's effect is almost entirely mediated by cognition. These insights provide key pieces of the AD causal puzzle, important for informing future multimodal work and progressing toward effective intervention strategies.

Massive genetic analysis identifies critical pathways and cell types involved in pathogenesis of amyotrophic lateral sclerosis., Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely, neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated six differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, MAPKAPK3, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types.

Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic cortical interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated five differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types.

Importance: Exposure to bullying is a prevalent experience with adverse consequences throughout the life span. Individual vulnerabilities and traits, such as preexisting mental health problems, may be associated with increased likelihood of experiencing bullying. Identifying such individual vulnerabilities and traits is essential for a better understanding of the etiology of exposure to bullying and for tailoring effective prevention.Objective: To identify individual vulnerabilities and traits associated with exposure to bullying in childhood and adolescence.Design, Setting, and Participants: For this study, data were drawn from the Avon Longitudinal Study of Parents and Children (ALSPAC), a population-based birth cohort study. The initial ALSPAC sample consisted of 14 062 children born to women residing in Avon, United Kingdom, with an expected date of delivery between April 1, 1991, and December 31, 1992. Collection of the ALSPAC data began in September 6, 1990, and the last follow-up assessment of exposure to bullying was conducted when participants were 13 years of age. Data analysis was conducted from November 1, 2017, to January 1, 2019.Exposures: The polygenic score approach was used to derive genetic proxies that indexed vulnerabilities and traits. A total of 35 polygenic scores were computed for a range of mental health vulnerabilities (eg, depression) and traits related to cognition (eg, intelligence), personality (eg, neuroticism), and physical measures (eg, body mass index), as well as negative controls (eg, osteoporosis).Main Outcomes and Measures: Single and multi-polygenic score regression models were fitted to test the association between indexed traits and exposure to bullying. Children completed the Bullying and Friendship Interview Schedule at the ages of 8, 10, and 13 years. A mean score of exposure to bullying across ages was used as the main outcome.Results: A total of 5028 genotyped individuals (2481 boys and 2547 girls) with data on exposure to bullying were included. Among the 35 initially included polygenic scores, 11 were independently associated with exposure to bullying; no significant association was detected for the 24 remaining scores. In multivariable analyses, 5 polygenic scores were associated with exposure to bullying; the largest associations were present for genetic risk relating to mental health vulnerabilities, including diagnosis of depression (standardized b = 0.065; 95% CI, 0.035-0.095) and attention-deficit/hyperactivity disorder (standardized b = 0.063; 95% CI, 0.035-0.091), followed by risk taking (standardized b = 0.041; 95% CI, 0.013-0.069), body mass index (standardized b = 0.036; 95% CI, 0.008-0.064), and intelligence (standardized b = -0.031; 95% CI, -0.059 to 0.003).Conclusion and Relevance: Using the multi-polygenic score approach, the findings implicate preexisting mental health vulnerabilities as risk factors for exposure to bullying. A mechanistic understanding of how these vulnerabilities link to exposure of bullying is important to inform prevention strategies.

Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.

INTRODUCTION: It’s imperative to understand the specific and shared aetiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. METHODS: Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic aetiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in eleven data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. RESULTS: All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. CONCLUSIONS: The phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic aetiology irrespective of the age depression first presents.

Background Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required. Results Here we introduce PRSice-2, an efficient and scalable software program for automating and simplifying PRS analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models, and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice-1 and alternative PRS software, LDpred and lassosum, while having comparable predictive power. Conclusion PRSice-2's combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated, e.g., when incorporated into high-dimensional or gene set–based analyses. PRSice-2 is written in C++, with an R script for plotting, and is freely available for download from http://PRSice.info.

Genome-wide studies often exclude family members, even though they are a valuable source of information. We identified parent–offspring pairs, siblings and couples in the UK Biobank and implemented a family-based DNA-derived heritability method to capture additional genetic effects and multiple sources of environmental influence on neuroticism and years of education. Compared to estimates from unrelated individuals, total heritability increased from 10 to 27% and from 17 to 56% for neuroticism and education respectively by including family-based genetic effects. We detected no family environmental influences on neuroticism. The couple similarity variance component explained 35% of the variation in years of education, probably reflecting assortative mating. Overall, our genetic and environmental estimates closely replicate previous findings from an independent sample. However, more research is required to dissect contributions to the additional heritability by rare and structural genetic effects, assortative mating, and residual environmental confounding. The latter is especially relevant for years of education, a highly socially contingent variable, for which our heritability estimate is at the upper end of twin estimates in the literature. Family-based genetic effects could be harnessed to improve polygenic prediction. Electronic supplementary material The online version of this article (10.1007/s10519-019-09984-5) contains supplementary material, which is available to authorized users.

Background & Aims Hepatitis B virus (HBV) integration is common in HBV-associated hepatocellular carcinoma (HCC) and may play an important pathogenic role through the production of chimeric HBV-human transcripts. We aimed to screen the transcriptome for HBV integrations in HCCs. Methods Transcriptome sequencing was performed on paired HBV-associated HCCs and corresponding non-tumorous liver tissues to identify viral-human chimeric sites. Validation was further performed in an expanded cohort of human HCCs. Results Here we report the discovery of a novel pre-mRNA splicing mechanism in generating HBV-human chimeric protein. This mechanism was exemplified by the formation of a recurrent HBV-cyclin A2 (CCNA2) chimeric transcript (A2S), as detected in 12.5% (6 of 48) of HCC patients, but in none of the 22 non-HCC HBV-associated cirrhotic liver samples examined. Upon the integration of HBV into the intron of the CCNA2 gene, the mammalian splicing machinery utilized the foreign splice sites at 282nt. and 458nt. of the HBV genome to generate a pseudo-exon, forming an in-frame chimeric fusion with CCNA2 . The A2S chimeric protein gained a non-degradable property and promoted cell cycle progression, demonstrating its potential oncogenic functions. Conclusions A pre-mRNA splicing mechanism is involved in the formation of HBV-human chimeric proteins. This represents a novel and possibly common mechanism underlying the formation of HBV-human chimeric transcripts from intronically integrated HBV genome with functional impact. Lay summary HBV is involved in the mammalian pre-mRNA splicing machinery in the generation of potential tumorigenic HBV-human chimeras. This study also provided insight on the impact of intronic HBV integration with the gain of splice sites in the development of HBV-associated HCC.

Following publication of this article, the authors noticed an error in Supplementary Table 1. In the original Supplementary Table 1, one of the criteria for control participants was incorrectly given as ‘Report extensive recent symptoms of depression: less than 14 on summed response (where “not at all” = 1 and “nearly every day” = 4) to recent’. This has now been corrected to: ‘Report extensive recent symptoms of depression: less than 5 on summed response (where “not at all” = 1 and “nearly every day” = 4) to recent’.

A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.

BackgroundThe prevalence of depression is higher in individuals suffering from autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Epidemiological findings point to a bi-directional relationship - that depression increases the risk of developing an autoimmune disease, and vice-versa. Shared genetic etiology is a plausible explanation for the overlap between depression and autoimmune diseases. In this study we tested whether genetic variation in the Major Histocompatibility Complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression.MethodWe fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 Human Leukocyte Antigen (HLA) alleles and four Complement Component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium (PGC) Major Depressive Disorder (MDD) working group and the UK Biobank (UKB). In the 26 PGC-MDD studies, cases met a lifetime diagnosis of MDD, determined by a structured diagnostic interview. In the UKB, cases and controls were identified from an online mental health questionnaire. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants in each study and performed an inverse-variance weighted meta-analysis across the PGC-MDD and UKB samples, applying both a conservative region-wide significance threshold (3.9-e6) and a candidate threshold (1.6e-4).ResultsNo HLA alleles or C4 haplotypes were associated with depression at the conservative threshold in the PGC, UKB or meta-analysis. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold in the meta-analysis. Under the conservative threshold, 70 SNPs were detected in the UKB and 143 SNPs were detected in the meta-analysis, mirroring previous findings from highly powered GWAS of depression.DiscussionWe found no evidence that HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia, confer risk for depression. These results indicate that autoimmune diseases and depression do not share common risk loci of moderate or large effect in the MHC.

Background As the scale and power of GWAS have increased to detect the small genetic effect sizes involved in complex polygenic traits, the results of GWAS, especially SNP effect size estimates, are increasingly utilised for prediction. A popular approach is the application of Polygenic Risk Scores (PRS). However, the effect size estimates are inherently prone to overfit the specific samples on which the GWAS were performed (e.g. “the Winner's Curse”). The inflation of effect size estimation reduces the out-of-sample accuracy of prediction based on GWAS results. Many shrinkage methods have been developed to correct for such inflation. As GWAS results and large individual genotype data sets become widely available, there is greater opportunity to accurately evaluate effect size inflation and compare the performance of different shrinkage methods. Methods We develop a novel permutation-based shrinkage method and compare its performance with previously developed methods based on local false discovery rate and the LASSO. We evaluate the performance of the methods across a wide range of phenotypes and investigate different factors (e.g. phenotype heritability) that influence their impact on the predictive power of polygenic risk scores. Results Using large-scale real genotype data, we show that SNP effect sizes are markedly overfit even in relatively large samples, but that our permutation-based shrinkage approach can improve PRS prediction dramatically. Discussion Our results suggest that GWAS results can be adjusted using an efficient empirical approach to provide more accurate effect size estimates and thus greater downstream predictive power. This approach could be applied to a wide variety of big data settings.

Polygenic scores (PGS) summarize the genetic contribution of a person’s genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating polygenic scores have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can make use of LD information available elsewhere to supplement such analyses. To answer this question we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and p-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.

Background In 2015, we published PRSice (‘precise’), a software for the calculation, application and evaluation of polygenic risk scores (PRS). PRSice has been widely used in the field of psychiatric genetics since. Here we describe an updated version – PRSice 2 – which contains several important new features. While a key feature of the original version was its identification of the most predictive PRS via its ‘high resolution scoring’, the inherent over-fitting involved meant that we recommended using a significance threshold of P Methods We demonstrate the performance of PRSice 2 here with our latest cross-trait PRS analysis results, and, in particular, investigate how confounding typically associated with non-genetic studies can be re-introduced in such studies and give rise to misinterpretation of their results.

NIH The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported. (c) 2016 Wolters Kluwer Health, Inc. All rights reserved. Univ Oslo, Oslo Univ Hosp, NORMENT, Oslo, Norway Harvard Univ, Massachusetts Gen Hosp, Sch Med, Ctr Human Genet Res,Dept Psychiat,Psychiat & Neur, Boston, MA USA BroInst MIT & Harvard, Stanley Ctr Psychiat Res, Cambridge, MA USA Harvard Univ, Sch Med, Brockton VA Boston Healthcare Syst, Brockton, MA 02401 USA Texas Tech Univ, Hlth Sci Ctr, Ctr Excellence Neurosci, El Paso, TX USA Virginia Inst Psychiat & Behav Genet, Richmond, VA USA Inst Psychiat Res, Neurosci Res Bldg,320 W 15th St, Indianapolis, IN 46202 USA Cardiff Univ, MRC Ctr Neuropsychiat Genet & Genom, Inst Psychol Med & Clin Neurosci, Cardiff CF10 3AX, S Glam, Wales Univ Edinburgh, Royal Edinburgh Hosp, Sch Clin Sci, Div Psychiat, Edinburgh, Midlothian, Scotland Wellcome Trust Sanger Inst, Hinxton, England UCL, Div Psychiat, London WC1E 6BT, England UCL, Div Psychiat, Mol Psychiat Lab, London WC1E 6BT, England UCL, Inst Psychiat, MRC Social Genet & Dev Psychiat Ctr, London WC1E 6BT, England Univ Hong Kong, Dept Psychiat, Hong Kong, Hong Kong, Peoples R China H Lundbeck & Co AS, Dept Synapt Transmiss, Valby, Denmark Univ Wurzburg, Dept Psychiat, Div Mol Psychiat, Wurzburg, Germany Goethe Univ Frankfurt, Dept Child & Adolescent Psychiat Psychosomat & Ps, D-60054 Frankfurt, Germany Natl Inst Mental Hlth & NeuroSci, Bangalore, Karnataka, India Univ Stellenbosch, Fac Med & Hlth Sci, SA MRC Ctr TB Res,div Mol Biol & Human Genet, DST NRF Ctr Excellence Biomed TB Res,Dept Psychia, Cape Town, South Africa Univ Helsinki, Fac Biol & Environm Sci, Dept Biosci, Helsinki, Finland Semmelweis Univ, Inst Med Chem Mol Biol & Pathobiochem, H-1085 Budapest, Hungary Univ Cagliari, Dept Biomed Sci, Sect Neurosci & Clin Pharmacol, Monserrato, Italy Univ Fed Sao Paulo, Dept Morphol & Genet, Sao Paulo, Brazil Neurosci Res Australia, Barker St Randwick, Sydney, NSW, Australia Karolinska Inst, Dept Med Epidemiol & Biostat, Solna, Sweden Radboud Univ Nijmegen, Med Ctr, Donders Inst Brain Cognit & Behav, Dept Human Genet, NL-6525 ED Nijmegen, Netherlands Univ Fed Sao Paulo, Dept Morphol & Genet, Sao Paulo, Brazil NIH: R13AA017055 Web of Science

In this paper, we report a novel heterozygous mutation of A285V codon conversion on exon 4 of the desmin (DES), using whole exome sequencing (WES) in an isolated proband with documented dilated cardiomyopathy (DCM). This mutation is predicted to cause three-dimensional structure changes of DES. Immunohistological and electron microscopy studies demonstrated diffuse abnormal DES aggregations in DCM-induced-pluripotent stem cell (iPSC)-derived cardiomyocytes, and control-iPSC-derived cardiomyocytes transduced with A285V-DES. DCM-iPSC-derived cardiomyocytes also exhibited functional abnormalities in vitro. This is the first demonstration that patient-specific iPSC-derived cardiomyocytes can be used to provide histological and functional confirmation of a suspected genetic basis for DCM identified by WES.

Detection of structural variations is an important problem. With the next generation sequencing technology, it is relatively easier to obtain a set of paired-end short reads from an individual (the donor). By aligning these reads onto a reference genome (the reference), we are able to detect some of the structural variations that exist between the donor and the reference. A number of tools were developed in this direction. However, these tools are not able to detect all types of variations. In particular, they do not perform well for the detection of tandem duplications which are found to be associated with some diseases. In this paper, we try to solve this problem and developed algorithm to identify novel tandem duplications that exist in donor and vice versa. Experimental results on both simulated and real datasets showed that our solutions are effective.