Connecting cohorts of Finnish biobanks creates a research resource for the study of healthy ageing.

AIMS: Connecting cohorts with biobanks is a Finnish biobank collaboration, creating an infrastructure for the study of healthy ageing. We aimed to develop a model for data integration and harmonisation between different biobanks with procedures for joint access. METHODS: The heart of the collaboration is the integrated datasets formed by using data from three biobanks: (a) Arctic Biobank, hosting regional birth cohorts and cohorts of elderly; (b) hospital-affiliated Borealis Biobank of Northern Finland; and (c) THL Biobank, hosting population-based cohorts. The datasets were created by developing a data dictionary, harmonising cohort data and with a joint pseudonymisation process. RESULTS: The connecting cohorts with biobanks resource at its widest consists altogether of almost 1.4 million individuals from collaborating biobanks. Utilising data from 107,000 cohort participants, we created harmonised datasets that contain attributes describing metabolic risk and frailty for studies of healthy ageing. These data can be complemented with medical data available from Biobank Borealis and with samples taken at hospital settings for approximately 38,000 cohort participants. In addition, the harmonised connecting cohorts with biobanks datasets can be expanded with supplementary data and samples from the collaborating biobanks. CONCLUSIONS: The connecting cohorts with biobanks datasets provide a unique resource for research on ageing-related personalised healthcare and for real-world evidence studies. Following the FAIR principles on findability, accessibility, interoperability, and reusability, the reused and harmonised datasets are findable and made accessible for researchers. The same approach can be further utilised to develop additional datasets for other research topics.

TWINGEN - protocol for an observational clinical biobank recall and biomarker study to identify individuals with high risk of Alzheimer's disease.

Introduction: A better understanding of the earliest stages of Alzheimer's disease (AD) could expedite the development or administration of treatments. Large population biobanks hold the promise to identify individuals at an elevated risk of AD and related dementias based on health registry information. Here, we establish the protocol for an observational clinical recall and biomarker study called TWINGEN with the aim to identify individuals at high risk of AD by assessing cognition, health and AD-related biomarkers. Suitable candidates were identified and invited to participate in the new study among Finnish biobank donors according to TWINGEN study criteria. Methods and analysis: A multi-center study (n=800) to obtain blood-based biomarkers, telephone-administered and web-based memory and cognitive parameters, questionnaire information on lifestyle, health and psychological factors, and accelerometer data for measures of physical activity, sedentary behavior and sleep. A sub-cohort are being asked to participate in an in-person neuropsychological assessment (n=200) and wear an Oura ring (n=50). All participants in the TWINGEN study have genome-wide genotyping data and up to 48 years of follow-up data from the population-based older Finnish Twin Cohort (FTC) study of the University of Helsinki. TWINGEN data will be transferred to Finnish Institute of Health and Welfare (THL) biobank and we aim to further to transfer it to the FinnGen study where it will be combined with health registry data for prediction of AD. Ethics and dissemination: This recall study consists of FTC/THL/FinnGen participants whose data were acquired in accordance with the Finnish Biobank Act. The recruitment protocols followed the biobank protocols approved by Finnish Medicines Agency. The TWINGEN study plan was approved by the Ethics Committee of Hospital District of Helsinki and Uusimaa (number 16831/2022). THL Biobank approved the research plan with the permission no: THLBB2022_83.

New Genetic Variants in CYP2B6 and SLC6A Support the Role of Oxidative Stress in Familial Ménière's Disease.

The objective was to study the genetic etiology of Ménière's disease (MD) using next-generation sequencing in three families with three cases of MD. Whole exome sequencing was used to identify rare genetic variants co-segregating with MD in Finnish families. In silico estimations and population databases were used to estimate the frequency and pathogenicity of the variants. Variants were validated and genotyped from additional family members using capillary sequencing. A geneMANIA analysis was conducted to investigate the functional pathways and protein interactions of candidate genes. Seven rare variants were identified to co-segregate with MD in the three families: one variant in the CYP2B6 gene in family I, one variant in GUSB and EPB42 in family II, and one variant in each of the SLC6A, ASPM, KNTC1, and OVCH1 genes in family III. Four of these genes were linked to the same co-expression network with previous familial MD candidate genes. Dysfunction of CYP2B6 and SLC6A could predispose to MD via the oxidative stress pathway. Identification of ASPM and KNTC1 as candidate genes for MD suggests dysregulation of mitotic spindle formation in familial MD. The genetic etiology of familial MD is heterogenic. Our findings suggest a role for genes acting on oxidative stress and mitotic spindle formation in MD but also highlight the genetic complexity of MD.

Circulating inflammatory cytokines and risk of five cancers: a Mendelian randomization analysis.

BACKGROUND: Epidemiological and experimental evidence has linked chronic inflammation to cancer aetiology. It is unclear whether associations for specific inflammatory biomarkers are causal or due to bias. In order to examine whether altered genetically predicted concentration of circulating cytokines are associated with cancer development, we performed a two-sample Mendelian randomisation (MR) analysis. METHODS: Up to 31,112 individuals of European descent were included in genome-wide association study (GWAS) meta-analyses of 47 circulating cytokines. Single nucleotide polymorphisms (SNPs) robustly associated with the cytokines, located in or close to their coding gene (cis), were used as instrumental variables. Inverse-variance weighted MR was used as the primary analysis, and the MR assumptions were evaluated in sensitivity and colocalization analyses and a false discovery rate (FDR) correction for multiple comparisons was applied. Corresponding germline GWAS summary data for five cancer outcomes (breast, endometrial, lung, ovarian, and prostate), and their subtypes were selected from the largest cancer-specific GWASs available (cases ranging from 12,906 for endometrial to 133,384 for breast cancer). RESULTS: There was evidence of inverse associations of macrophage migration inhibitory factor with breast cancer (OR per SD = 0.88, 95% CI 0.83 to 0.94), interleukin-1 receptor antagonist with endometrial cancer (0.86, 0.80 to 0.93), interleukin-18 with lung cancer (0.87, 0.81 to 0.93), and beta-chemokine-RANTES with ovarian cancer (0.70, 0.57 to 0.85) and positive associations of monokine induced by gamma interferon with endometrial cancer (3.73, 1.86 to 7.47) and cutaneous T-cell attracting chemokine with lung cancer (1.51, 1.22 to 1.87). These associations were similar in sensitivity analyses and supported in colocalization analyses. CONCLUSIONS: Our study adds to current knowledge on the role of specific inflammatory biomarker pathways in cancer aetiology. Further validation is needed to assess the potential of these cytokines as pharmacological or lifestyle targets for cancer prevention.

Genome-Wide Association Study Identifies Two Novel Loci Associated with Female Stress and Urgency Urinary Incontinence.

PURPOSE: Genome-wide association studies have not identified replicable genetic risk loci for stress or urgency urinary incontinence. MATERIALS AND METHODS: We carried out a discovery stage, case control, genome-wide association study in 3 independent discovery cohorts of European women (8,979) for stress incontinence, urgency incontinence, and any incontinence phenotypes. We conducted replication in 6 additional studies of European ancestry (4,069). We collected bladder biopsies from women with incontinence (50) to further investigate bladder expression of implicated genes and pathways and used symptom questionnaires for phenotyping. We conducted meta-analyses using inverse variance fixed effects models and whole transcriptome analyses using Affymetrix® arrays with replication with TaqMan® polymerase chain reaction. RESULTS: In the discovery stage, we identified 16 single nucleotide polymorphisms genotyped or imputed at 5 loci that reached genome-wide significance (p <5×10-8). In replication, rs138724718 on chromosome 2 near the macrophage receptor with collagenous structure (MARCO) gene (replication p=0.003) was associated with stress incontinence. In addition, rs34998271 on chromosome 6 near the endothelin 1 (EDN1) gene (replication p=0.0008) was associated with urgency incontinence. In combined meta-analyses of discovery and replication cohorts, associations with genome-wide significance for these 2 single nucleotide polymorphisms were confirmed. Transcriptomics analyses showed differential expression of 7 of 19 genes in the endothelin pathway between stress and urgency incontinence (p <0.0001). CONCLUSIONS: We uncovered 2 new risk loci near the genes endothelin 1 (EDN1), associated with urgency incontinence, and macrophage receptor with collagenous structure (MARCO), associated with stress incontinence. These loci are biologically plausible given their roles in smooth muscle contraction and innate host defense, respectively.

Polygenic burden has broader impact on health, cognition, and socioeconomic outcomes than most rare and high-risk copy number variants.

Copy number variants (CNVs) are associated with syndromic and severe neurological and psychiatric disorders (SNPDs), such as intellectual disability, epilepsy, schizophrenia, and bipolar disorder. Although considered high-impact, CNVs are also observed in the general population. This presents a diagnostic challenge in evaluating their clinical significance. To estimate the phenotypic differences between CNV carriers and non-carriers regarding general health and well-being, we compared the impact of SNPD-associated CNVs on health, cognition, and socioeconomic phenotypes to the impact of three genome-wide polygenic risk score (PRS) in two Finnish cohorts (FINRISK, n = 23,053 and NFBC1966, n = 4895). The focus was on CNV carriers and PRS extremes who do not have an SNPD diagnosis. We identified high-risk CNVs (DECIPHER CNVs, risk gene deletions, or large [>1 Mb] CNVs) in 744 study participants (2.66%), 36 (4.8%) of whom had a diagnosed SNPD. In the remaining 708 unaffected carriers, we observed lower educational attainment (EA; OR = 0.77 [95% CI 0.66-0.89]) and lower household income (OR = 0.77 [0.66-0.89]). Income-associated CNVs also lowered household income (OR = 0.50 [0.38-0.66]), and CNVs with medical consequences lowered subjective health (OR = 0.48 [0.32-0.72]). The impact of PRSs was broader. At the lowest extreme of PRS for EA, we observed lower EA (OR = 0.31 [0.26-0.37]), lower-income (OR = 0.66 [0.57-0.77]), lower subjective health (OR = 0.72 [0.61-0.83]), and increased mortality (Cox's HR = 1.55 [1.21-1.98]). PRS for intelligence had a similar impact, whereas PRS for schizophrenia did not affect these traits. We conclude that the majority of working-age individuals carrying high-risk CNVs without SNPD diagnosis have a modest impact on morbidity and mortality, as well as the limited impact on income and educational attainment, compared to individuals at the extreme end of common genetic variation. Our findings highlight that the contribution of traditional high-risk variants such as CNVs should be analyzed in a broader genetic context, rather than evaluated in isolation.

Maternal and infant prediction of the child BMI trajectories; studies across two generations of Northern Finland birth cohorts.

BACKGROUND/OBJECTIVE: Children BMI is a longitudinal phenotype, developing through interplays between genetic and environmental factors. Whilst childhood obesity is escalating, we require a better understanding of its early origins and variation across generations to prevent it. SUBJECTS/METHODS: We designed a cross-cohort study including 12,040 Finnish children from the Northern Finland Birth Cohorts 1966 and 1986 (NFBC1966 and NFBC1986) born before or at the start of the obesity epidemic. We used group-based trajectory modelling to identify BMI trajectories from 2 to 20 years. We subsequently tested their associations with early determinants (mother and child) and the possible difference between generations, adjusted for relevant biological and socioeconomic confounders. RESULTS: We identified four BMI trajectories, 'stable-low' (34.8%), 'normal' (44.0%), 'stable-high' (17.5%) and 'early-increase' (3.7%). The 'early-increase' trajectory represented the highest risk for obesity. We analysed a dose-response association of maternal pre-pregnancy BMI and smoking with BMI trajectories. The directions of effect were consistent across generations and the effect sizes tended to increase from earlier generation to later. Respectively for NFBC1966 and NFBC1986, the adjusted risk ratios of being in the early-increase group were 1.08 (1.06-1.10) and 1.12 (1.09-1.15) per unit of pre-pregnancy BMI and 1.44 (1.05-1.96) and 1.48 (1.17-1.87) in offspring of smoking mothers compared to non-smokers. We observed similar relations with infant factors including birthweight for gestational age and peak weight velocity. In contrast, the age at adiposity peak in infancy was associated with the BMI trajectories in NFBC1966 but did not replicate in NFBC1986. CONCLUSIONS: Exposures to adverse maternal predictors were associated with a higher risk obesity trajectory and were consistent across generations. However, we found a discordant association for the timing of adiposity peak over a 20-year period. This suggests the role of residual environmental factors, such as nutrition, and warrants additional research to understand the underlying gene-environment interplay.

Association of Body Mass Index with Fecal Microbial Diversity and Metabolites in the Northern Finland Birth Cohort.

BACKGROUND: Obesity is an established risk factor for multiple cancer types. Lower microbial richness has been linked to obesity, but human studies are inconsistent, and associations of early-life body mass index (BMI) with the fecal microbiome and metabolome are unknown. METHODS: We characterized the fecal microbiome (n = 563) and metabolome (n = 340) in the Northern Finland Birth Cohort 1966 using 16S rRNA gene sequencing and untargeted metabolomics. We estimated associations of adult BMI and BMI history with microbial features and metabolites using linear regression and Spearman correlations (rs ) and computed correlations between bacterial sequence variants and metabolites overall and by BMI category. RESULTS: Microbial richness, including the number of sequence variants (rs = -0.21, P < 0.0001), decreased with increasing adult BMI but was not independently associated with BMI history. Adult BMI was associated with 56 metabolites but no bacterial genera. Significant correlations were observed between microbes in 5 bacterial phyla, including 18 bacterial genera, and metabolites in 49 of the 62 metabolic pathways evaluated. The genera with the strongest correlations with relative metabolite levels (positively and negatively) were Blautia, Oscillospira, and Ruminococcus in the Firmicutes phylum, but associations varied by adult BMI category. CONCLUSIONS: BMI is strongly related to fecal metabolite levels, and numerous associations between fecal microbial features and metabolite levels underscore the dynamic role of the gut microbiota in metabolism. IMPACT: Characterizing the associations between the fecal microbiome, the fecal metabolome, and BMI, both recent and early-life exposures, provides critical background information for future research on cancer prevention and etiology.

A single genetic locus associated with pediatric fractures: A genome-wide association study on 3,230 patients.

The understanding of the biological and environmental risk factors of fractures in pediatrics is limited. Previous studies have reported that fractures involve heritable traits, but the genetic factors contributing to the risk of fractures remain elusive. Furthermore, genetic influences specific to immature bone have not been thoroughly studied. Therefore, the aim of the present study was to identify genetic variations that are associated with fractures in early childhood. The present study used a prospective Northern Finland Birth Cohort (year 1986; n=9,432). The study population was comprised of 3,230 cohort members with available genotype data. A total of 48 members of the cohort (1.5%) had in-hospital treated bone fractures during their first 6 years of life. Furthermore, individuals without fracture (n=3,182) were used as controls. A genome-wide association study (GWAS) was performed using a frequentist association test. In the GWAS analysis, a linear regression model was fitted to test for additive effects of single-nucleotide polymorphisms (SNPs; genotype dosage) adjusting for sex and performing population stratification using genotypic principal components. Using the GWAS analysis, the present study identified one locus with a significant association with fractures during childhood on chromosome 10 (rs112635931) and six loci with a suggested implication. The lead SNP rs112635931 was located near proline- and serine-rich 2 (PROSER2) antisense RNA 1 (PROSER2-AS1) and PROSER2, thus suggesting that these may be novel candidate genes associated with the risk of pediatric fractures.

Exome Sequencing Reveals a Phenotype Modifying Variant in ZNF528 in Primary Osteoporosis With a COL1A2 Deletion.

We studied a family with severe primary osteoporosis carrying a heterozygous p.Arg8Phefs*14 deletion in COL1A2, leading to haploinsufficiency. Three affected individuals carried the mutation and presented nearly identical spinal fractures but lacked other typical features of either osteogenesis imperfecta or Ehlers-Danlos syndrome. Although mutations leading to haploinsufficiency in COL1A2 are rare, mutations in COL1A1 that lead to less protein typically result in a milder phenotype. We hypothesized that other genetic factors may contribute to the severe phenotype in this family. We performed whole-exome sequencing in five family members and identified in all three affected individuals a rare nonsense variant (c.1282C > T/p.Arg428*, rs150257846) in ZNF528. We studied the effect of the variant using qPCR and Western blot and its subcellular localization with immunofluorescence. Our results indicate production of a truncated ZNF528 protein that locates in the cell nucleus as per the wild-type protein. ChIP and RNA sequencing analyses on ZNF528 and ZNF528-c.1282C > T indicated that ZNF528 binding sites are linked to pathways and genes regulating bone morphology. Compared with the wild type, ZNF528-c.1282C > T showed a global shift in genomic binding profile and pathway enrichment, possibly contributing to the pathophysiology of primary osteoporosis. We identified five putative target genes for ZNF528 and showed that the expression of these genes is altered in patient cells. In conclusion, the variant leads to expression of truncated ZNF528 and a global change of its genomic occupancy, which in turn may lead to altered expression of target genes. ZNF528 is a novel candidate gene for bone disorders and may function as a transcriptional regulator in pathways affecting bone morphology and contribute to the phenotype of primary osteoporosis in this family together with the COL1A2 deletion. © 2020 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Polygenic Risk Scores and Physical Activity.

PURPOSE: Polygenic risk scores (PRS) summarize genome-wide genotype data into a single variable that produces an individual-level risk score for genetic liability. PRS has been used for prediction of chronic diseases and some risk factors. As PRS has been studied less for physical activity (PA), we constructed PRS for PA and studied how much variation in PA can be explained by this PRS in independent population samples. METHODS: We calculated PRS for self-reported and objectively measured PA using UK Biobank genome-wide association study summary statistics, and analyzed how much of the variation in self-reported (MET-hours per day) and measured (steps and moderate-to-vigorous PA minutes per day) PA could be accounted for by the PRS in the Finnish Twin Cohorts (FTC; N = 759-11,528) and the Northern Finland Birth Cohort 1966 (NFBC1966; N = 3263-4061). Objective measurement of PA was done with wrist-worn accelerometer in UK Biobank and NFBC1966 studies, and with hip-worn accelerometer in the FTC. RESULTS: The PRS accounted from 0.07% to 1.44% of the variation (R) in the self-reported and objectively measured PA volumes (P value range = 0.023 to <0.0001) in the FTC and NFBC1966. For both self-reported and objectively measured PA, individuals in the highest PRS deciles had significantly (11%-28%) higher PA volumes compared with the lowest PRS deciles (P value range = 0.017 to <0.0001). CONCLUSIONS: PA is a multifactorial phenotype, and the PRS constructed based on UK Biobank results accounted for statistically significant but overall small proportion of the variation in PA in the Finnish cohorts. Using identical methods to assess PA and including less common and rare variants in the construction of PRS may increase the proportion of PA explained by the PRS.

Exploring the role of genetic confounding in the association between maternal and offspring body mass index: evidence from three birth cohorts.

BACKGROUND: Maternal pre-pregnancy body mass index (BMI) is positively associated with offspring birth weight (BW) and BMI in childhood and adulthood. Each of these associations could be due to causal intrauterine effects, or confounding (genetic or environmental), or some combination of these. Here we estimate the extent to which the association between maternal BMI and offspring body size is explained by offspring genotype, as a first step towards establishing the importance of genetic confounding. METHODS: We examined the associations of maternal pre-pregnancy BMI with offspring BW and BMI at 1, 5, 10 and 15 years, in three European birth cohorts (n ≤11 498). Bivariate Genomic-relatedness-based Restricted Maximum Likelihood implemented in the GCTA software (GCTA-GREML) was used to estimate the extent to which phenotypic covariance was explained by offspring genotype as captured by common imputed single nucleotide polymorphisms (SNPs). We merged individual participant data from all cohorts, enabling calculation of pooled estimates. RESULTS: Phenotypic covariance (equivalent here to Pearson's correlation coefficient) between maternal BMI and offspring phenotype was 0.15 [95% confidence interval (CI): 0.13, 0.17] for offspring BW, increasing to 0.29 (95% CI: 0.26, 0.31) for offspring 15 year BMI. Covariance explained by offspring genotype was negligible for BW [-0.04 (95% CI: -0.09, 0.01)], but increased to 0.12 (95% CI: 0.04, 0.21) at 15 years, which is equivalent to 43% (95% CI: 15%, 72%) of the phenotypic covariance. Sensitivity analyses using weight, BMI and ponderal index as the offspring phenotype at all ages showed similar results. CONCLUSIONS: Offspring genotype explains a substantial fraction of the covariance between maternal BMI and offspring adolescent BMI. This is consistent with a potentially important role for genetic confounding as a driver of the maternal BMI-offspring BMI association.

Author Correction: Exome sequencing of Finnish isolates enhances rare-variant association power.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

Genome-wide association study identifies seven novel loci associating with circulating cytokines and cell adhesion molecules in Finns.

BACKGROUND: Inflammatory processes contribute to the pathophysiology of multiple chronic conditions. Genetic factors play a crucial role in modulating the inflammatory load, but the exact mechanisms are incompletely understood. OBJECTIVE: To assess genetic determinants of 16 circulating cytokines and cell adhesion molecules (inflammatory phenotypes) in Finns. METHODS: Genome-wide associations of the inflammatory phenotypes were studied in Northern Finland Birth Cohort 1966 (N=5284). A subsequent meta-analysis was completed for 10 phenotypes available in a previous genome-wide association study, adding up to 13 577 individuals in the study. Complementary association tests were performed to study the effect of the ABO blood types on soluble adhesion molecule levels. RESULTS: We identified seven novel and six previously reported genetic associations (p<3.1×10-9). Three loci were associated with soluble vascular cell adhesion molecule-1 (sVCAM-1) level, one of which was the ABO locus that has been previously associated with soluble E-selectin (sE-selectin) and intercellular adhesion molecule-1 (sICAM-1) levels. Our findings suggest that the blood type B associates primarily with sVCAM-1 level, while the A1 subtype shows a robust effect on sE-selectin and sICAM-1 levels. The genotypes in the ABO locus associating with higher soluble adhesion molecule levels tend to associate with lower circulating cholesterol levels and lower cardiovascular disease risk. CONCLUSION: The present results extend the knowledge about genetic factors contributing to the inflammatory load. Our findings suggest that two distinct mechanisms contribute to the soluble adhesion molecule levels in the ABO locus and that elevated soluble adhesion molecule levels per se may not increase risk for cardiovascular disease.

Whole-exome sequencing suggests multiallelic inheritance for childhood-onset Ménière's disease.

The genetic background of Ménière's disease (MD) was studied in one patient with childhood-onset MD and his grandfather affected with middle age-onset MD. Whole-exome sequencing was performed and the data were compared to 76 exomes from unrelated subjects without MD. Thirteen rare inner ear expressed variants with pathogenic estimations were observed in the case of childhood-onset MD. These variants were in genes involved in the formation of cell membranes or the cytoskeleton and in genes participating in cell death or gene-regulation pathways. His grandfather shared two of the variants: p.Y273N in HMX2 and p.L229F in TMEM55B. HMX2 p.Y273N was considered the more likely candidate for MD, as the gene is known to affect both hearing and vestibular function. The variant in the HMX2 gene may affect inner ear development and structural integrity and thus might predispose to the onset of MD. As there was a significant difference in onset between the patients, an accumulation of defects in several pathways is probably responsible for the exceptionally early onset of the disease, and the genetic etiology of childhood-onset MD is most likely multifactorial. This is the first molecular genetic study of childhood-onset MD.

Identification of disease-associated loci using machine learning for genotype and network data integration.

MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wide Association Studies (GWAS) identify loci with a strong effect size, whereas GWAS meta-analyses are often needed to capture weak loci contributing to the missing heritability. Development of novel machine learning algorithms for merging genotype data with other omics data is highly needed as it could enhance the prioritization of weak loci. RESULTS: We developed cNMTF (corrected non-negative matrix tri-factorization), an integrative algorithm based on clustering techniques of biological data. This method assesses the inter-relatedness between genotypes, phenotypes, the damaging effect of the variants and gene networks in order to identify loci-trait associations. cNMTF was used to prioritize genes associated with lipid traits in two population cohorts. We replicated 129 genes reported in GWAS world-wide and provided evidence that supports 85% of our findings (226 out of 265 genes), including recent associations in literature (NLGN1), regulators of lipid metabolism (DAB1) and pleiotropic genes for lipid traits (CARM1). Moreover, cNMTF performed efficiently against strong population structures by accounting for the individuals' ancestry. As the method is flexible in the incorporation of diverse omics data sources, it can be easily adapted to the user's research needs. AVAILABILITY AND IMPLEMENTATION: An R package (cnmtf) is available at https://lgl15.github.io/cnmtf_web/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide meta-analysis identifies genetic locus on chromosome 9 associated with Modic changes.

BACKGROUND: Low back pain (LBP) is a common disabling condition. Lumbar disc degeneration (LDD) may be a contributing factor for LBP. Modic change (MC), a distinct phenotype of LDD, is presented as a pathological bone marrow signal change adjacent to vertebral endplate on MRI. It is strongly associated with LBP and has heritability around 30%. Our objective was to identify genetic loci associated with MC using a genome-wide meta-analysis. METHODS: Presence of MC was evaluated in lumbar MRI in the Northern Finland Birth Cohort 1966 (n=1182) and TwinsUK (n=647). Genome-wide association analyses were carried out using linear regression model. Inverse-variance weighting approach was used in the meta-analysis. RESULTS: A locus associated with MC (p<5e-8) was found on chromosome 9 with the lead SNP rs1934268 in an intron of the PTPRD gene. It is located in the binding region of BCL11A, SPI1 and PBX3 transcription factors. The SNP was nominally associated with LBP in TwinsUK (p=0.001) but not associated in the UK Biobank (p=0.914). Suggestive signals (p<1e-5) were identified near XKR4, SCIN, MGMT, DLG2, ZNF184 and OPRK1. CONCLUSION: PTPRD is a novel candidate gene for MC that may act via the development of cartilage or nervous system; further work is needed to define the mechanisms underlying the pathways leading to development of MC. This is the first genome-wide meta-analysis of MC, and the results pave the way for further studies on the genetic factors underlying the various features of spine degeneration and LBP.

Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics.

BACKGROUND: Quantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available. METHODS: We describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative line shape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n = 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n = 578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n = 4548). RESULTS: Intra-assay metabolite variations were mostly <5%, indicating high robustness and accuracy of urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found to be clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance. CONCLUSION: Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.