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.

Recontacting biobank participants to collect lifestyle, behavioural and cognitive information via online questionnaires: lessons from a pilot study within FinnGen.

OBJECTIVES: To recontact biobank participants and collect cognitive, behavioural and lifestyle information via a secure online platform. DESIGN: Biobank-based recontacting pilot study. SETTING: Three Finnish biobanks (Helsinki, Auria, Tampere) recruiting participants from February 2021 to July 2021. PARTICIPANTS: All eligible invitees were enrolled in FinnGen by their biobanks (Helsinki, Auria, Tampere), had available genetic data and were >18 years old. Individuals with severe neuropsychiatric disease or cognitive or physical disabilities were excluded. Lastly, 5995 participants were selected based on their polygenic score for cognitive abilities and invited to the study. Among invitees, 1115 had successfully participated and completed the study questionnaire(s). OUTCOME MEASURES: The primary outcome was the participation rate among study invitees. Secondary outcomes included questionnaire completion rate, quality of data collected and comparison of participation rate boosting strategies. RESULTS: The overall participation rate was 18.6% among all invitees and 23.1% among individuals aged 18-69. A second reminder letter yielded an additional 9.7% participation rate in those who did not respond to the first invitation. Recontacting participants via an online healthcare portal yielded lower participation than recontacting via physical letter. The completion rate of the questionnaire and cognitive tests was high (92% and 85%, respectively), and measurements were overall reliable among participants. For example, the correlation (r) between self-reported body mass index and that collected by the biobanks was 0.92. CONCLUSION: In summary, this pilot suggests that recontacting FinnGen participants with the goal to collect a wide range of cognitive, behavioural and lifestyle information without additional engagement results in a low participation rate, but with reliable data. We suggest that such information be collected at enrolment, if possible, rather than via post hoc recontacting.

NCOR2 is a novel candidate gene for migraine-epilepsy phenotype.

HYPOTHESIS: To identify genetic factors predisposing to migraine-epilepsy phenotype utilizing a multi-generational family with known linkage to chr12q24.2-q24.3. METHODS: We used single nucleotide polymorphism (SNP) genotyping and next-generation sequencing technologies to perform linkage, haplotype, and variant analyses in an extended Finnish migraine-epilepsy family (n = 120). In addition, we used a large genome-wide association study (GWAS) dataset of migraine and two biobank studies, UK Biobank and FinnGen, to test whether variants within the susceptibility region associate with migraine or epilepsy related phenotypes in a population setting. RESULTS: The family showed the highest evidence of linkage (LOD 3.42) between rs7966411 and epilepsy. The haplotype shared among 12 out of 13 epilepsy patients in the family covers almost the entire NCOR2 and co-localizes with one of the risk loci of the recent GWAS on migraine. The haplotype harbors nine low-frequency variants with potential regulatory functions. Three of them, in addition to two common variants, show nominal associations with neurological disorders in either UK Biobank or FinnGen. CONCLUSION: We provide several independent lines of evidence supporting association between migraine-epilepsy phenotype and NCOR2. Our study suggests that NCOR2 may have a role in both migraine and epilepsy and thus would provide evidence for shared pathophysiology underlying these two diseases.

An epigenome-wide association study of metabolic syndrome and its components.

The role of metabolic syndrome (MetS) as a preceding metabolic state for type 2 diabetes and cardiovascular disease is widely recognised. To accumulate knowledge of the pathological mechanisms behind the condition at the methylation level, we conducted an epigenome-wide association study (EWAS) of MetS and its components, testing 1187 individuals of European ancestry for approximately 470 000 methylation sites throughout the genome. Methylation site cg19693031 in gene TXNIP -previously associated with type 2 diabetes, glucose and lipid metabolism, associated with fasting glucose level (P = 1.80 × 10-8). Cg06500161 in gene ABCG1 associated both with serum triglycerides (P = 5.36 × 10-9) and waist circumference (P = 5.21 × 10-9). The previously identified type 2 diabetes-associated locus cg08309687 in chromosome 21 associated with waist circumference for the first time (P = 2.24 × 10-7). Furthermore, a novel HDL association with cg17901584 in chromosome 1 was identified (P = 7.81 × 10-8). Our study supports previous genetic studies of MetS, finding that lipid metabolism plays a key role in pathology of the syndrome. We provide evidence regarding a close interplay with glucose metabolism. Finally, we suggest that in attempts to identify methylation loci linking separate MetS components, cg19693031 appears to represent a strong candidate.

A genome-wide screen for acrophobia susceptibility loci in a Finnish isolate.

Acrophobia, an abnormal fear of heights, is a specific phobia characterized as apprehension cued by the occurrence or anticipation of elevated spaces. It is considered a complex trait with onset influenced by both genetic and environmental factors. Identification of genetic risk variants would provide novel insight into the genetic basis of the fear of heights phenotype and contribute to the molecular-level understanding of its aetiology. Genetic isolates may facilitate identification of susceptibility alleles due to reduced genetic heterogeneity. We took advantage of an internal genetic isolate in Finland in which a distinct acrophobia phenotype appears to be segregating in pedigrees originally ascertained for schizophrenia. We conducted parametric, nonparametric, joint linkage and linkage disequilibrium analyses using a microsatellite marker panel, genotyped in families to search for chromosomal regions correlated with acrophobia. Our results implicated a few regions with suggestive evidence for linkage on chromosomes 4q28 (LOD = 2.17), 8q24 (LOD = 2.09) and 13q21-q22 (LOD = 2.22). We observed no risk haplotypes shared between different families. These results suggest that genetic predisposition to acrophobia in this genetic isolate is unlikely to be mediated by a small number of shared high-risk alleles, but rather has a complex genetic architecture.

AMIGO-Kv2.1 Potassium Channel Complex Is Associated With Schizophrenia-Related Phenotypes.

The enormous variability in electrical properties of neurons is largely affected by a multitude of potassium channel subunits. Kv2.1 is a widely expressed voltage-dependent potassium channel and an important regulator of neuronal excitability. The Kv2.1 auxiliary subunit AMIGO constitutes an integral part of the Kv2.1 channel complex in brain and regulates the activity of the channel. AMIGO and Kv2.1 localize to the distinct somatodendritic clusters at the neuronal plasma membrane. Here we have created and characterized a mouse line lacking the AMIGO gene. Absence of AMIGO clearly reduced the amount of the Kv2.1 channel protein in mouse brain and altered the electrophysiological properties of neurons. These changes were accompanied by behavioral and pharmacological abnormalities reminiscent of those identified in schizophrenia. Concomitantly, we have detected an association of a rare, population-specific polymorphism of KV2.1 (KCNB1) with human schizophrenia in a genetic isolate enriched with schizophrenia. Our study demonstrates the involvement of AMIGO-Kv2.1 channel complex in schizophrenia-related behavioral domains in mice and identifies KV2.1 (KCNB1) as a strong susceptibility gene for schizophrenia spectrum disorders in humans.

DataSHIELD: taking the analysis to the data, not the data to the analysis.

BACKGROUND: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK's proposed 'care.data' initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. METHODS: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. RESULTS: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. CONCLUSIONS: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property-the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.

The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies.

BACKGROUND: Not all obese subjects have an adverse metabolic profile predisposing them to developing type 2 diabetes or cardiovascular disease. The BioSHaRE-EU Healthy Obese Project aims to gain insights into the consequences of (healthy) obesity using data on risk factors and phenotypes across several large-scale cohort studies. Aim of this study was to describe the prevalence of obesity, metabolic syndrome (MetS) and metabolically healthy obesity (MHO) in ten participating studies. METHODS: Ten different cohorts in seven countries were combined, using data transformed into a harmonized format. All participants were of European origin, with age 18-80 years. They had participated in a clinical examination for anthropometric and blood pressure measurements. Blood samples had been drawn for analysis of lipids and glucose. Presence of MetS was assessed in those with obesity (BMI ≥ 30 kg/m2) based on the 2001 NCEP ATP III criteria, as well as an adapted set of less strict criteria. MHO was defined as obesity, having none of the MetS components, and no previous diagnosis of cardiovascular disease. RESULTS: Data for 163,517 individuals were available; 17% were obese (11,465 men and 16,612 women). The prevalence of obesity varied from 11.6% in the Italian CHRIS cohort to 26.3% in the German KORA cohort. The age-standardized percentage of obese subjects with MetS ranged in women from 24% in CHRIS to 65% in the Finnish Health2000 cohort, and in men from 43% in CHRIS to 78% in the Finnish DILGOM cohort, with elevated blood pressure the most frequently occurring factor contributing to the prevalence of the metabolic syndrome. The age-standardized prevalence of MHO varied in women from 7% in Health2000 to 28% in NCDS, and in men from 2% in DILGOM to 19% in CHRIS. MHO was more prevalent in women than in men, and decreased with age in both sexes. CONCLUSIONS: Through a rigorous harmonization process, the BioSHaRE-EU consortium was able to compare key characteristics defining the metabolically healthy obese phenotype across ten cohort studies. There is considerable variability in the prevalence of healthy obesity across the different European populations studied, even when unified criteria were used to classify this phenotype.

PSEUDOMARKER 2.0: efficient computation of likelihoods using NOMAD.

BACKGROUND: PSEUDOMARKER is a software package that performs joint linkage and linkage disequilibrium analysis between a marker and a putative disease locus. A key feature of PSEUDOMARKER is that it can combine case-controls and pedigrees of varying structure into a single unified analysis. Thus it maximizes the full likelihood of the data over marker allele frequencies or conditional allele frequencies on disease and recombination fraction. RESULTS: The new version 2.0 uses the software package NOMAD to maximize likelihoods, resulting in generally comparable or better optima with many fewer evaluations of the likelihood functions. CONCLUSIONS: After being modified substantially to use modern optimization methods, PSEUDOMARKER version 2.0 is more robust and substantially faster than version 1.0. NOMAD may be useful in other bioinformatics problems where complex likelihood functions are optimized.

Identifying flavor preference subgroups. Genetic basis and related eating behavior traits.

Subgroups based on flavor preferences were identified and their genetic and behavior related characteristics investigated using extensive data from 331 Finnish twins (21-25years, 146 men) including 47 monozygotic (MZ) and 93 dizygotic (DZ) pairs, and 51 twin individuals. The subgroup identification (hierarchical and K-means clustering) was based on liking responses to food names representing sour, umami, and spicy flavor qualities. Furthermore, sensory tests were conducted, a questionnaire on food likes completed, and various eating behavior related traits measured with validated scales. Sensory data included intensity ratings of PROP (6-n-propylthiouracil-impregnated filter paper), hedonic and intensity responses to sourness (orange juice with and without added citric acid, 0.42%), pungency (strawberry jelly with and without added capsaicin 0.00013%) and umami ('mouthfeel flavor' taste solution). Ratings of liking of 41 general food names were categorized into salty-and-fatty, sweet-and-fatty, fruits and vegetables and fish foods. Subgroup differences (complex samples procedure) and the genetics underlying the subgroups (structural equation modeling) were investigated. Of the resulting two groups (basic, n=140, adventurous n=152; non-grouped n=39), the adventurous expressed higher liking for sour and spicy foods, and had more tolerance for capsaicin burn in the sensory-hedonic test. The adventurous were also less food neophobic (25.9±9.1 vs. 32.5±10.6, respectively) and expressed higher liking for fruits and vegetables compared to the basic group. Genetic effects were shown to underlie the subgroups (heritability 72%, CI: 36-92%). Linkage analysis for 27 candidate gene regions revealed suggestively that being adventurous is linked to TAS1R1 and PKD1L3 genes. These results indicate that food neophobia and genetic differences may form a barrier through which individual flavor preferences are generated.

Mortality rate increases steeply with nonadherence to statin therapy in patients with acute coronary syndrome.

BACKGROUND: In a prospective cohort of consecutive acute coronary syndrome (ACS) patients, we compared the adherence rate of statin usage and mortality rate during a median follow-up of 23 months. HYPOTHESIS: Adherence to statin therapy after acute coronary syndrome affects mortality rate. METHODS: We analyzed ACS patients (N = 1969; age, 65.9 ± 11.8 years; female 30.4%) who underwent angiography between March 2006 and March 2008. The postdischarge usage of statins was based on the purchase register of the Social Insurance Institution of Finland. The death rate was verified from Statistics Finland. RESULTS: At discharge, the rate of statin prescription to patients was 95.4% (n = 1878). When comparing adherent patients (n = 1200; 61.7%), who purchased the medication systematically until the end of the median 23-month follow-up, with nonadherent patients (n = 94; 4.8%), who did not use the medication at all, there was a vast difference in absolute death rate between the groups: 4.9% vs 14.9%, respectively (P < 0.001). We conducted Cox proportional hazards model with ACS type, cerebrovascular attack, diabetes, age, 3-artery disease, and cancer as adjusted confounders. Compared with regular statin users, nonusers were associated with a >2× increased hazard ratio of mortality (hazard ratio: 2.70, 95% confidence interval: 1.49-4.90, P = 0.001). CONCLUSIONS: Statin medication is essential for discharged ACS patients. They should be strongly encouraged to purchase and use it.

On the statistical properties of family-based association tests in datasets containing both pedigrees and unrelated case-control samples.

A common approach to genetic mapping of loci for complex diseases is to perform a genome-wide association study (GWAS) by analyzing a vast number of SNP markers in cohorts of unrelated cases and controls. A direct motivation for the case-control design is that unrelated, affected individuals can be easier to collect than large families with multiple affected persons in the Western world. Despite its higher potential power, investigators have not actively pursued family ascertainment in part because of a dearth of methods for analyzing such correlated data on a large scale. We examine the statistical properties of several commonly used family-based association tests, as to their performance using real-life mixtures of families and singletons taken from our own migraine and schizophrenia studies, as well as population-based data for a complex trait simulated with the evolutionary phenogenetic simulator, ForSim. In virtually every situation, the full likelihood-based methods in the PSEUDOMARKER program outperformed those implemented in FBAT, GENEHUNTER TDT, PLINK (family-based options), HRR/HHRR, QTDT, TRANSMIT, UNPHASED, MENDEL, and LAMP. We further show that GWAS is much more powerful when family samples are used rather than unrelateds, on a genotype-by-genotype basis.

On the validity of the likelihood ratio test and consistency of resulting parameter estimates in joint linkage and linkage disequilibrium analysis under improperly specified parametric models.

It has been shown that parametric analysis of linkage disequilibrium conditional on linkage using an overly deterministic model can be optimal for family-based association analysis. However, if one applies this strategy carelessly, there is a risk of false inference. We analyse properties of such likelihood ratio tests when the assumed disease mode of inheritance is inaccurate. Under some conditions, problems result if one is not careful to consider what null hypothesis is being tested. We show that: (a) tests for which the null hypothesis assumes the absence of both linkage and association are independent of the true mode of inheritance; (b) likelihood ratio tests assuming either linkage or association under the null hypothesis may depend on the true mode of inheritance, leading to inconsistent parameter estimates, in particular under extremely deterministic models; (c) this problem cannot be eliminated by increasing sample size or adding population controls--as sample size increases, the chance of false positive inference goes to 100%; (d) this issue can lead to systematic false positive inference of association in regions of linkage. This is important because highly deterministic models are often used intentionally in model-based analyses because they can have more power than the true model, and are implicit in many model-free analysis methods.

Novel susceptibility locus at 22q11 for diabetic nephropathy in type 1 diabetes.

BACKGROUND: Diabetic nephropathy (DN) affects about 30% of patients with type 1 diabetes (T1D) and contributes to serious morbidity and mortality. So far only the 3q21-q25 region has repeatedly been indicated as a susceptibility region for DN. The aim of this study was to search for new DN susceptibility loci in Finnish, Danish and French T1D families. METHODS AND RESULTS: We performed a genome-wide linkage study using 384 microsatellite markers. A total of 175 T1D families were studied, of which 94 originated from Finland, 46 from Denmark and 35 from France. The whole sample set consisted of 556 individuals including 42 sib-pairs concordant and 84 sib-pairs discordant for DN. Two-point and multi-point non-parametric linkage analyses were performed using the Analyze package and the MERLIN software. A novel DN locus on 22q11 was identified in the joint analysis of the Finnish, Danish and French families by genome-wide multipoint non-parametric linkage analysis using the Kong and Cox linear model (NPL(pairs) LOD score 3.58). Nominal or suggestive evidence of linkage to this locus was also detected when the three populations were analyzed separately. Suggestive evidence of linkage was found to six additional loci in the Finnish and French sample sets. CONCLUSIONS: This study identified a novel DN locus at chromosome 22q11 with significant evidence of linkage to DN. Our results suggest that this locus may be of importance in European populations. In addition, this study supports previously indicated DN loci on 3q21-q25 and 19q13.

PSEUDOMARKER: a powerful program for joint linkage and/or linkage disequilibrium analysis on mixtures of singletons and related individuals.

A decade ago, there was widespread enthusiasm for the prospects of genome-wide association studies to identify common variants related to common chronic diseases using samples of unrelated individuals from populations. Although technological advancements allow us to query more than a million SNPs across the genome at low cost, a disappointingly small fraction of the genetic portion of common disease etiology has been uncovered. This has led to the hypothesis that less frequent variants might be involved, stimulating a renaissance of the traditional approach of seeking genes using multiplex families from less diverse populations. However, by using the modern genotyping and sequencing technology, we can now look not just at linkage, but jointly at linkage and linkage disequilibrium (LD) in such samples. Software methods that can look simultaneously at linkage and LD in a powerful and robust manner have been lacking. Most algorithms cannot jointly analyze datasets involving families of varying structures in a statistically or computationally efficient manner. We have implemented previously proposed statistical algorithms in a user-friendly software package, PSEUDOMARKER. This paper is an announcement of this software package. We describe the motivation behind the approach, the statistical methods, and software, and we briefly demonstrate PSEUDOMARKER's advantages over other packages by example.

Genome-wide linkage screen for stature and body mass index in 3.032 families: evidence for sex- and population-specific genetic effects.

Stature (adult body height) and body mass index (BMI) have a strong genetic component explaining observed variation in human populations; however, identifying those genetic components has been extremely challenging. It seems obvious that sample size is a critical determinant for successful identification of quantitative trait loci (QTL) that underlie the genetic architecture of these polygenic traits. The inherent shared environment and known genetic relationships in family studies provide clear advantages for gene mapping over studies utilizing unrelated individuals. To these ends, we combined the genotype and phenotype data from four previously performed family-based genome-wide screens resulting in a sample of 9.371 individuals from 3.032 African-American and European-American families and performed variance-components linkage analyses for stature and BMI. To our knowledge, this study represents the single largest family-based genome-wide linkage scan published for stature and BMI to date. This large study sample allowed us to pursue population- and sex-specific analyses as well. For stature, we found evidence for linkage in previously reported loci on 11q23, 12q12, 15q25 and 18q23, as well as 15q26 and 19q13, which have not been linked to stature previously. For BMI, we found evidence for two loci: one on 7q35 and another on 11q22, both of which have been previously linked to BMI in multiple populations. Our results show both the benefit of (1) combining data to maximize the sample size and (2) minimizing heterogeneity by analyzing subgroups where within-group variation can be reduced and suggest that the latter may be a more successful approach in genetic mapping.

Linkage analysis of schizophrenia controlling for population substructure.

Etiological heterogeneity and complexity has hampered attempts to identify predisposing genes for schizophrenia. We sought to minimize the number of segregating genes involved by focusing on a population isolate with elevated disease prevalence. We exploited the well-established population history, and searched for disease susceptibility loci in families from two alternative founder lineages. We studied 28 schizophrenia pedigrees (123 nuclear families) from an outlying municipality on the eastern border of Finland. We divided the families based on their genealogy and defined two routes of immigration: southern and northern. We examined the kinship coefficients and allele frequency distributions within each group, and performed a linkage analysis based on 497 microsatellite markers across the genome. A high degree of historical relatedness was demonstrated by higher sharing of alleles than predicted by the relationships we identified within the previous four generations alone, as would be expected. Between the two subpopulations, allele frequencies were significantly different, consistent with their isolated genealogies. The southern families showed some evidence of linkage in a schizophrenia locus at 4q23 (Z = 3.3) near our previous finding with quantitative variation in verbal learning and memory [Paunio et al. (2004); Hum Mol Genet 13: 1693-1702], while the northern pedigrees gave most significant evidence on 10q21 (Z = 2.53). Joint analysis of families from both lineages suggested evidence of linkage only at 3p14 (Z = 3.18). Thus the detailed genealogical information led us to identification of distinct linkage signals for schizophrenia susceptibility loci between the three analyses we performed.