Biology subjects

Veikko Salomaa

Publications and source records attributed to Veikko Salomaa.

7 recordsLinked to original sources

bioRxiv2016

Whole genome view of the consequences of a population bottleneck using 2926 genome sequences from Finland and United Kingdom

Isolated populations with enrichment of variants due to recent population bottlenecks provide a powerful resource for identifying disease-associated genetic variants and genes. As a model of an isolate population, we sequenced the genomes of 1463 Finnish individuals as part of the Sequencing Initiative Suomi (SISu) Project. We compared the genomic profiles of the 1463 Finns to a sample of 1463 British individuals that were sequenced in parallel as part of the UK10K Project. Whereas there were no major differences in the allele frequency of common variants, a significant depletion of variants in the rare frequency spectrum was observed in Finns when comparing the two populations. On the other hand, we observed >2.1 million variants that were twice as frequent among Finns compared to Britons and 800,000 variants that were more than 10 times more frequent in Finns. Furthermore, in Finns we observed a relative proportional enrichment of variants in the minor allele frequency range between 2 - 5% (p < 2.2x10-16). When stratified by their functional annotations, loss-of-function (LoF) variants showed the highest proportional enrichment in Finns (p = 0.0291). In the noncoding part of the genome, variants in conserved regions (p = 0.002) and promoters (p = 0.01) were also significantly enriched in the Finnish samples. These functional categories represent the highest a priori power for downstream association studies of rare variants using population isolates.

Genetics↗

bioRxiv2016

Ultra-rare disruptive and damaging mutations influence educational attainment in the general population

Ultra-rare inherited and de novo disruptive variants in highly constrained (HC) genes are enriched in neurodevelopmental disorders 1-5. However, their impact on cognition in the general population has not been explored. We hypothesize that disruptive and damaging ultra-rare variants (URVs) in HC genes not only confer risk to neurodevelopmental disorders, but also influence general cognitive abilities measured indirectly by years of education (YOE). We tested this hypothesis in 14,133 individuals with whole exome or genome sequencing data. The presence of one or more URVs was associated with a decrease in YOE (3.1 months less for each additional mutation; P-value=3.3x10-8) and the effect was stronger in HC genes enriched for brain expression (6.5 months less, P-value=3.4x10-5). The effect of these variants was more pronounced than the estimated effects of runs of homozygosity and pathogenic copy number variation 6-9. Our findings suggest that effects of URVs in HC genes are not confined to severe neurodevelopmental disorder, but influence the cognitive spectrum in the general population

Genetics↗

bioRxiv2016

Genome-wide association study identifies 17 new loci influencing concentrations of circulating cytokines and growth factors

Circulating cytokines and growth factors are regulators of inflammation and have been implicated in autoimmune and metabolic diseases. In this genome-wide association study (GWAS) up to n=8,293 Finns we identified 27 loci with genome-wide association (P-value<1.2x10-9) for one or more cytokines, including 17 unidentified in previous GWASes. Fifteen of the associated SNPs had expression quantitative trait loci in whole blood. We provide strong genetic instruments to clarify the causal roles of cytokine signaling and upstream inflammation in immune-related and other chronic diseases. We further link known autoimmune disease variants including Crohn's disease, multiple sclerosis and ulcerative colitis with new inflammatory markers, which elucidate the molecular mechanisms underpinning these diseases and suggest potential drug targets.

Genomics↗

bioRxiv2016

Metabolic profiling of alcohol consumption in 9778 young adults

BackgroundHigh alcohol consumption is a major cause of morbidity, yet alcohol is associated with both favourable and adverse effects on cardiometabolic risk markers. We aimed to characterize the associations of usual alcohol consumption with a comprehensive systemic metabolite profile in young adults.\n\nMethodsCross-sectional associations of alcohol intake with 86 metabolic measures were assessed for 9778 individuals from three population-based cohorts from Finland (age 24-45 years, 52% women). Metabolic changes associated with change in alcohol intake during 6-year follow-up were further examined for 1466 individuals. Alcohol intake was assessed by questionnaires. Circulating lipids, fatty acids and metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays.\n\nResultsIncreased alcohol intake was associated with cardiometabolic risk markers across multiple metabolic pathways, including higher lipid concentrations in HDL subclasses and smaller LDL particle size, increased proportions of monounsaturated fatty acids and decreased proportion of omega-6 fatty acids, lower concentrations of glutamine and citrate (P<0.001 for 56 metabolic measures). Many metabolic biomarkers displayed U-shaped associations with alcohol consumption. Results were coherent for men and women, consistent across the three cohorts, and similar if adjusting for body mass index, smoking and physical activity. The metabolic changes accompanying change in alcohol intake during follow-up resembled the cross-sectional association pattern (R2=0.83, slope=0.72{+/-}0.04).\n\nConclusionsAlcohol consumption is associated with a complex metabolic signature, including aberrations in multiple biomarkers for elevated cardiometabolic risk. The metabolic signature tracks with long-term changes in alcohol consumption. These results elucidate the double-edged effects of alcohol on cardiovascular risk.\n\nKey messagesO_LIAlcohol intake in young adults associates with multiple novel metabolic biomarkers for the risk of cardiovascular disease and type 2 diabetes. The metabolic aberrations are mainly adversely related to cardiometabolic risk\nC_LIO_LIProminent metabolic associations with alcohol consumption include monounsaturated fatty acids, omega-6 fatty acids, glutamine, citrate and lipoprotein particle size. Many of these cardiometabolic biomarkers were as strongly associated with alcohol intake as HDL cholesterol\nC_LIO_LIThe strongest novel biomarkers of alcohol consumption followed linear association shapes, whereas many lipid measures displayed U-shaped associations\nC_LIO_LIThe detailed metabolic signature of alcohol consumption tracked with long-term changes in alcohol use, suggesting that the observed metabolic changes are at least partly due to alcohol consumption\nC_LIO_LIThe results provide improved understanding of the diverse molecular processes related to alcohol intake. Novel metabolic biomarkers reflecting both alcohol intake and cardiovascular risk could serve as intermediates that may help to bridge the complex relation between alcohol and cardiometabolic risk\nC_LI

Systems Biology↗

bioRxiv2016

Genomic prediction of coronary heart disease

BackgroundGenetics plays an important role in coronary heart disease (CHD) but the clinical utility of a genomic risk score (GRS) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear.\n\nMethodsWe generated a GRS of 49,310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested this using five prospective population cohorts (three FINRISK cohorts, combined n=12,676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n=3,406, 587 incident CHD events).\n\nResultsThe GRS was strongly associated with time to CHD event (FINRISK HR=1.74, 95% CI 1.61-1.86 per S.D. of GRS; Framingham HR=1.28, 95% CI 1.18-1.38), and was largely unchanged by adjustment for clinical risk scores or individual risk factors, including family history. Integration of the GRS with clinical risk scores (FRS and ACC/AHA13 score) improved prediction of CHD events within 10 years (meta-analysis C-index: +1.5-1.6%, P<0.001), particularly for individuals [≥]60 years old (meta-analysis C-index: +4.6-5.1%, P<0.001). Men in the top 20% of the GRS had 3-fold higher risk of CHD by age 75 in FINRISK and 2-fold in FHS, and attaining 10% cumulative CHD risk 18y earlier in FINRISK and 12y earlier in FHS than those in the bottom 20%. Furthermore, high genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking.\n\nConclusionsA GRS based on a large number of SNPs substantially improves CHD risk prediction and encodes decades of variation in CHD risk not captured by traditional clinical risk scores.

Genomics↗

bioRxiv2015

metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analysing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests.\n\nWe introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.\n\nMultivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies.\n\nCode is available at https://github.com/aalto-ics-kepaco.

Bioinformatics↗

bioRxiv2015

Systems medicine links microbial inflammatory response with glycoprotein-associated mortality risk

Integrative analyses of high-throughput omics data have elucidated the aetiology and pathogenesis for complex traits and diseases1-4, and the linking of omics information to electronic health records promises new insights into human health and disease. Recent nuclear magnetic resonance (NMR) spectroscopy biomarker profiling has implicated glycoprotein acetyls (GlycA) as a biomarker for cardiovascular risk5 and all-cause mortality6. To elucidate biological processes contributing to GlycA-associated mortality risk, we leveraged human omics data from three population-based cohorts together with nation-wide Finnish hospital and mortality records. Elevated GlycA was associated with myriad infection-related inflammatory processes. Within individuals, elevated GlycA levels were stable over long time periods, up to a decade, and chronically elevated GlycA was also associated with modest elevation of numerous cytokines. Individuals with elevated GlycA also showed increased expression of a transcriptional sub-network, the Neutrophil Degranulation Module (NDM), suggesting an increased activity of microbe-driven immune response. Subsequent analysis of nation-wide hospitalisation and death records was consistent with a microbial basis for GlycA-associated mortality, with each standard deviation increase in GlycA raising an individuals future risk of hospitalization and death from non-localized infection by 40% and 136%, respectively. These results show that, beyond its established role in acute-phase response7-9, elevated GlycA is more broadly a biomarker for low-grade chronic inflammation and increased neutrophil activity. Further, increased risk of susceptibility to severe microbial-infection events in healthy individuals suggests this inflammation is a contributor to mortality risk. Taken together, this study demonstrates the power of an integrative approach that combines omics data and health records to delineate the biological processes underlying a newly discovered biomarker, providing a model strategy for future systems medicine studies.

Genomics↗