Publications by authors named "Sanni E Ruotsalainen"

4 Publications

  • Page 1 of 1

Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices.

Nat Commun 2021 04 12;12(1):2182. Epub 2021 Apr 12.

Division of Cardiology, George Washington University School of Medicine and Healthcare Sciences, Washington, DC, USA.

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.
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http://dx.doi.org/10.1038/s41467-021-22339-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8042019PMC
April 2021

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease.

Eur J Hum Genet 2021 02 27;29(2):309-324. Epub 2020 Oct 27.

Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.

Multivariate methods are known to increase the statistical power to detect associations in the case of shared genetic basis between phenotypes. They have, however, lacked essential analytic tools to follow-up and understand the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 representative variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1 × 10). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the representative variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.
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http://dx.doi.org/10.1038/s41431-020-00730-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7868371PMC
February 2021

Geographic Variation and Bias in the Polygenic Scores of Complex Diseases and Traits in Finland.

Am J Hum Genet 2019 06 30;104(6):1169-1181. Epub 2019 May 30.

Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki 00014, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Helsinki Institute for Information Technology and Department of Mathematics and Statistics, University of Helsinki, Helsinki 00014, Finland. Electronic address:

Polygenic scores (PSs) are becoming a useful tool to identify individuals with high genetic risk for complex diseases, and several projects are currently testing their utility for translational applications. It is also tempting to use PSs to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how the population genetic properties of the training and target samples affect the geographic distribution of PSs. Here, we evaluate geographic differences, and related biases, of PSs in Finland in a geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PSs for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waist-hip ratio (WHR), body-mass index (BMI), and height, but not for Crohn disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PSs and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulations of geographic differences for CAD, WHR, BMI, and height, suggesting bias arising from the population's genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PSs are for small biases even within relatively homogeneous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PSs is essential for translational applications of PSs.
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http://dx.doi.org/10.1016/j.ajhg.2019.05.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6562021PMC
June 2019

Deep-coverage whole genome sequences and blood lipids among 16,324 individuals.

Nat Commun 2018 08 23;9(1):3391. Epub 2018 Aug 23.

School of Medicine, University of Maryland, Baltimore, MD, 21201, USA.

Large-scale deep-coverage whole-genome sequencing (WGS) is now feasible and offers potential advantages for locus discovery. We perform WGS in 16,324 participants from four ancestries at mean depth >29X and analyze genotypes with four quantitative traits-plasma total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. Common variant association yields known loci except for few variants previously poorly imputed. Rare coding variant association yields known Mendelian dyslipidemia genes but rare non-coding variant association detects no signals. A high 2M-SNP LDL-C polygenic score (top 5th percentile) confers similar effect size to a monogenic mutation (~30 mg/dl higher for each); however, among those with severe hypercholesterolemia, 23% have a high polygenic score and only 2% carry a monogenic mutation. At these sample sizes and for these phenotypes, the incremental value of WGS for discovery is limited but WGS permits simultaneous assessment of monogenic and polygenic models to severe hypercholesterolemia.
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http://dx.doi.org/10.1038/s41467-018-05747-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6107638PMC
August 2018
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