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ObjectiveBoth the gut microbiome and host genetics are known to play significant roles in the pathogenesis of IBD. However, the interaction between these two factors and its implications in the aetiology of IBD remain underexplored. Here, we report on the influence of host genetics on the gut microbiome in IBD.DesignTo evaluate the impact of host genetics on the gut microbiota of patients with IBD, we combined whole exome sequencing of the host genome and whole genome shotgun sequencing of 1464 faecal samples from 525 patients with IBD and 939 population-based controls. We followed a four-step analysis: (1) exome-wide microbial quantitative trait loci (mbQTL) analyses, (2) a targeted approach focusing on IBD-associated genomic regions and protein truncating variants (PTVs, minor allele frequency (MAF) >5%), (3) gene-based burden tests on PTVs with MAF ResultsWe identified 12 mbQTLs, including variants in the IBD-associated genes IL17REL, MYRF, SEC16A and WDR78. For example, the decrease of the pathway acetyl-coenzyme A biosynthesis, which is involved in short chain fatty acids production, was associated with variants in the gene MYRF (false discovery rate CYP2D6, GPR151 and CD160 genes. These genes are known for their function in the immune system. Moreover, interaction analyses confirmed previously known IBD disease-specific mbQTLs in TNFSF15.ConclusionThis study highlights that both common and rare genetic variants affecting the immune system are key factors in shaping the gut microbiota in the context of IBD and pinpoints towards potential mechanisms for disease treatment.

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African–European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants. Tractor is a statistical framework that facilitates the inclusion of admixed individuals in association studies by leveraging local ancestry. Tractor generates accurate ancestry-specific effect-size estimates and improves the resolution of association signals.

BACKGROUND: Tourette syndrome (TS) is often found comorbid with other neurodevelopmental disorders across the impulsivity-compulsivity spectrum, with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) as most prevalent. This points to the possibility of a common etiological thread along an impulsivity-compulsivity continuum.METHODS: Investigating the shared genetic basis across TS, ADHD, ASD, and OCD, we undertook an evaluation of cross-disorder genetic architecture and systematic meta-analysis, integrating summary statistics from the latest genome-wide association studies (93,294 individuals, 6,788,510 markers).RESULTS: As previously identified, a common unifying factor connects TS, ADHD, and ASD, while TS and OCD show the highest genetic correlation in pairwise testing among these disorders. Thanks to a more homogeneous set of disorders and a targeted approach that is guided by genetic correlations, we were able to identify multiple novel hits and regions that seem to play a pleiotropic role for the specific disorders analyzed here and could not be identified through previous studies. In the TS-ADHD-ASD genome-wide association study single nucleotide polymorphism-based and gene-based meta-analysis, we uncovered 13 genome-wide significant regions that host single nucleotide polymorphisms with a high posterior probability for association with all three studied disorders (m-value > 0.9), 11 of which were not identified in previous cross-disorder analysis. In contrast, we also identified two additional pleiotropic regions in the TS-OCD meta-analysis. Through conditional analysis, we highlighted genes and genetic regions that play a specific role in a TS-ADHD-ASD genetic factor versus TS-OCD. Cross-disorder tissue specificity analysis implicated the hypothalamus-pituitary-adrenal gland axis in TS-ADHD-ASD.CONCLUSIONS: Our work underlines the value of redefining the framework for research across traditional diagnostic categories.

Funding Information: This work was supported by the National Institute of Mental Health / U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium ), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience . Statistical analyses were carried out on the LISA/Genetic Cluster Computer ( https://userinfo.surfsara.nl/systems/lisa ) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. Funding Information: MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc., RallyPoint Networks, Inc., Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Funding Information: This work was supported by the National Institute of Mental Health/ U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience. Statistical analyses were carried out on the LISA/Genetic Cluster Computer (https://userinfo.surfsara.nl/systems/lisa) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. This material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting true views of the U.S. Department of the Army or the Department of Defense. We thank the investigators who comprise the PGC-PTSD working group and especially the more than 206,000 research participants worldwide who shared their life experiences and biological samples with PGC-PTSD investigators. We thank Mark Zervas for his critical input. Full acknowledgments are in Supplement 1. MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc. RallyPoint Networks, Inc. Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled ?Genotype-guided dosing of opioid agonists,? filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: © 2021 Society of Biological Psychiatry Background: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods. publishersversion published

Rare coding variation has historically provided the most direct connections between gene function and disease pathogenesis. By meta-analysing the whole exomes of 24,248 schizophrenia cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in 10 genes as conferring substantial risk for schizophrenia (odds ratios of 3–50, P < 2.14 × 10−6) and 32 genes at a false discovery rate of 1, epilepsy and severe neurodevelopmental disorders2, although different mutation types are implicated in some shared genes. Most genes described here, however, are not implicated in neurodevelopment. We demonstrate that genes prioritized from common variant analyses of schizophrenia are enriched in rare variant risk3, suggesting that common and rare genetic risk factors converge at least partially on the same underlying pathogenic biological processes. Even after excluding significantly associated genes, schizophrenia cases still carry a substantial excess of URVs, which indicates that more risk genes await discovery using this approach.

Large-scale next-generation sequencing datasets have been transformative for informing clinical variant interpretation and as reference panels for statistical and population genetic efforts. While such resources are often treated as ground truth, we find that in widely used reference datasets such as the Genome Aggregation Database (gnomAD), some variants pass gold standard filters yet are systematically different in their genotype calls across genotype discovery approaches. The inclusion of such discordant sites in study designs involving multiple genotype discovery strategies could bias results and lead to false-positive hits in association studies due to technological artifacts rather than a true relationship to the phenotype. Here, we describe this phenomenon of discordant genotype calls across genotype discovery approaches, characterize the error mode of wrong calls, provide a blacklist of discordant sites identified in gnomAD that should be treated with caution in analyses, and present a metric and machine learning classifier trained on gnomAD data to identify likely discordant variants in other datasets. We find that different genotype discovery approaches have different sets of variants at which this problem occurs but that there are characteristic variant features that can be used to predict discordant behavior. Discordant sites are largely shared across ancestry groups, though different populations are powered for discovery of different variants. We find that the most common error mode is that of a variant being heterozygous for one approach and homozygous for the other, with heterozygous in the genomes and homozygous reference in the exomes making up the majority of miscalls.

Human genetics can inform the biology and epidemiology of coronavirus disease 2019 (COVID-19) by pinpointing causal mechanisms that explain why some individuals become more severely affected by the disease upon infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Large-scale genetic association studies, encompassing both rare and common genetic variants, have used different study designs and multiple disease phenotype definitions to identify several genomic regions associated with COVID-19. Along with a multitude of follow-up studies, these findings have increased our understanding of disease aetiology and provided routes for management of COVID-19. Important emergent opportunities include the clinical translatability of genetic risk prediction, the repurposing of existing drugs, exploration of variable host effects of different viral strains, study of inter-individual variability in vaccination response and understanding the long-term consequences of SARS-CoV-2 infection. Beyond the current pandemic, these transferrable opportunities are likely to affect the study of many infectious diseases.

Population isolates such as Finland provide benefits in genetic studies because the allelic spectrum of damaging alleles in any gene is often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%), which survived the founding bottleneck, as opposed to being distributed over a much larger number of ultra--rare variants. While this advantage is well-- established in Mendelian genetics, its value in common disease genetics has been less explored. FinnGen aims to study the genome and national health register data of 500,000 Finns, already reaching 224,737 genotyped and phenotyped participants. Given the relatively high median age of participants (63 years) and dominance of hospital-based recruitment, FinnGen is enriched for many disease endpoints often underrepresented in population-based studies (e.g., rarer immune-mediated diseases and late onset degenerative and ophthalmologic endpoints). We report here a genome-wide association study (GWAS) of 1,932 clinical endpoints defined from nationwide health registries. We identify genome--wide significant associations at 2,491 independent loci. Among these, finemapping implicates 148 putatively causal coding variants associated with 202 endpoints, 104 with low allele frequency (AFWe studied a benchmark set of 15 diseases that had previously been investigated in large genome-wide association studies. FinnGen discovery analyses were meta-analysed in Estonian and UK biobanks. We identify 30 novel associations, primarily low-frequency variants strongly enriched, in or specific to, the Finnish population and Uralic language family neighbors in Estonia and Russia.These findings demonstrate the power of bottlenecked populations to find unique entry points into the biology of common diseases through low-frequency, high impact variants. Such high impact variants have a potential to contribute to medical translation including drug discovery.

The impact of healthy aging on molecular programming of immune cells is poorly understood. Here, we report comprehensive characterization of healthy aging in human classical monocytes, with a focus on epigenomic, transcriptomic, and proteomic alterations, as well as the corresponding proteomic and metabolomic data for plasma, using healthy cohorts of 20 young and 20 older males (~27 and ~64 years old on average). For each individual, we performed eRRBS-based DNA methylation profiling, which allowed us to identify a set of age-associated differentially methylated regions (DMRs) – a novel, cell-type specific signature of aging in DNA methylome. Hypermethylation events were associated with H3K27me3 in the CpG islands near promoters of lowly-expressed genes, while hypomethylated DMRs were enriched in H3K4me1 marked regions and associated with age-related increase of expression of the corresponding genes, providing a link between DNA methylation and age-associated transcriptional changes in primary human cells.

Common genetic variants interact with environmental factors to impact risk of heritable diseases. A notable example of this is a single-nucleotide variant in the Solute Carrier Family 39 Member 8 (SLC39A8) gene encoding the missense variant A391T, which is associated with a variety of traits ranging from Parkinson's disease and neuropsychiatric disease to cardiovascular and metabolic diseases and Crohn's disease. The remarkable extent of pleiotropy exhibited by SLC39A8 A391T raises key questions regarding how a single coding variant can contribute to this diversity of clinical outcomes and what is the mechanistic basis for this pleiotropy. Here, we generate a murine model for the Slc39a8 A391T allele and demonstrate that these mice exhibit Mn deficiency in the colon associated with impaired intestinal barrier function and epithelial glycocalyx disruption. Consequently, Slc39a8 A391T mice exhibit increased sensitivity to epithelial injury and pathological inflammation in the colon. Taken together, our results link a genetic variant with a dietary trace element to shed light on a tissue-specific mechanism of disease risk based on impaired intestinal barrier integrity.

With very large sample sizes, biobanks provide an exciting opportunity to identify genetic components of complex traits. To analyze rare variants, region-based multiple-variant aggregate tests are commonly used to increase power for association tests. However, because of the substantial computational cost, existing region-based tests cannot analyze hundreds of thousands of samples while accounting for confounders such as population stratification and sample relatedness. Here we propose a scalable generalized mixed-model region-based association test, SAIGE-GENE, that is applicable to exome-wide and genome-wide region-based analysis for hundreds of thousands of samples and can account for unbalanced case–control ratios for binary traits. Through extensive simulation studies and analysis of the HUNT study with 69,716 Norwegian samples and the UK Biobank data with 408,910 White British samples, we show that SAIGE-GENE can efficiently analyze large-sample data (N > 400,000) with type I error rates well controlled. SAIGE-GENE is a scalable generalized mixed-model region-based association test that can analyze large datasets while accounting for sample relatedness and unbalanced case–control ratios for binary traits.

Advances in genomics are opening new windows into the biology of schizophrenia. Though common variants individually have small effects on disease risk, GWAS provide a powerful opportunity to explore pathways and mechanisms contributing to pathophysiology. Here, we highlight an underappreciated biological theme emerging from GWAS: the role of glycosylation in schizophrenia. The strongest coding variant in schizophrenia GWAS is a missense mutation in the manganese transporter SLC39A8, which is associated with altered glycosylation patterns in humans. Furthermore, variants near several genes encoding glycosylation enzymes are unambiguously associated with schizophrenia: FUT9, MAN2A1, TMTC1, GALNT10, and B3GAT1. Here, we summarize the known biological functions, target substrates, and expression patterns of these enzymes as a primer for future studies. We also highlight a subset of schizophrenia-associated proteins critically modified by glycosylation including glutamate receptors, voltage-gated calcium channels, the dopamine D2 receptor, and complement glycoproteins. We hypothesize that common genetic variants alter brain glycosylation and play a fundamental role in the development of schizophrenia. Leveraging these findings will advance our mechanistic understanding of disease and may provide novel avenues for treatment development.

Gastrointestinal infections can be life threatening, but not much is known about the host’s genetic contribution to susceptibility to gastrointestinal infections or the latter’s association with psychiatric disorders. We utilized iPSYCH, a genotyped population-based sample of individuals born between 1981 and 2005 comprising 65,534 unrelated Danish individuals (45,889 diagnosed with mental disorders and 19,645 controls from a random population sample) in which all individuals were linked utilizing nationwide population-based registers to estimate the genetic contribution to susceptibility to gastrointestinal infections, identify genetic variants associated with gastrointestinal infections, and examine the link between gastrointestinal infections and psychiatric and neurodevelopmental disorders. The SNP heritability of susceptibility to gastrointestinal infections ranged from 3.7% to 6.4% on the liability scale. Significant correlations were found between gastrointestinal infections and the combined group of mental disorders (OR = 2.09; 95% CI: 1.82–2.4, P = 1.87 × 10–25). Correlations with autism spectrum disorder, attention deficit hyperactivity disorder, and depression were also significant. We identified a genome-wide significant locus associated with susceptibility to gastrointestinal infections (OR = 1.13; 95% CI: 1.08–1.18, P = 2.9 × 10–8), where the top SNP was an eQTL for the ABO gene. The risk allele was associated with reduced ABO expression, providing, for the first time, genetic evidence to support previous studies linking the O blood group to gastrointestinal infections. This study also highlights the importance of integrative work in genetics, psychiatry, infection, and epidemiology on the road to translational medicine.

Protein-coding de novo mutations (DNMs) are significant risk factors in many neurodevelopmental disorders, whereas schizophrenia (SCZ) risk associated with DNMs has thus far been shown to be modest. We analyzed DNMs from 1,695 SCZ-affected trios and 1,077 published SCZ-affected trios to better understand the contribution to SCZ risk. Among 2,772 SCZ probands, exome-wide DNM burden remained modest. Gene set analyses revealed that SCZ DNMs were significantly concentrated in genes that were highly expressed in the brain, that were under strong evolutionary constraint and/or overlapped with genes identified in other neurodevelopmental disorders. No single gene surpassed exome-wide significance; however, 16 genes were recurrently hit by protein-truncating DNMs, corresponding to a 3.15-fold higher rate than the mutation model expectation (permuted 95% confidence interval: 1-10 genes; permuted P = 3 × 10-5). Overall, DNMs explain a small fraction of SCZ risk, and larger samples are needed to identify individual risk genes, as coding variation across many genes confers risk for SCZ in the population.

Contains fulltext : 287469.pdf (Publisher’s version ) (Open Access) Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental conditions, with considerable overlap in their genetic etiology. We dissected their shared and distinct genetic etiology by cross-disorder analyses of large datasets. We identified seven loci shared by the disorders and five loci differentiating them. All five differentiating loci showed opposite allelic directions in the two disorders and significant associations with other traits, including educational attainment, neuroticism and regional brain volume. Integration with brain transcriptome data enabled us to identify and prioritize several significantly associated genes. The shared genomic fraction contributing to both disorders was strongly correlated with other psychiatric phenotypes, whereas the differentiating portion was correlated most strongly with cognitive traits. Additional analyses revealed that individuals diagnosed with both ASD and ADHD were double-loaded with genetic predispositions for both disorders and showed distinctive patterns of genetic association with other traits compared with the ASD-only and ADHD-only subgroups. These results provide insights into the biological foundation of the development of one or both conditions and of the factors driving psychopathology discriminatively toward either ADHD or ASD.

Contains fulltext : 248656.pdf (Publisher’s version ) (Closed access) BACKGROUND: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. METHODS: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH. RESULTS: Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10(-8)), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10(-6)) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10(-7); rs73033497, p = 8.8 × 10(-7); rs7914279, p = 6.4 × 10(-7)), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10(-7)) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10(-7)), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10(-7)) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05). CONCLUSIONS: In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels.

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene–phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene–phenotype associations.

SUMMARYIdentifying individuals at high risk of heart failure during precursor stages could allow for earlier initiation of treatments to modify disease progression. We performed a GWAS meta- analysis to generate a heart failure (HF) polygenic risk score (PRS) then tested the association with phenotypic subtypes (reduced ejection fraction [HFrEF] and preserved ejection fraction [HFpEF]) to evaluate the value of polygenic risk prediction. Results from the European-ancestry analysis showed that an ancestry-matched PRS, calculated from GBMI meta-analysis outperformed the previous HF GWAS (HERMES), yielding an adjusted odds ratio (aOR) of 2.27 (95% CI: 2.05-2.51; p: 1.76×10−56) from GBMI compared to 1.30 (95% CI: 1.18-1.44; p: 1.42×10− 7) from HERMES, and 1.49 (95% CI: 1.33-1.66; p: 8.38×10−13) compared to 1.17 (95% CI: 1.05- 1.31; p: 0.004) for HFrEF and HFpEF, respectively. Next, we evaluated the performance differences between ancestry-matched and multi-ancestry PRS in the African American cohort. The GBMI multi-ancestry GWAS-based PRS had a significant aOR of 1.49 (p: 0.006). Findings suggest that a PRS for heart failure derived from the GBMI multi-ancestry study is useful in predicting HFrEF, but less powerful in predicting HFpEF in an independent cohort. The difficulty in predicting HFpEF could result from the GBMI HF phenotype, preferencing HFrEF over HFpEF, and/or greater genetic heterogeneity in the HFpEF phenotype.

SummaryAsthma is a complex disease that affects millions of people and varies in prevalence by an order of magnitude across geographic regions and populations. However, the extent to which genetic variation contributes to these disparities is unclear, as studies probing the genetics of asthma have been primarily limited to populations of European (EUR) descent. As part of the Global Biobank Meta-analysis Initiative (GBMI), we conducted the largest genome-wide association study of asthma to date (153,763 cases and 1,647,022 controls) via meta-analysis across 18 biobanks spanning multiple countries and ancestries. Altogether, we discovered 179 genome-wide significant loci (p < 5×10−8) associated with asthma, 49 of which are not previously reported. We replicate well-known associations such as IL1RL1 and STAT6, and find that overall the novel associations have smaller effects than previously-discovered loci, highlighting our substantial increase in statistical power. Despite the considerable range in prevalence of asthma among biobanks, from 3% to 24%, the genetic effects of associated loci are largely consistent across the biobanks and ancestries. To further investigate the polygenic architecture of asthma, we construct polygenic risk scores (PRS) using a multi-ancestry approach, which yields higher predictive power for asthma in non-EUR populations compared to PRS derived from previous asthma meta-analyses. Additionally, we find considerable genetic overlap between asthma age-of-onset subtypes, as well as between asthma and chronic obstructive pulmonary disease (COPD) but minimal overlap in enriched biological pathways. Our work underscores the multifactorial nature of asthma development and offers insight into the shared genetic architecture of asthma that may be differentially perturbed by environmental factors and contribute to variation in prevalence.

SUMMARYThe Global Biobank Meta-analysis Initiative (GBMI), through its genetic and demographic diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ethnic study populations across biobanks, the GBMI provides a distinct set of challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWAS) are a popular tool to boost detection power for and provide biological context to genetic associations by integrating single nucleotide polymorphism to trait (SNP-trait) associations from genome-wide association studies (GWAS) with SNP-based predictive models of gene expression. TWAS presents unique challenges beyond GWAS, especially in a multi-biobank and meta-analytic setting like the GBMI. In this work, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity and meta-analytic strategies, as well as open challenges at every step of the framework. Our work provides a strong foundation for adding tissue-specific gene expression context to biobank-linked genetic association studies, allowing for ancestry-aware discovery to accelerate genomic medicine.

SummaryBiobanks are being established across the world to understand the genetic, environmental, and epidemiological basis of human diseases with the goal of better prevention and treatments. Genome-wide association studies (GWAS) have been very successful at mapping genomic loci for a wide range of human diseases and traits, but in general, lack appropriate representation of diverse ancestries - with most biobanks and preceding GWAS studies composed of individuals of European ancestries. Here, we introduce the Global Biobank Meta-analysis Initiative (GBMI) -- a collaborative network of 19 biobanks from 4 continents representing more than 2.1 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWAS generated using harmonized genotypes and phenotypes from member biobanks. GBMI brings together results from GWAS analysis across 6 main ancestry groups: approximately 33,000 of African ancestry either from Africa or from admixed-ancestry diaspora (AFR), 18,000 admixed American (AMR), 31,000 Central and South Asian (CSA), 341,000 East Asian (EAS), 1.4 million European (EUR), and 1,600 Middle Eastern (MID) individuals. In this flagship project, we generated GWASs from across 14 exemplar diseases and endpoints, including both common and less prevalent diseases that were previously understudied. Using the genetic association results, we validate that GWASs conducted in biobanks worldwide can be successfully integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics between biobanks. We demonstrate the value of this collaborative effort to improve GWAS power for diseases, increase representation, benefit understudied diseases, and improve risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of the studied traits.

Identifying Mendelian diseases with recessive inheritance is challenging as the majority of cases are caused by compound heterozygous genotypes which require sequencing data in families to definitively identify. Bottleneck events, such as in the Finnish population, enrich specific homozygous variants to higher frequencies and thus facilitate identification of disease associations through easily recognized homozygous genotypes. Here, we study homozygous and heterozygous effects of 82,516 coding variants on 2,444 disease endpoints using nationwide electronic health record (EHR) data of 176,899 Finns. We find known and novel associations to homozygous genotypes across a broad spectrum of phenotypes such as retinal dystrophy, adult-onset cataract and female infertility (13/20 of which would have been missed by the traditional additive GWAS model). With these results, and supporting simulations, we demonstrate the added benefit of homozygous scans in GWAS. We further use these results to explore inheritance patterns of known Mendelian variants. We find many Mendelian variants whose inheritance cannot be adequately described with the traditional definition of dominant or recessive. In particular, we find disease risk in heterozygous carriers of variants known to cause disease with recessive inheritance, as well as for variants labeled benign in ClinVar. Our results demonstrate how biobank efforts, particularly in founder populations, can broaden our understanding of the impact of genetic variants.

Genome-wide association studies (GWAS) have identified thousands of genetic variants linked to the risk of human disease. However, GWAS have thus far remained largely underpowered to identify associations in the rare and low frequency allelic spectrum and have lacked the resolution to trace causal mechanisms to underlying genes. Here, we combined whole exome sequencing in 392,814 UK Biobank participants with imputed genotypes from 260,405 FinnGen participants (653,219 total individuals) to conduct association meta-analyses for 744 disease endpoints across the protein-coding allelic frequency spectrum, bridging the gap between common and rare variant studies. We identified 975 associations, with more than one-third of our findings not reported previously. We demonstrate population-level relevance for mutations previously ascribed to causing single-gene disorders, map GWAS associations to likely causal genes, explain disease mechanisms, and systematically relate disease associations to levels of 117 biomarkers and clinical-stage drug targets. Combining sequencing and genotyping in two population biobanks allowed us to benefit from increased power to detect and explain disease associations, validate findings through replication and propose medical actionability for rare genetic variants. Our study provides a compendium of protein-coding variant associations for future insights into disease biology and drug discovery.

Introduction Prehypertension is a medical classification for patients with above normal blood pressure (BP) but insufficient for confirming hypertension diagnosis. Condition is often asymptomatic as it gradually develops over the years, yet recent meta-analyses suggest that prehypertension is a significant risk factor for stroke and other cardiovascular diseases. Methods We analyzed phenotypic data from 879 (age 25–64) individuals without hypertension from a population based-sampling cohort of St. Petersburg region in the North-West of Russia to identify clinical risk factors associated with prehypertension condition. All patients were divided in two groups – optimal BP (systolic BP Results Phenotypic analysis with linear regression was corrected for age, sex, smoking status, BMI and levels of LDL and HDL cholesterol (Figure 1). Interestingly, blood level of insulin was significantly associated with prehypertension status along with insulin resistance index, however, presence of diabetes diagnosis in medical history was not significant. Phenotypes associated with prehypertension suggest that prehypertension is often developing along with hyperinsulinemia. Finally, we estimated polygenic risk scores (PRS) for hypertension using UK biobank GWAS summary statistics and confirmed that prehypertension is more frequent at earlier age in patients with higher genetic susceptibility (Figure 1, Figure 2). Conclusions Hyperinsulinemia and genetic susceptibility to hypertension are strong risk factors for prehypertension. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This work was financially supported by the Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075-15-2020-901) to Al.K. Figure 1. Clinical markers of prehypertensionFigure 2. Genetic markers of prehypertension

To study how the 22q11.2 deletion predisposes to psychiatric disease, we generated induced pluripotent stem cells from deletion carriers and controls, as well as utilized CRISPR/Cas9 to introduce the heterozygous deletion into a control cell line. Upon differentiation into neural progenitor cells, we found the deletion acted in trans to alter the abundance of transcripts associated with risk for neurodevelopmental disorders including Autism Spectrum Disorder. In more differentiated excitatory neurons, altered transcripts encoded presynaptic factors and were associated with genetic risk for schizophrenia, including common (per-SNP heritability p (τc)= 4.2 x 10-6) and rare, loss of function variants (p = 1.29×10-12). These findings suggest a potential relationship between cellular states, developmental windows and susceptibility to psychiatric conditions with different ages of onset. To understand how the deletion contributed to these observed changes in gene expression, we developed and applied PPItools, which identifies the minimal protein-protein interaction network that best explains an observed set of gene expression alterations. We found that many of the genes in the 22q11.2 interval interact in presynaptic, proteasome, and JUN/FOS transcriptional pathways that underlie the broader alterations in psychiatric risk gene expression we identified. Our findings suggest that the 22q11.2 deletion impacts genes and pathways that may converge with risk loci implicated by psychiatric genetic studies to influence disease manifestation in each deletion carrier.

The invasive Japanese stiltgrass (Microstegium vimineum) affects a wide range of ecosystems and threatens biodiversity across the eastern USA. However, the mechanisms underlying rapid adaptation, plasticity, and epigenetics in the invasive range are largely unknown. We present a chromosome-level assembly for M. vimineum to investigate genome dynamics, evolution, adaptation, and the genomics of phenotypic plasticity. We generated a 1.12 Gb genome with scaffold N50 length of 53.44 Mb respectively, taking a de novo assembly approach that combined PacBio and Dovetail Genomics Omni-C sequencing. The assembly contains 23 pseudochromosomes, representing 99.96% of the genome. BUSCO assessment indicated that 80.3% of Poales gene groups are present in the assembly. The genome is predicted to contain 39,604 protein-coding genes, of which 26,288 are functionally annotated. Furthermore, 66.68% of the genome is repetitive, of which unclassified (35.63%) and long terminal repeat (LTR) retrotransposons (26.90%) are predominant. Similar to other grasses, Gypsy (41.07%) and Copia (32%) are the most abundant LTR-retrotransposon families. The majority of LTR-retrotransposons are derived from a significant expansion in the past 1-2 million years, suggesting the presence of relatively young LTR-retrotransposon lineages. We find corroborating evidence from Ks plots for a stiltgrass-specific duplication event, distinct from the more ancient grass-specific duplication event. The assembly and annotation of M. vimineum will serve as an essential genomic resource facilitating studies of the invasion process, the history and consequences of polyploidy in grasses, and provides a crucial tool for natural resource managers.SignificanceThe current lack of genomic resources for the invasive Japanese stiltgrass—and thousands of other invasive species globally—severely limits our understanding of the invasion process and hinders decision-making for effective management and control. In this study, we present a chromosome-level genome assembly and annotation of Japanese stiltgrass, a problematic weed in eastern North America, identifying a clear history of polyploidy and recent activity of transposable elements. The ultimate goal is to advance genomic studies to better understand the dynamics of non-native species during the various invasion phases, thereby providing insights into effective control strategies to manage current and future invasions.

Despite the great success of genome-wide association studies (GWAS) in identifying genetic loci significantly associated with diseases, the vast majority of causal variants underlying disease-associated loci have not been identified1–3. To create an atlas of causal variants, we performed and integrated fine-mapping across 148 complex traits in three large-scale biobanks (BioBank Japan4,5, FinnGen6, and UK Biobank7,8; total n = 811,261), resulting in 4,518 variant-trait pairs with high posterior probability (> 0.9) of causality. Of these, we found 285 high-confidence variant-trait pairs replicated across multiple populations, and we characterized multiple contributors to the surprising lack of overlap among fine-mapping results from different biobanks. By studying the bottlenecked Finnish and Japanese populations, we identified 21 and 26 putative causal coding variants with extreme allele frequency enrichment (> 10-fold) in these two populations, respectively. Aggregating data across populations enabled identification of 1,492 unique fine-mapped coding variants and 176 genes in which multiple independent coding variants influence the same trait (i.e., with an allelic series of coding variants). Our results demonstrate that fine-mapping in diverse populations enables novel insights into the biology of complex traits by pinpointing high-confidence causal variants for further characterization.

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes

Funding Information: The work of the contributing biobanks was supported by numerous grants from governmental and charitable bodies. Biobank-specific acknowledgments and more detailed acknowledgments are included in Data S2. Initiative management, S.B.C. J.C. N.J.C. M.J.D. E.E.K. A.R.M. B.M.N. Y.O. A.V.P. D.A.v.H. R.G.W. C.J.W. W.Z. and S.Z.; individual biobank analysis, A.B. Y.B. B.M.B. C.D.B. S.C. T.-T.C. K.C. S.M.D. M.D. G.H.d.B. Y.D. N.J.D. M.-J.F. Y.-C.A.F. S.F. V.L.F. L.G.F. E.R.G. T.R.G. D.H.G. C.R.G. G.G.-A. S.E.G. L.A.G. C.H. J.B.H. W.E.H. H.H. K.H. N.I. A.I. R.J. M. Kurki, J.K. N.K. E.E.K. J.T.K. M. Kanai, T.L. K.L. M.H.L. S.L. K.L. Y.-F.L. V.L.F. R.J.F.L. E.A.L.-M. A.R.-M. S.M.-G. R.M. R.E.M. H.C.M. A.R.M. Y.M. H.M. S.E.M. I.Y.M. B.M. S.M. K.N. S.N. M.A.N.-A. K.N. Y.O. P.P. A.L.-P. A.P. B.P. S.P. M.H.P. D.J.R. N.R. M.D.R. A.R. C.S. S.S. S.S.S. J.A.S. P.S. I.S. T.T. R.T. K.T. J.U. D.A.v.H. B.V. M.V. Y.V. J.M.V. R.G.W. Y.W. S.J.W. B.N.W. K.-H.H.W. M.Z. X.Z. and S.Z.; individual biobank management, N.A. A.A.T. K.M.A.-D. P.A. K.C.B. M. Boehnke, M. Boezen, C.D.B. A.C. Z.C. C.-Y.C. J.C. N.J.C. S.M.D. S.F. Y.-C.A.F. S.F. E.F. T.G. C.R.G. C.J.G. Y.G. H.H. K.A.H. K.H. S.I.I. N.M.J. N.K. E.E.K. J.T.K. C.L. M.H.L. M.T.M.L. L.L. K.L. Y.-F.L. R.J.F.L. J.L. S.M. Y.M. K.M. I.Y.M. Y.O. C.M.O. A.V.P. B.P. D.J.P. D.J.R. M.D.R. S.S. J.W.S. H.S. K.S. T.T. U.T. R.C.T. D.A.v.H. M.V. R.G.W. D.C.W. C.W. J.W. M.Z. X.Z. and S.Z.; study design and interpretation of results, A.B. M. Boehnke, M. Boezen, B.M.B. T.-T.C. C.-Y.C. M.J.D. G.D.S. N.J.D. S.F. M.-J.F. H.K.F. E.R.G. A.G. T.G. J.B.H. J.H. K.H. R.J. M.K. E.E.K. T.K. C.M.L. V.L.F. E.A.L.-M. A.R.M. S.N. B.M.N. C.M.O. J.J.P. B.P. N.R. H.R. J.A.S. I.S. K.T. D.A.v.H. R.G.W. Y.W. D.C.W. S.J.W. C.J.W. B.N.W. J.W. K.-H.H.W. M.Z. H.Z. J.Z. W.Z. X.Z. and S.Z.; drafted and edited the paper, A.B. M. Boehnke, M. Boezen, M.J.D. G.H.d.B. N.J.D. T.R.G. J.B.H. N.I. N.M.J. M.K. V.L.F. S.M. A.R.M. H.M. S.N. B.M.N. C.M.O. B.P. H.R. C.S. J.A.S. J.W.S. K.T. Y.W. D.C.W. C.J.W. K.-H.H.W. H.Z. J.Z. W.Z. and S.Z.; primary meta-analysis and quality control, M.J.D. H.K.F. M. Kanai, J.K. J.T.K. M. Kurki, M.M. B.M.N. C.J.W. K.-H.H.W. and W.Z.; drug discovery: S.N. T.K. K.-H.H.W. W.Z. and Y.O.; fine mapping, M. Kanai, W.Z. M.J.D. and H.K.F.; polygenic risk score, Y.W. S.N. E.A.L.-M. S.K. K.T. K.L. M. Kanai, W.Z. K.W. M.-J.F. L.B. P.A. P.D. V.L.F. R.M. Y.M. B.B. S.S. J.U. E.R.G. N.J.C. I.S. Y.O. A.R.M. and J.B.H.; proteome-wide Mendelian randomization, H.Z. H.R. A.B. G.H. G.D.S. B.M.B. W.Z. B.M.N. T.R.G. and J.Z.; transcriptome-wide association study, A.B. J.B.H. W.Z. J.Z. M. Kanai, B.P. E.R.G. and N.J.C.; asthma, K.T. W.Z. Y.W. M. Kanai, S.N. Y.O. B.M.N. M.J.D. and A.R.M.; heart failure, K.-H.H.W. N.J.D. B.N.W. I.S. S.E.G. J.B.H. N.J.C. M.P. R.J.F.L. M.J.D. B.M.N. W.Z. W.E.H. and C.J.W.; idiopathic pulmonary fibrosis, J.J.P. W.Z. M.J.D. J.T.K. N.J.C. and J.B.H.; primary open-angle glaucoma, V.L.F. A.B. W.Z. Y.W. K.L. M. Kanai, E.A.L.-M. P.S. R.T. X.Z. S.N. S.S. Y.O. N.I. S.M. H.S. I.S. C.W. A.R.M. E.R.G. N.M.J. N.J.C. and J.B.H.; stroke, I.S. K.-H.H.W. W.H. B.N.W. W.Z. J.E.H. A.P. B.B. A.H.S. M.E.G. R.G.W. K.H. C.K. S.Z. M.J.D. B.M.N. and C.J.W.; venous thromboembolism, B.N.W. I.S. K.-H.H.W. B.B. V.L.F. K.T. M.D. B.N. W.Z. J.A.S. and C.J.W. All authors reviewed the manuscript. M.J.D. is a founder of Maze Therapeutics. B.M.N. is a member of the scientific advisory board at Deep Genomics and a consultant for Camp4 Therapeutics, Takeda Pharmaceutical, and Biogen. The spouse of C.J.W. works at Regeneron Pharmaceuticals. C.-Y.C. is employed by Biogen. C.R.G. owns stock in 23andMe, Inc. T.R.G. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. E.E.K. has received speaker fees from Regeneron, Illumina, and 23andMe and is a member of the advisory board for Galateo Bio. R.E.M. has received speaker fees from Illumina and is a scientific advisor to the Epigenetic Clock Development Foundation. G.D.S. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. K.S. and U.T. are employed by deCODE Genetics/Amgen, Inc. J.Z. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. S.M. is a co-founder of and holds stock in Seonix Bio. Publisher Copyright: © 2022 Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Extracellular acidification occurs in inflamed tissue and the tumor microenvironment; however, a systematic study on how pH sensing contributes to tissue homeostasis is lacking. Here, we examine cell type-specific roles of the pH sensor GPR65 and its inflammatory disease-associated I231L coding variant in inflammation control. GPR65 I231L knock-in mice are highly susceptible to both bacterial infection-induced and T cell-driven colitis. Mechanistically, GPR65 I231L elicits a cytokine imbalance through impaired Th17 and Th22 differentiation and IL-22 production in association with altered cellular metabolism controlled through the cAMP-CREB-DGAT1 axis. In dendritic cells, GPR65 I231L elevates IL-12 and IL-23 release at acidic pH and alters endo-lysosomal fusion and degradation capacity, resulting in enhanced antigen presentation. This study highlights GPR65 I231L as a multistep risk factor in intestinal inflammation and illuminates a mechanism by which pH sensing controls inflammatory circuits and tissue homeostasis.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder, with onset in childhood (“childhood ADHD”), and around two thirds of affected individuals will continue to have ADHD symptoms in adulthood (“persistent ADHD”). Age at first diagnosis can vary, and sometimes ADHD is first diagnosed in adulthood (“late-diagnosed ADHD”).In this study, we analyzed a large Danish population-based case-cohort generated by iPSYCH in order to identify common genetic risk loci and perform in-depth characterization of the polygenic architecture of childhood (N=14,878), persistent (N=1,473) and late-diagnosed ADHD (N=6,961) alongside 38,303 controls. Additionally, the burden of rare protein truncating variants in the three groups were evaluated in whole-exome sequencing data from a subset of the individuals (7,650 ADHD cases and 8,649 controls). We identified genome-wide significant loci associated with childhood ADHD (four loci) and late-diagnosed ADHD (one locus). In analyses of the polygenic architecture, we found higher polygenic score (PGS) of ADHD risk variants in persistent ADHD (mean PGS=0.41) compared to childhood (mean PGS=0.26) and late-diagnosed ADHD (mean PGS=0.27), and we found a significant decreased genetic correlation of late-diagnosed ADHD with inattention (rg=0.57) compared to childhood ADHD (rg=0.86). These results suggest that a higher ADHD polygenic risk burden is associated with persistence of symptoms, and that a later diagnosis of ADHD could be due in part to genetic factors. Additionally, childhood ADHD demonstrated both a significantly increased genetic overlap with autism compared to late-diagnosed ADHD as well as the highest burden of rare protein-truncating variants in highly constrained genes among ADHD subgroups (compared to controls: beta=0.13, P=2.41×10−11). Late-diagnosed ADHD demonstrated significantly larger genetic overlap with depression than childhood ADHD and no increased burden in rare protein-truncating variants (compared to controls: beta=0.06). Overall, our study finds genetic heterogeneity among ADHD subgroups and suggests that genetic factors influence time of first ADHD diagnosis, persistence of ADHD and comorbidity patterns in the sub-groups.

Background Inflammatory bowel diseases (IBDs) are characterized by chronic inflammation and tissue damages in limited segments of the digestive tract. Pathogenesis in the tissue and mucosal inflammation probably differs according to disease location. Our aim was to further analyze transcriptomic profiles in different locations of IBD, differentiating ulcerative colitis (UC), colonic Crohn’s disease (CD), ileal CD, and pouchitis, with respect to normal colonic and ileal mucosa. We thus performed a meta-analysis focusing on specific transcriptomic signatures of ileal and colonic diseases. Methods We identified 5 cohorts with available transcriptomic data in ileal or colonic samples from active IBD and non-IBD control samples. The meta-analysis was performed on 1047 samples. In each cohort separately, we compared gene expression in CD ileitis and normal ileum; in CD colitis, UC, and normal colon; and finally in pouchitis and normal ileum. Results We identified specific markers of ileal (FOLH1, CA2) and colonic (REG3A) inflammation and showed that, with disease, some cells from the ileum start to express colonic markers. We confirmed by immunohistochemistry that these markers were specifically present in ileal or colonic diseases. We highlighted that, overall, colonic CD resembles UC and is distinct from ileal CD, which is in turn closer to pouchitis. Conclusions We demonstrated that ileal and colonic diseases exhibit specific signatures, independent of their initial clinical classification. This supports molecular, rather than clinical, disease stratification, and may be used to design drugs specifically targeting ileal or colonic diseases.

Whole-genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery not addressed by the traditional one variant, one phenotype association study. Here, we introduce a Bayesian model comparison approach called MRP (multiple rare variants and phenotypes) for rare-variant association studies that considers correlation, scale, and direction of genetic effects across a group of genetic variants, phenotypes, and studies, requiring only summary statistic data. We apply our method to exome sequencing data (n = 184,698) across 2,019 traits from the UK Biobank, aggregating signals in genes. MRP demonstrates an ability to recover signals such as associations between PCSK9 and LDL cholesterol levels. We additionally find MRP effective in conducting meta-analyses in exome data. Non-biomarker findings include associations between MC1R and red hair color and skin color, IL17RA and monocyte count, and IQGAP2 and mean platelet volume. Finally, we apply MRP in a multi-phenotype setting; after clustering the 35 biomarker phenotypes based on genetic correlation estimates, we find that joint analysis of these phenotypes results in substantial power gains for gene-trait associations, such as in TNFRSF13B in one of the clusters containing diabetes- and lipid-related traits. Overall, we show that the MRP model comparison approach improves upon useful features from widely used meta-analysis approaches for rare-variant association analyses and prioritizes protective modifiers of disease risk.

A primary challenge in understanding disease biology from genome-wide association studies (GWAS) arises from the inability to directly implicate causal genes from association data. Integration of multiple-omics data sources potentially provides important functional links between associated variants and candidate genes. Machine-learning is well-positioned to take advantage of a variety of such data and provide a solution for the prioritization of disease genes. Yet, classical positive-negative classifiers impose strong limitations on the gene prioritization procedure, such as a lack of reliable non-causal genes for training. Here, we developed a novel gene prioritization tool—Gene Prioritizer (GPrior). It is an ensemble of five positive-unlabeled bagging classifiers (Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Adaptive Boosting), that treats all genes of unknown relevance as an unlabeled set. GPrior selects an optimal composition of algorithms to tune the model for each specific phenotype. Altogether, GPrior fills an important niche of methods for GWAS data post-processing, significantly improving the ability to pinpoint disease genes compared to existing solutions.

UK Biobank has released the whole-exome sequencing (WES) data for 200,000 participants, but the best practices remain unclear for rare variant tests, and an existing approach, SAIGE-GENE, can have inflated type I error rates with high computation cost. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency compared to SAIGE-GENE. In the analysis of UKBB WES data of 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene-phenotype associations. In addition, we showed that incorporating multiple MAF cutoffs and functional annotations can help identify novel gene-phenotype associations and SAIGE-GENE+ can facilitate this.

Inflammatory and infectious upper respiratory diseases (IURDs; ICD-10: J30-J39), such as diseases of sinonasal tract, pharynx and larynx, are growing health problems yet their genomic similarity is not known. We analyzed genome-wide association to eight IURDs (61,195 cases) among 260,405 FinnGen participants, meta-analyzing diseases in four groups based on an underlying genetic correlation structure. We aimed to understand which genetic loci contribute to susceptibility to IURDs in general and its subtypes. We detected 59 independent genome-wide significant (GWS) loci, distinguishing impact on sinonasal or pharyngeal diseases, or both. Fine-mapping implicated non-synonymous variants in 16 genes, including 10 linked to immune-related diseases. Phenome-wide analysis implicated asthma and atopic dermatitis at sinonasal disease loci and inflammatory bowel diseases, and other immune-mediated disorders at pharyngeal disease loci. IURDs also genetically correlated with autoimmune diseases such as rheumatoid arthritis, autoimmune hypothyroidism, and psoriasis. Finally, we associated separate gene pathways in sinonasal and pharyngeal diseases that both contribute to type 2 immunological reaction. We show shared heritability among IURDs that extends to several immune-mediated diseases with diverse mechanisms, such as type 2 high inflammation.

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene-phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene-phenotype associations.

Background The uterine cervix has an important role in female reproductive health, but not much is known about the genetic determinants of cervical biology and pathology. Genome-wide association studies (GWAS) with increasing sample sizes have reported preliminary genetic associations for cervical cancer. However, GWAS is only the first step in mapping the genetic susceptibility and thus, the underlying biology in cervical cancer and other cervical phenotypes is still not entirely understood. Here, we use data from large biobanks to characterise the genetics of cervical phenotypes (including cervical cancer) and leverage latest computational methods and gene expression data to refine the association signals for cervical cancer. Methods Using Estonian Biobank and FinnGen data, we characterise the genetic signals associated with cervical ectropion (10,162 cases/151,347 controls), cervicitis (19,285/185,708) and cervical dysplasia (14,694/150,563). We present the results from the largest trans-ethnic GWAS meta-analysis of cervical cancer, including up to 9,229 cases and 490,304 controls from Estonian Biobank, the FinnGen study, the UK Biobank and Biobank Japan. We combine GWAS results with gene expression data and chromatin regulatory annotations in HeLa cervical carcinoma cells to propose the most likely candidate genes and causal variants for every locus associated with cervical cancer. We further dissect the HLA association with cervical pathology using imputed data on alleles and amino acid polymorphisms. Results We report a single associated locus on 2q13 for both cervical ectropion (rs3748916, p=5.1 × 10−16) and cervicitis (rs1049137, p=3.9 × 10−10), and five signals for cervical dysplasia - HLA-B and HLA-DQA1 (rs1053726, p=9.1 × 10−9; rs36214159, 1.6 × 10−22), DAPL1 (rs12611652, p=3.2 × 10−9), PAX8 (rs1049137, p=6.4 × 10−9), and CLPTM1L (rs6866294, p=2.1 × 10−9). We identify five loci associated with cervical cancer in the trans-ethnic meta-analysis: LINC00339/CDC42 (rs2268177, p= 3.1 × 10−8), PAX8/PAX8-AS1 (rs4849177, p=9.4 × 10−15), CLPTM1L (rs27069, p=1.3 × 10−14), GSDMB (rs12603332, p=1.2 × 10−9), and HLA-DRB1/HLA-DQA1 (rs35508382, p=1.0 × 10−39). Joint analysis of dysplasia and cancer datasets revealed an association on chromosome 19 (rs425787, p=3.5 × 10−8), near CD70. Discussion Our results map PAX8/PAX8-AS1, LINC00339, CDC42, CLPTM1L, HLA-DRB1, HLA-B, and GSDMB as the most likely candidate genes for cervical cancer, which provides novel insight into cervical cancer pathogenesis and supports the role of genes involved in reproductive tract development, immune response, and cellular proliferation/apoptosis. We further show that PAX8/PAX8-AS1 has a central role in cervical biology and pathology, as it was associated with all analysed phenotypes. The detailed characterisation of association signals, together with mapping of causal variants and genes offers valuable leads for further functional studies.

Genome-wide association studies (GWAS) have identified hundreds of loci associated with Crohns disease (CD), however, as with all complex diseases, deriving pathogenic mechanisms from these non-coding GWAS discoveries has been challenging. To complement GWAS and better define actionable biological targets, we analysed sequenced data from more than 30,000 CD patients and 80,000 population controls. We observe rare coding variants in established CD susceptibility genes as well as ten genes where coding variation directly implicates the gene in disease risk for the first time.

IMPORTANCE The c.1102C>T, p.(Gln368Ter) variant in themyocilin (MYOC) gene is a known risk allele for glaucoma. It is the most common MYOC risk variant for glaucoma among individuals of European ancestry, and its prevalence is highest in Finland. Furthermore, exfoliation syndrome has high prevalence in Scandinavia, making the Finnish population ideal to study the association of the variant with different types of glaucoma. OBJECTIVES To examine the association and penetrance of MYOC p.(Gln368Ter) (rs74315329) variant with different types of glaucoma in a Finnish population. DESIGN, SETTING, AND PARTICIPANTS This genetic association study included individuals of Finnish ancestry in the FinnGen project. The participants were collected from Finnish biobanks, and the disease end points were defined using nationwide registries. The MYOC c.1102C>T variant was either directly genotyped or imputed with microarrays. Recruitment of samples to FinnGen was initiated in 2017, and data analysis was performed between December 2019 and May 2020. MAIN OUTCOMES AND MEASURES The main outcomes were odds ratios (ORs) and penetrance with different types of glaucoma and in different age groups. RESULTS A total of 218 792 individuals were included in this study (mean [SD] age 52.4 [17.5] years; 123 579 women [56.5%]), including 8591 (3.9%) with glaucoma, 3412 (1.6%) with primary open-angle glaucoma, 1515 (0.7%) with exfoliation glaucoma, 892 (0.4%) with normal-tension glaucoma, and 4766 (2.2%) with suspected glaucoma. The minor allele frequency of MYOC p.(Gln368Ter) was 0.28%. Individuals with the heterozygous variant had higher odds of primary open-angle glaucoma (OR, 3.36; 95% CI, 2.55-4.37), overall glaucoma (OR, 2.58; 95% CI, 2.12-3.13), suspected glaucoma (OR, 2.53; 95% CI, 1.93-3.26), exfoliation glaucoma (OR, 2.61; 95% CI, 1.60-4.02), and undergoing glaucoma-related operations (OR, 5.45; 95% CI, 2.95-9.28). The penetrance of heterozygous MYOC p.(Gln368Ter) was 5.2% in individuals with primary open-angle glaucoma, 9.6% in individuals with glaucoma, 5.4% in individuals with suspected glaucoma, and 1.9% in individuals with exfoliation glaucoma. There was no significant association with normal-tension glaucoma (OR, 1.69; 95% CI, 0.72-3.35). CONCLUSIONS AND RELEVANCE This genetic association study found that the MYOC p.(Gln368Ter) variant was associated with exfoliation glaucoma. The association with normal-tension glaucoma could not be replicated. These findings suggest that MYOC p.(Gln368Ter) was associated with open-angle glaucoma and exfoliation glaucoma in a Finnish population.

The exome sequences of approximately 8,000 children with autism spectrum disorder (ASD) and/or attention deficit hyperactivity disorder (ADHD) and 5,000 controls were analyzed, finding that individuals with ASD and individuals with ADHD had a similar burden of rare protein-truncating variants in evolutionarily constrained genes, both significantly higher than controls. This motivated a combined analysis across ASD and ADHD, identifying microtubule-associated protein 1A (MAP1A) as a new exome-wide significant gene conferring risk for childhood psychiatric disorders. Using samples from the Danish Neonatal Screening Biobank, children with ASD and children with ADHD were found to have similar, significantly increased rates of rare protein-truncating variants in evolutionarily constrained genes.

PurposeWe aimed to gain insight into frequencies of genetic variants in genes implicated in neurodevelopmental disorder with epilepsy (NDD+E) by investigating large cohorts of patients in a diagnostic setting.MethodsWe analyzed variants in NDD+E using epilepsy gene panel sequencing performed between 2013 and 2017 by two large diagnostic companies. We compared variant frequencies in 6,994 panels to other 8,588 recently published panels as well as exome-widede novovariants in 1,942 individuals with NDD+E and 10,937 controls.ResultsGenes with highest frequencies of ultra-rare variants in NDD+E comprisedSCN1A, KCNQ2, SCN2A, CDKL5, SCN8AandSTXBP1, concordant with the two other epilepsy cohorts we investigated. Only 46% of the analysed 262 dominant and X-linked panel genes contained ultra-rare variants in patients. Among genes with contradictory evidence of association with epilepsyCACNB4, CLCN2, EFHC1, GABRD, MAGI2andSRPX2showed equal frequencies in cases and controls.ConclusionWe show that improvement of panel design increased diagnostic yield over time, but panels still display genes with low or no diagnostic yield. With our data, we hope to improve current diagnostic NDD+E panel design and provide a resource of ultra-rare variants in individuals with NDD+E to the community.

It is challenging to estimate genetic variant burden across different subtypes of epilepsy. Herein, we used a comparative approach to assess the genetic variant burden and genotype–phenotype correlations in four most common brain lesions in patients with drug-resistant focal epilepsy. Targeted sequencing analysis was performed for a panel of 161 genes with a mean coverage of >400×. Lesional tissue was histopathologically reviewed and dissected from hippocampal sclerosis (n = 15), ganglioglioma (n = 16), dysembryoplastic neuroepithelial tumors (n = 8), and focal cortical dysplasia type II (n = 15). Peripheral blood (n = 12) or surgical tissue samples histopathologically classified as lesion-free (n = 42) were available for comparison. Variants were classified as pathogenic or likely pathogenic according to American College of Medical Genetics and Genomics guidelines. Overall, we identified pathogenic and likely pathogenic variants in 25.9% of patients with a mean coverage of 383×. The highest number of pathogenic/likely pathogenic variants was observed in patients with ganglioglioma (43.75%; all somatic) and dysembryoplastic neuroepithelial tumors (37.5%; all somatic), and in 20% of cases with focal cortical dysplasia type II (13.33% somatic, 6.67% germline). Pathogenic/likely pathogenic positive genes were disorder specific and BRAF V600E the only recurrent pathogenic variant. This study represents a reference for the genetic variant burden across the four most common lesion entities in patients with drug-resistant focal epilepsy. The observed large variability in variant burden by epileptic lesion type calls for whole exome sequencing of histopathologically well-characterized tissue in a diagnostic setting and in research to discover novel disease-associated genes.

Genetics provides two major opportunities for understanding human disease—as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.

Along with traditional effects of aging and carcinogen exposure—inherited DNA variation has substantial contribution to cancer risk. Extraordinary progress made in analysis of common variation with GWAS methodology does not provide sufficient resolution to understand rare variation. To fulfill missing classification for rare germline variation we assembled dataset of whole exome sequences from>2000 patients (selected cases tested negative for candidate genes and unselected cases) with different types of cancers (breast cancer, colon cancer, and cutaneous and ocular melanomas) matched to more than 7000 non-cancer controls and analyzed germline variation in known cancer predisposing genes to identify common properties of disease-associated DNA variation and aid the future searches for new cancer susceptibility genes. Cancer predisposing genes were divided into non-overlapping classes according to the mode of inheritance of the related cancer syndrome or known tumor suppressor activity. Out of all classes only genes linked to dominant syndromes presented significant rare germline variants enrichment in cases. Separate analysis of protein-truncating and missense variation in this list of genes confirmed significant prevalence of protein-truncating variants in cases only in loss-of-function tolerant genes (pLI 0.9). In addition to findings in genetically enriched cases, we observed significant burden of rare variation in unselected cases, suggesting substantial role of inherited variation even in relatively late cancer manifestation. Taken together, our findings provide reference for distribution and types of DNA variation underlying inherited predisposition to some common cancer types.

Human genetic studies have long been vastly Eurocentric, raising a key question about the generalizability of these study findings to other populations. Because humans originated in Africa, these populations retain more genetic diversity, and yet individuals of African descent have been tremendously underrepresented in genetic studies. The diversity in Africa affords ample opportunities to improve fine-mapping resolution for associated loci, discover novel genetic associations with phenotypes, build more generalizable genetic risk prediction models, and better understand the genetic architecture of complex traits and diseases subject to varying environmental pressures. Thus, it is both ethically and scientifically imperative that geneticists globally surmount challenges that have limited progress in African genetic studies to date while meaningfully including African investigators, as greater inclusivity and enhanced research capacity affords enormous opportunities to accelerate genomic discoveries that translate more effectively to all populations. We review the advantages and challenges of studying the genetic architecture of complex traits and diseases in Africa. For example, with greater genetic diversity comes greater ancestral heterogeneity; this higher level of understudied diversity can yield novel genetic findings, but some methods that assume homogeneous population structure and work well in European populations may work less well in the presence of greater diversity and heterogeneity in African populations. Consequently, we advocate for methodological development that will accelerate studies important for all populations, especially those currently underrepresented in genetics.

Genome-wide association studies (GWAS) have identified thousands of genetic variants linked to the risk of human disease. However, GWAS have so far remained largely underpowered in relation to identifying associations in the rare and low-frequency allelic spectrum and have lacked the resolution to trace causal mechanisms to underlying genes