Biology subjects

Hveem, K.

Publications and source records attributed to Hveem, K..

11 recordsLinked to original sources

bioRxiv2019

Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

Mendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trondelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

genetics↗

bioRxiv2019

SNPs associated with HHIP expression have differential effects on lung function in males and females

Adult lung function is highly heritable and 279 genetic loci were recently reported as associated with spirometry-based measures of lung function. Though lung development and function differ between males and females throughout life, there has been no genome-wide study to identify genetic variants with differential effects on lung function in males and females. Here, we present the first genome-wide genotype-by-sex interaction study on four lung function traits in 303,612 participants from the UK Biobank. We detected five SNPs showing genome-wide significant (P<5 x 10-8) interactions with sex on lung function, as well as 21 suggestively significant interactions (P<1 x 10-6). The strongest sex interaction signal came from rs7697189 at 4:145436894 on forced expiratory volume in 1 second (FEV1) (P = 3.15 x 10-15), and was replicated (P = 0.016) in 75,696 individuals in the SpiroMeta consortium. Sex-stratified analyses demonstrated that the minor (C) allele of rs7697189 increased lung function to a greater extent in males than females (untransformed FEV1 {beta} = 0.028 [SE 0.0022] litres in males vs {beta} = 0.009 [SE 0.0014] litres in females), and this effect was not accounted for by differential effects on height, smoking or age at puberty. This SNP resides upstream of the gene encoding hedgehog-interacting protein (HHIP) and has previously been reported for association with lung function and HHIP expression in lung tissue. In our analyses, while HHIP expression in lung tissue was significantly different between the sexes with females having higher expression (most significant probeset P=6.90 x 10-6) after adjusting for age and smoking, rs7697189 did not demonstrate sex differential effects on expression. Establishing the mechanism by which HHIP SNPs have different effects on lung function in males and females will be important for our understanding of lung health and diseases, such as chronic obstructive pulmonary disease (COPD), in both sexes.

genetics↗

bioRxiv2019

Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts

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

genetics↗

bioRxiv2019

Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had [≥]1 predicted loss of function carrier, and most genes (>69%) had [≥]10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

genomics↗

bioRxiv2019

Search for early pancreatic cancer blood biomarkers in five European prospective population biobanks using metabolomics

Background and aimMost patients with pancreatic cancer present with advanced disease and die within the first year after diagnosis. Predictive biomarkers that signal the presence of pancreatic cancer in an early stage are desperately needed. We aimed to identify new and validate previously found plasma metabolomic biomarkers associated with early stages of pancreatic cancer. MethodsThe low incidence rate complicates prospective biomarker studies. Here, we took advantage of the availability of biobanked samples from five large population cohorts (HUNT2, HUNT3, FINRISK, Estonian biobank, Rotterdam Study) and identified prediagnostic blood samples from individuals who were to receive a diagnosis of pancreatic cancer between one month and seventeen years after blood sampling, and compared these with age- and gender-matched controls from the same cohorts. We applied 1H-NMR-based metabolomics on the Nightingale platform on these samples and applied logistic regression to assess the predictive value of individual metabolite concentrations, with gender, age, body mass index, smoking status, type 2 diabetes mellitus status, fasting status, and cohort as covariates. ResultsAfter quality assessment, we retained 356 cases and 887 controls. We identified two interesting hits, glutamine (p=0.011) and histidine (p=0.012), and obtained Westfall-Young family-wise error rate adjusted p-values of 0.43 for both. Stratification in quintiles showed a 1.5x elevated risk for the lowest 20% of glutamine and a 2.2x increased risk for the lowest 20% of histidine. Stratification by time to diagnosis (<2 years, 2-5 years, >5 years) suggested glutamine to be involved in an earlier process, tapering out closer to onset, and histidine in a process closer to the actual onset. Lasso-penalized logistic regression showed a slight improvement of the area under the Receiver Operator Curves when including glutamine and histidine in the model. Finally, our data did not support the earlier identified branched-chain amino acids as potential biomarkers for pancreatic cancer in several American cohorts. ConclusionWhile identifying glutamine and histidine as early biomarkers of potential biological interest, our results imply that a study at this scale does not yield metabolomic biomarkers with sufficient predictive value to be clinically useful per se as prognostic biomarkers.

epidemiology↗

bioRxiv2018

Genome-wide association study reveals a dynamic role of common genetic variation in infant and early childhood growth

Infant and childhood growth are dynamic processes characterized by drastic changes in fat mass and body mass index (BMI) at distinct developmental stages. To elucidate how genetic variation influences these processes, we performed the first genome-wide association study (GWAS) of BMI measurements at 12 time points from birth to eight years of age (9,286 children, 74,105 measurements) in the Norwegian Mother and Child Cohort Study (MoBa) with replication in 5,235 children (41,502 measurements). We identified five loci associated with BMI at distinct developmental stages with different patterns of association. Notably, we identified a novel transient effect in the leptin receptor (LEPR) locus, with no effect at birth, increasing effect on BMI in infancy, peaking at 6-12 months (rs2767486, P6m = 2.0 x 10-21, {beta}6m = 0.16) and little effect after age five. A similar transient effect was found near the leptin gene (LEP), peaking at 1.5 years of age (rs10487505, P1.5y = 1.3 x 10-8, {beta}1.5y = 0.079). Both signals are protein quantitative trait loci (pQTLs) for soluble LEPR and LEP in plasma in adults and independent from signals associated with other adult traits mapped to the respective genes, suggesting novel key roles of common variation in the leptin signaling pathway for healthy infant growth. Hence, our longitudinal analysis uncovers a complex and dynamic influence of common variation on BMI during infant and early childhood growth, dominated by the LEP-LEPR axis in infancy.

genetics↗

bioRxiv2018

Genome-wide association analysis of excessive daytime sleepiness identifies 42 loci that suggest phenotypic subgroups

Excessive daytime sleepiness (EDS) affects 10-20% of the population and is associated with substantial functional deficits. We identified 42 loci for self-reported EDS in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirmed the aggregate effect of a genetic risk score of 42 SNPs on EDS in independent Scandinavian cohorts and on other sleep disorders (restless leg syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). Strong genetic correlations were also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing. EDS variants clustered into two predominant composite phenotypes - sleep propensity and sleep fragmentation - with the former showing stronger evidence for enriched expression in central nervous system tissues, suggesting two unique mechanistic pathways. Mendelian randomization analysis indicated that higher BMI is causally associated with EDS risk, but EDS does not appear to causally influence BMI.

genomics↗

bioRxiv2018

Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis

Chronic Kidney Disease (CKD) is a growing health burden currently affecting 10-15% of adults worldwide. Estimated glomerular filtration rate (eGFR) as a marker of kidney function is commonly used to diagnose CKD. Previous genome-wide association study (GWAS) meta-analyses of CKD and eGFR or related phenotypes have identified a number of variants associated with kidney function, but these only explain a fraction of the variability in kidney phenotypes attributed to genetic components. To extend these studies, we analyzed data from the Nord-Trondelag Health Study (HUNT), which is more densely imputed than previous studies, and performed a GWAS meta-analysis of eGFR with publicly available summary statistics, more than doubling the sample size of previous meta-analyses. We identified 147 loci (53 novel loci) associated with eGFR, including genes involved in transcriptional regulation, kidney development, cellular signaling, metabolism, and solute transport. Moreover, genes at these loci show enriched expression in urogenital tissues and highlight gene sets known to play a role in kidney function. In addition, sex-stratified analysis identified three regions (prioritized genes: PPM1J, MCL1, and SLC47A1) with more significant effects in women than men. Using genetic risk scores constructed from these eGFR meta-analysis results, we show that associated variants are generally predictive of CKD but improve detection only modestly compared with other known clinical risk factors. Collectively, these results yield additional insight into the genetic factors underlying kidney function and progression to CKD.

genomics↗

bioRxiv2018

Biological and clinical insights from genetics of insomnia symptoms

Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes, but underlying pathophysiological processes and causal relationships with disease are poorly understood. Here we identify 57 loci for self-reported insomnia symptoms in the UK Biobank (n=453,379) and confirm their impact on self-reported insomnia symptoms in the HUNT study (n=14,923 cases, 47,610 controls), physician diagnosed insomnia in Partners Biobank (n=2,217 cases, 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n=83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis, phototransduction and muscle development pathways and of genes expressed in multiple brain regions, skeletal muscle and adrenal gland. Evidence of shared genetic factors is found between frequent insomnia symptoms and restless legs syndrome, aging, cardio-metabolic, behavioral, psychiatric and reproductive traits. Evidence is found for a possible causal link between insomnia symptoms and coronary heart disease, depressive symptoms and subjective well-being.\n\nOne Sentence SummaryWe identify 57 genomic regions associated with insomnia pointing to the involvement of phototransduction and ubiquitination and potential causal links to CAD and depression.

genomics↗

bioRxiv2017

Genome-wide analysis yields new loci associating with aortic valve stenosis

Aortic valve stenosis (AS) is the most common valvular heart disease, characterized by a thickened and calcified valve causing left ventricular outflow obstruction. Severe AS is a significant cause of morbidity and mortality, affecting approximately 5% of those over 70 years of age1,2,3. Little is known about the genetics of AS, although recently a variant at the LPA locus4 and a rare MYH6 missense variant were found to associate with AS5. We report a large genome-wide association study (GWAS) with a follow-up in up to 7,307 AS cases and 801,073 controls. We identified two new AS loci, on chromosome 1p21 near PALMD (rs7543130; OR=1.20, P=1.2x10-22) and on chromosome 2q22 in TEX41 (rs1830321; OR=1.15, P=1.8x10-13). Rs7543130 also associates with bicuspid aortic valve (BAV) (OR=1.28, P=6.6x10-10) and aortic root diameter (P=1.30x10-8) and rs1830321 associates with BAV (OR=1.12, P=5.3x10-3 and coronary artery disease (CAD) (OR=1.05, P=9.3x10-5). These results indicate that AS is partly rooted in the same processes as cardiac development and atherosclerosis.

genetics↗

bioRxiv2017

Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly - producing large type I error rates - in the analysis of phenotypes with unbalanced case-control ratios. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation (SPA) to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational time and memory cost of generalized mixed model. The computation cost linearly depends on sample size, and hence can be applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

genomics↗