Evidence for the additivity of rare and common variant burden throughout the spectrum of intellectual disability.

Intellectual disability (ID) is a common disorder, yet there is a wide spectrum of impairment from mild to profoundly affected individuals. Mild ID is seen as the low extreme of the general distribution of intelligence, while severe ID is often seen as a monogenic disorder caused by rare, pathogenic, highly penetrant variants. To investigate the genetic factors influencing mild and severe ID, we evaluated rare and common variation in the Northern Finland Intellectual Disability cohort (n = 1096 ID patients), a cohort with a high percentage of mild ID (n = 550) and from a population bottleneck enriched in rare, damaging variation. Despite this enrichment, we found only a small percentage of ID was due to recessive Finnish-enriched variants (0.5%). A larger proportion was linked to dominant variation, with a significant burden of rare, damaging variation in both mild and severe ID. This rare variant burden was enriched in more severe ID (p = 2.4e-4), patients without a relative with ID (p = 4.76e-4), and in those with features associated with monogenic disorders. We also found a significant burden of common variants associated with decreased cognitive function, with no difference between mild and more severe ID. When we included common and rare variants in a joint model, the rare and common variants had additive effects in both mild and severe ID. A multimodel inference approach also found that common and rare variants together best explained ID status (ΔAIC = 16.8, ΔBIC = 10.2). Overall, we report evidence for the additivity of rare and common variant burden throughout the spectrum of intellectual disability.

Distinct and shared genetic architectures of gestational diabetes mellitus and type 2 diabetes.

Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting more than 16 million pregnancies annually worldwide1,2. GDM is related to an increased lifetime risk of type 2 diabetes (T2D)1-3, with over a third of women developing T2D within 15 years of their GDM diagnosis. The diseases are hypothesized to share a genetic predisposition1-7, but few studies have sought to uncover the genetic underpinnings of GDM. Most studies have evaluated the impact of T2D loci only8-10, and the three prior genome-wide association studies of GDM11-13 have identified only five loci, limiting the power to assess to what extent variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen study and identified 13 GDM-associated loci, including nine new loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into the following two distinct categories: one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis and placental expression.

Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses.

Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.

Novel patients with NHLRC2 variants expand the phenotypic spectrum of FINCA disease.

Purpose: FINCA disease (Fibrosis, Neurodegeneration and Cerebral Angiomatosis, OMIM 618278) is an infantile-onset neurodevelopmental and multiorgan disease. Since our initial report in 2018, additional patients have been described. FINCA is the first human disease caused by recessive variants in the highly conserved NHLRC2 gene. Our previous studies have shown that Nhlrc2-null mouse embryos die during gastrulation, indicating the essential role of the protein in embryonic development. Defect in NHLRC2 leads to cerebral neurodegeneration and severe pulmonary, hepatic and cardiac fibrosis. Despite having a structure suggestive of an enzymatic role and the clinical importance of NHLRC2 in multiple organs, the specific physiological role of the protein is unknown. Methods: The clinical histories of five novel FINCA patients diagnosed with whole exome sequencing were reviewed. Segregation analysis of the biallelic, potentially pathogenic NHLRC2 variants was performed using Sanger sequencing. Studies on neuropathology and NHLRC2 expression in different brain regions were performed on autopsy samples of three previously described deceased FINCA patients. Results: One patient was homozygous for the pathogenic variant c.442G > T, while the other four were compound heterozygous for this variant and two other pathogenic NHLRC2 gene variants. All five patients presented with multiorgan dysfunction with neurodevelopmental delay, recurrent infections and macrocytic anemia as key features. Interstitial lung disease was pronounced in infancy but often stabilized. Autopsy samples revealed widespread, albeit at a lower intensity than the control, NHLRC2 expression in the brain. Conclusion: This report expands on the characteristic clinical features of FINCA disease. Presentation is typically in infancy, and although patients can live to late adulthood, the key clinical and histopathological features are fibrosis, infection susceptibility/immunodeficiency/intellectual disability, neurodevelopmental disorder/neurodegeneration and chronic anemia/cerebral angiomatosis (hence the acronym FINCA) that enable an early diagnosis confirmed by genetic investigations.

Overlap of Genetic Loci for Central Serous Chorioretinopathy With Age-Related Macular Degeneration.

Importance: Central serous chorioretinopathy (CSC) is a serous maculopathy of unknown etiology. Two of 3 previously reported CSC genetic risk loci are also associated with AMD. Improved understanding of CSC genetics may broaden our understanding of this genetic overlap and unveil mechanisms in both diseases. Objective: To identify novel genetic risk factors for CSC and compare genetic risk factors for CSC and AMD. Design, Setting, and Participants: Using International Classification of Diseases, Ninth (ICD-9) and Tenth (ICD-10) Revision code-based inclusion and exclusion criteria, patients with CSC and controls were identified in both the FinnGen study and the Estonian Biobank (EstBB). Also included in a meta-analysis were previously reported patients with chronic CSC and controls. Data were analyzed from March 1 to September 31, 2022. Main Outcomes and Measures: Genome-wide association studies (GWASs) were performed in the biobank-based cohorts followed by a meta-analysis of all cohorts. The expression of genes prioritized by the polygenic priority score and nearest-gene methods were assessed in cultured choroidal endothelial cells and public ocular single-cell RNA sequencing data sets. The predictive utility of polygenic scores (PGSs) for CSC and AMD were evaluated in the FinnGen study. Results: A total of 1176 patients with CSC and 526 787 controls (312 162 female [59.3%]) were included in this analysis: 552 patients with CSC and 343 461 controls were identified in the FinnGen study, 103 patients with CSC and 178 573 controls were identified in the EstBB, and 521 patients with chronic CSC and 3577 controls were included in a meta-analysis. Two previously reported CSC risk loci were replicated (near CFH and GATA5) and 3 novel loci were identified (near CD34/46, NOTCH4, and PREX1). The CFH and NOTCH4 loci were associated with AMD but in the opposite direction. Prioritized genes showed increased expression in cultured choroidal endothelial cells compared with other genes in the loci (median [IQR] of log 2 [counts per million], 7.3 [0.6] vs 4.7 [3.7]; P = .004) and were differentially expressed in choroidal vascular endothelial cells in single-cell RNA sequencing data (mean [SD] fold change, 2.05 [0.38] compared with other cell types; P < 7.1 × 10-20). A PGS for AMD was predictive of reduced CSC risk (odds ratio, 0.76; 95% CI, 0.70-0.83 per +1 SD in AMD-PGS; P = 7.4 × 10-10). This association may have been mediated by loci containing complement genes. Conclusions and Relevance: In this 3-cohort genetic association study, 5 genetic risk loci for CSC were identified, highlighting a likely role for genes involved in choroidal vascular function and complement regulation. Results suggest that polygenic AMD risk was associated with reduced risk of CSC and that this genetic overlap was largely due to loci containing complement genes.

LAVAA: a lightweight association viewer across ailments.

Motivation: Biobank scale genetic associations results over thousands of traits can be difficult to visualize and navigate. Results: We have created LAVAA, a visualization web-application to generate genetic volcano plots for simultaneously considering the P-value, effect size, case counts, trait class and fine-mapping posterior probability at a single-nucleotide polymorphism (SNP) across a range of traits from a large set of genome-wide association study. We find that user interaction with association results in LAVAA can enrich and enhance the biological interpretation of individual loci. Availability and implementation: LAVAA is available as a stand-alone web service (https://geneviz.aalto.fi/LAVAA/) and will be available in future releases of the finngen.fi website starting with release 10 in late 2023.

FinnGen provides genetic insights from a well-phenotyped isolated population.

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

Genome-wide screen of otosclerosis in population biobanks: 27 loci and shared associations with skeletal structure.

Otosclerosis is one of the most common causes of conductive hearing loss, affecting 0.3% of the population. It typically presents in adulthood and half of the patients have a positive family history. The pathophysiology of otosclerosis is poorly understood. A previous genome-wide association study (GWAS) identified a single association locus in an intronic region of RELN. Here, we report a meta-analysis of GWAS studies of otosclerosis in three population-based biobanks comprising 3504 cases and 861,198 controls. We identify 23 novel risk loci (p < 5 × 10-8) and report an association in RELN and three previously reported candidate gene or linkage regions (TGFB1, MEPE, and OTSC7). We demonstrate developmental stage-dependent immunostaining patterns of MEPE and RUNX2 in mouse otic capsules. In most association loci, the nearest protein-coding genes are implicated in bone remodelling, mineralization or severe skeletal disorders. We highlight multiple genes involved in transforming growth factor beta signalling for follow-up studies.

Inflammatory and infectious upper respiratory diseases associate with 41 genomic loci and type 2 inflammation.

Inflammatory and infectious upper respiratory diseases (ICD-10: J30-J39), such as diseases of the sinonasal tract, pharynx and larynx, are growing health problems yet their genomic similarity is not known. We analyze genome-wide association to eight upper respiratory diseases (61,195 cases) among 260,405 FinnGen participants, meta-analyzing diseases in four groups based on an underlying genetic correlation structure. Aiming to understand which genetic loci contribute to susceptibility to upper respiratory diseases in general and its subtypes, we detect 41 independent genome-wide significant loci, distinguishing impact on sinonasal or pharyngeal diseases, or both. Fine-mapping implicated non-synonymous variants in nine genes, including three 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. Upper respiratory diseases 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 upper respiratory diseases that extends to several immune-mediated diseases with diverse mechanisms, such as type 2 high inflammation.

Genome-wide association study of varicose veins identifies a protective missense variant in GJD3 enriched in the Finnish population.

Varicose veins is the most common manifestation of chronic venous disease that displays female-biased incidence. To identify protein-inactivating variants that could guide identification of drug target genes for varicose veins and genetic evidence for the disease prevalence difference between the sexes, we conducted a genome-wide association study of varicose veins in Finns using the FinnGen dataset with 17,027 cases and 190,028 controls. We identified 50 associated genetic loci (P < 5.0 × 10-8) of which 29 were novel including one near ERG with female-specificity (rs2836405-G, OR[95% CI] = 1.09[1.05-1.13], P = 3.1 × 10-8). These also include two X-chromosomal (ARHGAP6 and SRPX) and two autosomal novel loci (TGFB2 and GJD3) with protein-coding lead variants enriched above 56-fold in Finns over non-Finnish non-Estonian Europeans. A low-frequency missense variant in GJD3 (p.Pro59Thr) is exclusively associated with a lower risk for varicose veins (OR = 0.62 [0.55-0.70], P = 1.0 × 10-14) in a phenome-wide scan of the FinnGen data. The absence of observed pleiotropy and its membership of the connexin gene family underlines GJD3 as a potential connexin-modulating therapeutic strategy for varicose veins. Our results provide insights into varicose veins etiopathology and highlight the power of isolated populations, including Finns, to discover genetic variants that inform therapeutic development.

Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases.

Little is known about the genetic determinants of medication use in preventing cardiometabolic diseases. Using the Finnish nationwide drug purchase registry with follow-up since 1995, we performed genome-wide association analyses of longitudinal patterns of medication use in hyperlipidemia, hypertension and type 2 diabetes in up to 193,933 individuals (55% women) in the FinnGen study. In meta-analyses of up to 567,671 individuals combining FinnGen with the Estonian Biobank and the UK Biobank, we discovered 333 independent loci (P < 5 × 10-9) associated with medication use. Fine-mapping revealed 494 95% credible sets associated with the total number of medication purchases, changes in medication combinations or treatment discontinuation, including 46 credible sets in 40 loci not associated with the underlying treatment targets. The polygenic risk scores (PRS) for cardiometabolic risk factors were strongly associated with the medication-use behavior. A medication-use enhanced multitrait PRS for coronary artery disease matched the performance of a risk factor-based multitrait coronary artery disease PRS in an independent sample (UK Biobank, n = 343,676). In summary, we demonstrate medication-based strategies for identifying cardiometabolic risk loci and provide genome-wide tools for preventing cardiovascular diseases.

Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks.

With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants (down to minor allele count 20). We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser.

Inframe insertion and splice site variants in MFGE8 associate with protection against coronary atherosclerosis.

Cardiovascular diseases are the leading cause of premature death and disability worldwide, with both genetic and environmental determinants. While genome-wide association studies have identified multiple genetic loci associated with cardiovascular diseases, exact genes driving these associations remain mostly uncovered. Due to Finland's population history, many deleterious and high-impact variants are enriched in the Finnish population giving a possibility to find genetic associations for protein-truncating variants that likely tie the association to a gene and that would not be detected elsewhere. In a large Finnish biobank study FinnGen, we identified an association between an inframe insertion rs534125149 in MFGE8 (encoding lactadherin) and protection against coronary atherosclerosis. This variant is highly enriched in Finland, and the protective association was replicated in meta-analysis of BioBank Japan and Estonian biobank. Additionally, we identified a protective association between splice acceptor variant rs201988637 in MFGE8 and coronary atherosclerosis, independent of the rs534125149, with no significant risk-increasing associations. This variant was also associated with lower pulse pressure, pointing towards a function of MFGE8 in arterial aging also in humans in addition to previous evidence in mice. In conclusion, our results suggest that inhibiting the production of lactadherin could lower the risk for coronary heart disease substantially.

The 22q11.2 region regulates presynaptic gene-products linked to schizophrenia.

It is unclear how the 22q11.2 deletion predisposes to psychiatric disease. To study this, we generated induced pluripotent stem cells from deletion carriers and controls and utilized CRISPR/Cas9 to introduce the heterozygous deletion into a control cell line. Here, we show that upon differentiation into neural progenitor cells, the deletion acted in trans to alter the abundance of transcripts associated with risk for neurodevelopmental disorders including autism. In excitatory neurons, altered transcripts encoded presynaptic factors and were associated with genetic risk for schizophrenia, including common and rare variants. To understand how the deletion contributed to these changes, we defined the minimal protein-protein interaction network that best explains gene expression alterations. We found that many genes in 22q11.2 interact in presynaptic, proteasome, and JUN/FOS transcriptional pathways. Our findings suggest that the 22q11.2 deletion impacts genes that may converge with psychiatric risk loci to influence disease manifestation in each deletion carrier.

Genetic associations of protein-coding variants in human disease.

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 genes1. 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 being previously unreported. 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 enabled 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.

A novel variant in SMG9 causes intellectual disability, confirming a role for nonsense-mediated decay components in neurocognitive development.

Biallelic loss-of-function variants in the SMG9 gene, encoding a regulatory subunit of the mRNA nonsense-mediated decay (NMD) machinery, are reported to cause heart and brain malformation syndrome. Here we report five patients from three unrelated families with intellectual disability (ID) and a novel pathogenic SMG9 c.551 T > C p.(Val184Ala) homozygous missense variant, identified using exome sequencing. Sanger sequencing confirmed recessive segregation in each family. SMG9 c.551T > C p.(Val184Ala) is most likely an autozygous variant identical by descent. Characteristic clinical findings in patients were mild to moderate ID, intention tremor, pyramidal signs, dyspraxia, and ocular manifestations. We used RNA sequencing of patients and age- and sex-matched healthy controls to assess the effect of the variant. RNA sequencing revealed that the SMG9 c.551T > C variant did not affect the splicing or expression level of SMG9 gene products, and allele-specific expression analysis did not provide evidence that the nonsense mRNA-induced NMD was affected. Differential gene expression analysis identified prevalent upregulation of genes in patients, including the genes SMOX, OSBP2, GPX3, and ZNF155. These findings suggest that normal SMG9 function may be involved in transcriptional regulation without affecting nonsense mRNA-induced NMD. In conclusion, we demonstrate that the SMG9 c.551T > C missense variant causes a neurodevelopmental disorder and impacts gene expression. NMD components have roles beyond aberrant mRNA degradation that are crucial for neurocognitive development.

Recessive Genome-Wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes.

Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 case subjects and 279,507 control subjects from 7 European-ancestry cohorts, including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five variants had minor allele frequency of <5% and were each associated with more than a doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19; P = 1 × 10-16) and a stronger effect in men than in women (for interaction, P = 7 × 10-7). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL cholesterol and a 20% increase in triglycerides; colocalization analysis linked this signal to reduced expression of the nearby PELO gene. These results demonstrate that recessive models, when compared with GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

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.