Publications by authors named "Päivi Pajukanta"

97 Publications

Fast estimation of genetic correlation for biobank-scale data.

Am J Hum Genet 2021 Nov 24. Epub 2021 Nov 24.

Department of Computer Science, UCLA, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90094, USA. Electronic address:

Genetic correlation is an important parameter in efforts to understand the relationships among complex traits. Current methods that analyze individual genotype data for estimating genetic correlation are challenging to scale to large datasets. Methods that analyze summary data, while being computationally efficient, tend to yield estimates of genetic correlation with reduced precision. We propose SCORE (scalable genetic correlation estimator), a randomized method of moments estimator of genetic correlation that is both scalable and accurate. SCORE obtains more precise estimates of genetic correlations relative to summary-statistic methods that can be applied at scale; it achieves a 44% reduction in standard error relative to LD-score regression (LDSC) and a 20% reduction relative to high-definition likelihood (HDL) (averaged over all simulations). The efficiency of SCORE enables computation of genetic correlations on the UK Biobank dataset, consisting of ≈300 K individuals and ≈500 K SNPs, in a few h (orders of magnitude faster than methods that analyze individual data, such as GCTA). Across 780 pairs of traits in 291,273 unrelated white British individuals in the UK Biobank, SCORE identifies significant genetic correlation between 200 additional pairs of traits over LDSC (beyond the 245 pairs identified by both).
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http://dx.doi.org/10.1016/j.ajhg.2021.11.015DOI Listing
November 2021

Indole-3-Propionic Acid, a Gut-Derived Tryptophan Metabolite, Associates with Hepatic Fibrosis.

Nutrients 2021 Oct 5;13(10). Epub 2021 Oct 5.

Department of Clinical Nutrition, Institute of Public Health and Clinical Nutrition, University of Eastern Finland, 70211 Kuopio, Finland.

Background And Aims: Gut microbiota-derived metabolites play a vital role in maintenance of human health and progression of disorders, including obesity and type 2 diabetes (T2D). Indole-3-propionic acid (IPA), a gut-derived tryptophan metabolite, has been recently shown to be lower in individuals with obesity and T2D. IPA's beneficial effect on liver health has been also explored in rodent and cell models. In this study, we investigated the association of IPA with human liver histology and transcriptomics, and the potential of IPA to reduce hepatic stellate cell activation in vitro.

Methods: A total of 233 subjects (72% women; age 48.3 ± 9.3 years; BMI 43.1 ± 5.4 kg/m) undergoing bariatric surgery with detailed liver histology were included. Circulating IPA levels were measured using LC-MS and liver transcriptomics with total RNA-sequencing. LX-2 cells were used to study hepatoprotective effect of IPA in cells activated by TGF-β1.

Results: Circulating IPA levels were found to be lower in individuals with liver fibrosis compared to those without fibrosis ( = 0.039 for all participants; = 0.013 for 153 individuals without T2D). Accordingly, levels of circulating IPA associated with expression of 278 liver transcripts ( < 0.01) that were enriched for the genes regulating hepatic stellate cells (HSCs) activation and hepatic fibrosis signaling. Our results suggest that IPA may have hepatoprotective potential because it is able to reduce cell adhesion, cell migration and mRNA gene expression of classical markers of HSCs activation in LX-2 cells (all < 0.05).

Conclusion: The association of circulating IPA with liver fibrosis and the ability of IPA to reduce activation of LX-2 cells suggests that IPA may have a therapeutic potential. Further molecular studies are needed to investigate the mechanisms how IPA can ameliorate hepatic fibrosis.
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http://dx.doi.org/10.3390/nu13103509DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538297PMC
October 2021

Electrical impedance tomography for non-invasive identification of fatty liver infiltrate in overweight individuals.

Sci Rep 2021 Oct 6;11(1):19859. Epub 2021 Oct 6.

Department of Bioengineering, UCLA, Los Angeles, CA, USA.

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of cardiometabolic diseases in overweight individuals. While liver biopsy is the current gold standard to diagnose NAFLD and magnetic resonance imaging (MRI) is a non-invasive alternative still under clinical trials, the former is invasive and the latter costly. We demonstrate electrical impedance tomography (EIT) as a portable method for detecting fatty infiltrate. We enrolled 19 overweight subjects to undergo liver MRI scans, followed by EIT measurements. The MRI images provided the a priori knowledge of the liver boundary conditions for EIT reconstruction, and the multi-echo MRI data quantified liver proton-density fat fraction (PDFF%) to validate fat infiltrate. Using the EIT electrode belts, we circumferentially injected pairwise current to the upper abdomen, followed by acquiring the resulting surface-voltage to reconstruct the liver conductivity. Pearson's correlation analyses compared EIT conductivity or MRI PDFF with body mass index, age, waist circumference, height, and weight variables. We reveal that the correlation between liver EIT conductivity or MRI PDFF with demographics is statistically insignificant, whereas liver EIT conductivity is inversely correlated with MRI PDFF (R = -0.69, p = 0.003, n = 16). As a pilot study, EIT conductivity provides a portable method for operator-independent and cost-effective detection of hepatic steatosis.
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http://dx.doi.org/10.1038/s41598-021-99132-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494919PMC
October 2021

Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes.

Genome Med 2021 08 2;13(1):123. Epub 2021 Aug 2.

Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA.

Background: Obesity predisposes individuals to multiple cardiometabolic disorders, including type 2 diabetes (T2D). As body mass index (BMI) cannot reliably differentiate fat from lean mass, the metabolically detrimental abdominal obesity has been estimated using waist-hip ratio (WHR). Waist-hip ratio adjusted for body mass index (WHRadjBMI) in turn is a well-established sex-specific marker for abdominal fat and adiposity, and a predictor of adverse metabolic outcomes, such as T2D. However, the underlying genes and regulatory mechanisms orchestrating the sex differences in obesity and body fat distribution in humans are not well understood.

Methods: We searched for genetic master regulators of WHRadjBMI by employing integrative genomics approaches on human subcutaneous adipose RNA sequencing (RNA-seq) data (n ~ 1400) and WHRadjBMI GWAS data (n ~ 700,000) from the WHRadjBMI GWAS cohorts and the UK Biobank (UKB), using co-expression network, transcriptome-wide association study (TWAS), and polygenic risk score (PRS) approaches. Finally, we functionally verified our genomic results using gene knockdown experiments in a human primary cell type that is critical for adipose tissue function.

Results: Here, we identified an adipose gene co-expression network that contains 35 obesity GWAS genes and explains a significant amount of polygenic risk for abdominal obesity and T2D in the UKB (n = 392,551) in a sex-dependent way. We showed that this network is preserved in the adipose tissue data from the Finnish Kuopio Obesity Study and Mexican Obesity Study. The network is controlled by a novel adipose master transcription factor (TF), TBX15, a WHRadjBMI GWAS gene that regulates the network in trans. Knockdown of TBX15 in human primary preadipocytes resulted in changes in expression of 130 network genes, including the key adipose TFs, PPARG and KLF15, which were significantly impacted (FDR < 0.05), thus functionally verifying the trans regulatory effect of TBX15 on the WHRadjBMI co-expression network.

Conclusions: Our study discovers a novel key function for the TBX15 TF in trans regulating an adipose co-expression network of 347 adipose, mitochondrial, and metabolically important genes, including PPARG, KLF15, PPARA, ADIPOQ, and 35 obesity GWAS genes. Thus, based on our converging genomic, transcriptional, and functional evidence, we interpret the role of TBX15 to be a main transcriptional regulator in the adipose tissue and discover its importance in human abdominal obesity.
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http://dx.doi.org/10.1186/s13073-021-00939-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8327600PMC
August 2021

Molecular pathways behind acquired obesity: Adipose tissue and skeletal muscle multiomics in monozygotic twin pairs discordant for BMI.

Cell Rep Med 2021 Apr 30;2(4):100226. Epub 2021 Mar 30.

Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

Tissue-specific mechanisms prompting obesity-related development complications in humans remain unclear. We apply multiomics analyses of subcutaneous adipose tissue and skeletal muscle to examine the effects of acquired obesity among 49 BMI-discordant monozygotic twin pairs. Overall, adipose tissue appears to be more affected by excess body weight than skeletal muscle. In heavier co-twins, we observe a transcriptional pattern of downregulated mitochondrial pathways in both tissues and upregulated inflammatory pathways in adipose tissue. In adipose tissue, heavier co-twins exhibit lower creatine levels; in skeletal muscle, glycolysis- and redox stress-related protein and metabolite levels remain higher. Furthermore, metabolomics analyses in both tissues reveal that several proinflammatory lipids are higher and six of the same lipid derivatives are lower in acquired obesity. Finally, in adipose tissue, but not in skeletal muscle, mitochondrial downregulation and upregulated inflammation are associated with a fatty liver, insulin resistance, and dyslipidemia, suggesting that adipose tissue dominates in acquired obesity.
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http://dx.doi.org/10.1016/j.xcrm.2021.100226DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8080113PMC
April 2021

Integrative analysis of liver-specific non-coding regulatory SNPs associated with the risk of coronary artery disease.

Am J Hum Genet 2021 03 23;108(3):411-430. Epub 2021 Feb 23.

A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, 70211 Kuopio, Finland. Electronic address:

Genetic factors underlying coronary artery disease (CAD) have been widely studied using genome-wide association studies (GWASs). However, the functional understanding of the CAD loci has been limited by the fact that a majority of GWAS variants are located within non-coding regions with no functional role. High cholesterol and dysregulation of the liver metabolism such as non-alcoholic fatty liver disease confer an increased risk of CAD. Here, we studied the function of non-coding single-nucleotide polymorphisms in CAD GWAS loci located within liver-specific enhancer elements by identifying their potential target genes using liver cis-eQTL analysis and promoter Capture Hi-C in HepG2 cells. Altogether, 734 target genes were identified of which 121 exhibited correlations to liver-related traits. To identify potentially causal regulatory SNPs, the allele-specific enhancer activity was analyzed by (1) sequence-based computational predictions, (2) quantification of allele-specific transcription factor binding, and (3) STARR-seq massively parallel reporter assay. Altogether, our analysis identified 1,277 unique SNPs that display allele-specific regulatory activity. Among these, susceptibility enhancers near important cholesterol homeostasis genes (APOB, APOC1, APOE, and LIPA) were identified, suggesting that altered gene regulatory activity could represent another way by which genetic variation regulates serum lipoprotein levels. Using CRISPR-based perturbation, we demonstrate how the deletion/activation of a single enhancer leads to changes in the expression of many target genes located in a shared chromatin interaction domain. Our integrative genomics approach represents a comprehensive effort in identifying putative causal regulatory regions and target genes that could predispose to clinical manifestation of CAD by affecting liver function.
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http://dx.doi.org/10.1016/j.ajhg.2021.02.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8008493PMC
March 2021

Differential Mitochondrial Gene Expression in Adipose Tissue Following Weight Loss Induced by Diet or Bariatric Surgery.

J Clin Endocrinol Metab 2021 04;106(5):1312-1324

Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Finland.

Context: Mitochondria are essential for cellular energy homeostasis, yet their role in subcutaneous adipose tissue (SAT) during different types of weight-loss interventions remains unknown.

Objective: To investigate how SAT mitochondria change following diet-induced and bariatric surgery-induced weight-loss interventions in 4 independent weight-loss studies.

Methods: The DiOGenes study is a European multicenter dietary intervention with an 8-week low caloric diet (LCD; 800 kcal/d; n = 261) and 6-month weight-maintenance (n = 121) period. The Kuopio Obesity Surgery study (KOBS) is a Roux-en-Y gastric bypass (RYGB) surgery study (n = 172) with a 1-year follow-up. We associated weight-loss percentage with global and 2210 mitochondria-related RNA transcripts in linear regression analysis adjusted for age and sex. We repeated these analyses in 2 studies. The Finnish CRYO study has a 6-week LCD (800-1000 kcal/d; n = 19) and a 10.5-month follow-up. The Swedish DEOSH study is a RYGB surgery study with a 2-year (n = 49) and 5-year (n = 37) follow-up.

Results: Diet-induced weight loss led to a significant transcriptional downregulation of oxidative phosphorylation (DiOGenes; ingenuity pathway analysis [IPA] z-scores: -8.7 following LCD, -4.4 following weight maintenance; CRYO: IPA z-score: -5.6, all P < 0.001), while upregulation followed surgery-induced weight loss (KOBS: IPA z-score: 1.8, P < 0.001; in DEOSH: IPA z-scores: 4.0 following 2 years, 0.0 following 5 years). We confirmed an upregulated oxidative phosphorylation at the proteomics level following surgery (IPA z-score: 3.2, P < 0.001).

Conclusions: Differentially regulated SAT mitochondria-related gene expressions suggest qualitative alterations between weight-loss interventions, providing insights into the potential molecular mechanistic targets for weight-loss success.
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http://dx.doi.org/10.1210/clinem/dgab072DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8063261PMC
April 2021

Protein Phosphatase 1 Regulatory Subunit 3B Genotype at rs4240624 Has a Major Effect on Gallbladder Bile Composition.

Hepatol Commun 2021 02 25;5(2):244-257. Epub 2020 Nov 25.

Institute of Public Health and Clinical Nutrition University of Eastern Finland Kuopio Finland.

The protein phosphatase 1 regulatory subunit 3B () gene is a target of farnesoid X receptor (), which is a major regulator of bile acid metabolism. Both and have been suggested to take part in glycogen metabolism, which may explain the association of gene variants with altered hepatic computed tomography attenuation. We analyzed the effect of rs4240624 variant on bile acid composition in individuals with obesity. The study cohort consisted of 242 individuals from the Kuopio Obesity Surgery Study (73 men, 169 women, age 47.6 ± 9.0 years, body mass index 43.2 ± 5.4 kg/m) with genotype and liver RNA sequencing (RNA-seq) data available. Fasting plasma and gallbladder bile samples were collected from 50 individuals. Bile acids in plasma did not differ based on the rs4240624 genotype. However, the concentration of total bile acids (109 ± 55 vs. 35 ± 19 mM;  = 1.0 × 10) and all individual bile acids (also 7α-hydroxy-4-cholesten-3-one [C4]) measured from bile were significantly lower in those with the AG genotype compared to those with the AA genotype. In addition, total cholesterol ( = 0.011) and phospholipid ( = 0.001) levels were lower in individuals with the AG genotype, but cholesterol saturation index did not differ, indicating that the decrease in cholesterol and phospholipid levels was secondary to the change in bile acids. Liver RNA-seq data demonstrated that expression of , tankyrase (), chromosome 8 clone RP11-10A14.5 ( []), chromosome 8 clone RP11-375N15.1 (), and chromosome 8, clone RP11-10A14 () associated with the genotype. In addition, genes enriched in transmembrane transport and phospholipid binding pathways were associated with the genotype. The rs4240624 variant in has a major effect on the composition of gallbladder bile. Other transcripts in the same loci may be important mediators of the variant effect.
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http://dx.doi.org/10.1002/hep4.1630DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7850313PMC
February 2021

Further evidence supporting a potential role for ADH1B in obesity.

Sci Rep 2021 01 21;11(1):1932. Epub 2021 Jan 21.

South Texas Diabetes and Obesity Institute Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg/Harlingen/Brownsville, TX, USA.

Insulin is an essential hormone that regulates glucose homeostasis and metabolism. Insulin resistance (IR) arises when tissues fail to respond to insulin, and it leads to serious health problems including Type 2 Diabetes (T2D). Obesity is a major contributor to the development of IR and T2D. We previously showed that gene expression of alcohol dehydrogenase 1B (ADH1B) was inversely correlated with obesity and IR in subcutaneous adipose tissue of Mexican Americans. In the current study, a meta-analysis of the relationship between ADH1B expression and BMI in Mexican Americans, African Americans, Europeans, and Pima Indians verified that BMI was increased with decreased ADH1B expression. Using established human subcutaneous pre-adipocyte cell lines derived from lean (BMI < 30 kg m) or obese (BMI ≥ 30 kg m) donors, we found that ADH1B protein expression increased substantially during differentiation, and overexpression of ADH1B inhibited fatty acid binding protein expression. Mature adipocytes from lean donors expressed ADH1B at higher levels than obese donors. Insulin further induced ADH1B protein expression as well as enzyme activity. Knockdown of ADH1B expression decreased insulin-stimulated glucose uptake. Our findings suggest that ADH1B is involved in the proper development and metabolic activity of adipose tissues and this function is suppressed by obesity.
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http://dx.doi.org/10.1038/s41598-020-80563-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820614PMC
January 2021

Serum aromatic and branched-chain amino acids associated with NASH demonstrate divergent associations with serum lipids.

Liver Int 2021 04 5;41(4):754-763. Epub 2020 Dec 5.

Institute of Public Health and Clinical Nutrition, Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) has been associated with multiple metabolic abnormalities. By applying a non-targeted metabolomics approach, we aimed at investigating whether serum metabolite profile that associates with NAFLD would differ in its association with NAFLD-related metabolic risk factors.

Methods & Results: A total of 233 subjects (mean ± SD: 48.3 ± 9.3 years old; BMI: 43.1 ± 5.4 kg/m ; 64 male) undergoing bariatric surgery were studied. Of these participants, 164 with liver histology could be classified as normal liver (n = 79), simple steatosis (SS, n = 40) or non-alcoholic steatohepatitis (NASH, n = 45). Among the identified fasting serum metabolites with higher levels in those with NASH when compared to those with normal phenotype were the aromatic amino acids (AAAs: tryptophan, tyrosine and phenylalanine), the branched-chain amino acids (BCAAs: leucine and isoleucine), a phosphatidylcholine (PC(16:0/16:1)) and uridine (all FDRp < 0.05). Only tryptophan was significantly higher in those with NASH compared to those with SS (FDRp < 0.05). Only the AAAs tryptophan and tyrosine correlated positively with serum total and LDL cholesterol (FDRp < 0.1), and accordingly, with liver LDLR at mRNA expression level. In addition, tryptophan was the single AA associated with liver DNA methylation of CpG sites known to be differentially methylated in those with NASH.

Conclusions: We found that serum levels of the NASH-related AAAs and BCAAs demonstrate divergent associations with serum lipids. The specific correlation of tryptophan with LDL-c may result from the molecular events affecting LDLR mRNA expression and NASH-associated methylation of genes in the liver.
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http://dx.doi.org/10.1111/liv.14743DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048463PMC
April 2021

RIPK1 gene variants associate with obesity in humans and can be therapeutically silenced to reduce obesity in mice.

Nat Metab 2020 10 28;2(10):1113-1125. Epub 2020 Sep 28.

Cardiometabolic microRNA Laboratory, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.

Obesity is a major public health burden worldwide and is characterized by chronic low-grade inflammation driven by the cooperation of the innate immune system and dysregulated metabolism in adipose tissue and other metabolic organs. Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is a central regulator of inflammatory cell function that coordinates inflammation, apoptosis and necroptosis in response to inflammatory stimuli. Here we show that genetic polymorphisms near the human RIPK1 locus associate with increased RIPK1 gene expression and obesity. We show that one of these single nucleotide polymorphisms is within a binding site for E4BP4 and increases RIPK1 promoter activity and RIPK1 gene expression in adipose tissue. Therapeutic silencing of RIPK1 in vivo in a mouse model of diet-induced obesity dramatically reduces fat mass, total body weight and improves insulin sensitivity, while simultaneously reducing macrophage and promoting invariant natural killer T cell accumulation in adipose tissue. These findings demonstrate that RIPK1 is genetically associated with obesity, and reducing RIPK1 expression is a potential therapeutic approach to target obesity and related diseases.
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http://dx.doi.org/10.1038/s42255-020-00279-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8362891PMC
October 2020

The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance.

PLoS Genet 2020 09 14;16(9):e1009018. Epub 2020 Sep 14.

Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America.

Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes and obesity and insulin resistance. To disentangle whether obesity causally drives prediabetes and insulin resistance already in non-diabetic individuals, we utilized the UK Biobank and METSIM cohort to perform a Mendelian randomization (MR) analyses in the non-diabetic individuals. Our results suggest that both prediabetes and systemic insulin resistance are caused by obesity (p = 1.2×10-3 and p = 3.1×10-24). As obesity reflects the amount of body fat, we next studied how adipose tissue affects insulin resistance. We performed both bulk RNA-sequencing and single nucleus RNA sequencing on frozen human subcutaneous adipose biopsies to assess adipose cell-type heterogeneity and mitochondrial (MT) gene expression in insulin resistance. We discovered that the adipose MT gene expression and body fat percent are both independently associated with insulin resistance (p≤0.05 for each) when adjusting for the decomposed adipose cell-type proportions. Next, we showed that these 3 factors, adipose MT gene expression, body fat percent, and adipose cell types, explain a substantial amount (44.39%) of variance in insulin resistance and can be used to predict it (p≤2.64×10-5 in 3 independent human cohorts). In summary, we demonstrated that obesity is a strong determinant of both prediabetes and insulin resistance, and discovered that individuals' adipose cell-type composition, adipose MT gene expression, and body fat percent predict their insulin resistance, emphasizing the critical role of adipose tissue in systemic insulin resistance.
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http://dx.doi.org/10.1371/journal.pgen.1009018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515203PMC
September 2020

Adiponectin GWAS loci harboring extensive allelic heterogeneity exhibit distinct molecular consequences.

PLoS Genet 2020 09 11;16(9):e1009019. Epub 2020 Sep 11.

Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America.

Loci identified in genome-wide association studies (GWAS) can include multiple distinct association signals. We sought to identify the molecular basis of multiple association signals for adiponectin, a hormone involved in glucose regulation secreted almost exclusively from adipose tissue, identified in the Metabolic Syndrome in Men (METSIM) study. With GWAS data for 9,262 men, four loci were significantly associated with adiponectin: ADIPOQ, CDH13, IRS1, and PBRM1. We performed stepwise conditional analyses to identify distinct association signals, a subset of which are also nearly independent (lead variant pairwise r2<0.01). Two loci exhibited allelic heterogeneity, ADIPOQ and CDH13. Of seven association signals at the ADIPOQ locus, two signals colocalized with adipose tissue expression quantitative trait loci (eQTLs) for three transcripts: trait-increasing alleles at one signal were associated with increased ADIPOQ and LINC02043, while trait-increasing alleles at the other signal were associated with decreased ADIPOQ-AS1. In reporter assays, adiponectin-increasing alleles at two signals showed corresponding directions of effect on transcriptional activity. Putative mechanisms for the seven ADIPOQ signals include a missense variant (ADIPOQ G90S), a splice variant, a promoter variant, and four enhancer variants. Of two association signals at the CDH13 locus, the first signal consisted of promoter variants, including the lead adipose tissue eQTL variant for CDH13, while a second signal included a distal intron 1 enhancer variant that showed ~2-fold allelic differences in transcriptional reporter activity. Fine-mapping and experimental validation demonstrated that multiple, distinct association signals at these loci can influence multiple transcripts through multiple molecular mechanisms.
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http://dx.doi.org/10.1371/journal.pgen.1009019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7511027PMC
September 2020

ACE2 expression in adipose tissue is associated with COVID-19 cardio-metabolic risk factors and cell type composition.

medRxiv 2020 Aug 14. Epub 2020 Aug 14.

COVID-19 severity has varied widely, with demographic and cardio-metabolic factors increasing risk of severe reactions to SARS-CoV-2 infection, but the underlying mechanisms for this remain uncertain. We investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 ( ), which has been shown to act as a receptor for SARS-CoV-2 cellular entry. In a meta-analysis of three independent studies including up to 1,471 participants, lower adipose tissue expression was associated with adverse cardio-metabolic health indices including type 2 diabetes (T2D) and obesity status, higher serum fasting insulin and BMI, and lower serum HDL levels (P<5.32x10 ). expression levels were also associated with estimated proportions of cell types in adipose tissue; lower expression was associated with a lower proportion of microvascular endothelial cells (P=4.25x10 ) and higher macrophage proportion (P=2.74x10 ), suggesting a link to inflammation. Despite an estimated heritability of 32%, we did not identify any proximal or distal genetic variants (eQTLs) associated with adipose tissue expression. Our results demonstrate that at-risk individuals have lower background levels in this highly relevant tissue. Further studies will be required to establish how this may contribute to increased COVID-19 severity.
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http://dx.doi.org/10.1101/2020.08.11.20171108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7430606PMC
August 2020

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM.

Sci Rep 2020 07 3;10(1):11019. Epub 2020 Jul 3.

Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.

Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: (1) human differentiating preadipocytes in vitro, (2) fresh mouse brain tissue, and (3) human frozen adipose tissue (AT) from six individuals. All three data sets showed evidence of extranuclear RNA contamination, and we observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq, our clustering strategy also successfully filtered single-cell RNA-seq data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.
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http://dx.doi.org/10.1038/s41598-020-67513-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335186PMC
July 2020

Publisher Correction: Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

Nat Commun 2020 06 3;11(1):2891. Epub 2020 Jun 3.

Department of Human Genetics, David Geffen School ofMedicine at UCLA, Los Angeles, CA, 90095, USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41467-020-16607-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270096PMC
June 2020

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

Nat Commun 2020 04 24;11(1):1971. Epub 2020 Apr 24.

Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.

We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.
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http://dx.doi.org/10.1038/s41467-020-15816-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7181686PMC
April 2020

Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models.

J Comput Biol 2020 04 20;27(4):599-612. Epub 2020 Feb 20.

Neurology, UCLA, Los Angeles, California.

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.
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http://dx.doi.org/10.1089/cmb.2019.0325DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185352PMC
April 2020

Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution.

Hum Mol Genet 2019 12;28(24):4161-4172

Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.

Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist-hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.
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http://dx.doi.org/10.1093/hmg/ddz263DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202621PMC
December 2019

A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context.

Nat Commun 2019 10 21;10(1):4788. Epub 2019 Oct 21.

Department of Computational Biology - USR 3756 CNRS, Institut Pasteur, Paris, France.

Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders. However, these studies have relied on univariate analyses, reducing power and limiting context-specific understanding. Here we aim to provide an integrated perspective of the genetic basis of metabolites by leveraging the Finnish Metabolic Syndrome In Men (METSIM) cohort, a unique genetic resource which contains metabolic measurements, mostly lipids, across distinct time points as well as information on statin usage. We increase effective sample size by an average of two-fold by applying the Covariates for Multi-phenotype Studies (CMS) approach, identifying 588 significant SNP-metabolite associations, including 228 new associations. Our analysis pinpoints a small number of master metabolic regulator genes, balancing the relative proportion of dozens of metabolite levels. We further identify associations to changes in metabolic levels across time as well as genetic interactions with statin at both the master metabolic regulator and genome-wide level.
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http://dx.doi.org/10.1038/s41467-019-12703-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803661PMC
October 2019

Novel Lipid Long Intervening Noncoding RNA, Oligodendrocyte Maturation-Associated Long Intergenic Noncoding RNA, Regulates the Liver Steatosis Gene Stearoyl-Coenzyme A Desaturase As an Enhancer RNA.

Hepatol Commun 2019 Oct 14;3(10):1356-1372. Epub 2019 Aug 14.

Department of Human Genetics David Geffen School of Medicine at University of California Los Angeles Los Angeles CA.

The global obesity epidemic is driving the concomitant rise in nonalcoholic fatty liver disease (NAFLD). To identify new genes involved in central liver functions, we examined liver RNA-sequence data from 259 patients who underwent morbidly obese bariatric surgery. Of these patients, 84 had normal liver histology, 40 simple steatosis, 43 nonalcoholic steatohepatitis, and the remaining 92 patients had varying degrees of NAFLD based on liver histology. We discovered oligodendrocyte maturation-associated long intergenic noncoding RNA () a long intervening noncoding RNA (lincRNA) in a human liver co-expression network (n = 75 genes) that was strongly associated with statin use and serum triglycerides (TGs). liver expression was highly correlated with the expression of known cholesterol biosynthesis genes and stearoyl-coenzyme A desaturase (). is the rate-limiting enzyme in monounsaturated fatty acids and a key TG gene that is known to be up-regulated in liver steatosis and NAFLD and resides adjacent to on the human chromosome 10q24.31. Next, we functionally demonstrated that regulates as an enhancer-RNA (eRNA), thus describing the first lincRNA that functions as an eRNA to regulate lipid metabolism. Specifically, we show that promotes liver expression of in through regional chromosomal DNA-DNA looping interactions. The primate-specific lincRNA is a novel epigenetic regulator of the key TG and NAFLD gene .
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http://dx.doi.org/10.1002/hep4.1413DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6771395PMC
October 2019

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

Am J Hum Genet 2019 10 26;105(4):773-787. Epub 2019 Sep 26.

Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA. Electronic address:

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.
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http://dx.doi.org/10.1016/j.ajhg.2019.09.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6817527PMC
October 2019

Reverse gene-environment interaction approach to identify variants influencing body-mass index in humans.

Nat Metab 2019 06 14;1(6):630-642. Epub 2019 Jun 14.

Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA, 90095.

Identifying gene-environment interactions (GxEs) contributing to human cardiometabolic disorders is challenging. Here we apply a reverse GxE candidate search by deriving candidate variants from promoter-enhancer interactions that respond to dietary fatty acid challenge through altered chromatin accessibility in human primary adipocytes. We then test all variants residing in the lipid-responsive open chromatin sites within adipocyte promoter-enhancer contacts for interaction effects between the genotype and dietary saturated fat intake on body mass index (BMI) in the UK Biobank. We discover 14 novel GxE variants in 12 lipid-responsive promoters, including well-known lipid genes (, and ) and novel genes, such as , for which we provide further functional and integrative genomics evidence. We further identify 24 GxE variants in enhancers, totaling 38 new GxE variants for BMI in the UK Biobank, demonstrating that molecular genomics data produced in physiologically relevant contexts can discover new functional GxE mechanisms in humans.
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http://dx.doi.org/10.1038/s42255-019-0071-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6752726PMC
June 2019

Reverse GWAS: Using genetics to identify and model phenotypic subtypes.

PLoS Genet 2019 04 5;15(4):e1008009. Epub 2019 Apr 5.

Department of Medicine, UCSF, San Francisco, California, United States of America.

Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automated statistical approaches to subtype definition valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a novel decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show that modelling these features can be crucial for power and calibration. We validate RGWAS in practice by recovering a recently discovered stress subtype in major depression. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests subtypes may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting the subtypes have potential translational value.
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http://dx.doi.org/10.1371/journal.pgen.1008009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6469799PMC
April 2019

Genetic and environmental perturbations lead to regulatory decoherence.

Elife 2019 03 5;8. Epub 2019 Mar 5.

Department of Ecology and Evolution, Princeton University, Princeton, United States.

Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease.

Editorial Note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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http://dx.doi.org/10.7554/eLife.40538DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6400502PMC
March 2019

GENOMICS AND SYSTEMS BIOLOGY APPROACHES IN THE STUDY OF LIPID DISORDERS.

Rev Invest Clin 2018 ;70(5):217-223

Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, USA.

Cardiovascular disease (CVD) is a broad definition for diseases of the heart and blood vessels with high mortality and morbidity worldwide. Atherosclerosis and hypertension are the most common causes of CVD, and multiple factors confer the susceptibility. Some of the predisposing factors are modifiable such as diet, smoking, and exercise, whereas others, including age, sex, and individual's genetic variations contributing to the CVD composition traits, are non-modifiable. This latter group includes serum lipid traits. High serum lipid levels, specifically high levels of serum low-density lipoprotein cholesterol and triglycerides, are well-established key risk factors of atherosclerosis. This review will discuss genomics and systems biology approaches in the study of common dyslipidemias. The non-Mendelian forms of dyslipidemias are highly complex, and the molecular mechanisms underlying these polygenic lipid disorders are estimated to involve hundreds of genes. Interactions between the different genes and environmental factors also contribute to the clinical outcomes; however, very little is known about these interactions and their molecular mechanisms. To better address the complex genetic architecture and multiple properties leading to high serum lipid levels, networks and systems approach combining information at genomic, transcriptomics, methylomics, proteomics, metabolomics, and phenome level are being developed, with the ultimate goal to elucidate the cascade of dynamic changes leading to CVD in humans. (REV INVEST CLIN. 2018;70:217-23).
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http://dx.doi.org/10.24875/RIC.18002576DOI Listing
December 2018

Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits.

Am J Hum Genet 2018 10;103(4):535-552

Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA. Electronic address:

Although recent studies provide evidence for a common genetic basis between complex traits and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific manner remains elusive. Here, we have quantified the overlap of genes identified through large-scale genome-wide association studies (GWASs) for 62 complex traits and diseases with genes containing mutations known to cause 20 broad categories of Mendelian disorders. We identified a significant enrichment of genes linked to phenotypically matched Mendelian disorders in GWAS gene sets; of the total 1,240 comparisons, a higher proportion of phenotypically matched or related pairs (n = 50 of 92 [54%]) than phenotypically unmatched pairs (n = 27 of 1,148 [2%]) demonstrated significant overlap, confirming a phenotype-specific enrichment pattern. Further, we observed elevated GWAS effect sizes near genes linked to phenotypically matched Mendelian disorders. Finally, we report examples of GWAS variants localized at the transcription start site or physically interacting with the promoters of genes linked to phenotypically matched Mendelian disorders. Our results are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are dysregulated by non-coding variants in complex traits and demonstrate how leveraging findings from related Mendelian disorders and functional genomic datasets can prioritize genes that are putatively dysregulated by local and distal non-coding GWAS variants.
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http://dx.doi.org/10.1016/j.ajhg.2018.08.017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174356PMC
October 2018

Author Correction: Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS.

Nat Commun 2018 08 22;9(1):3472. Epub 2018 Aug 22.

Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.

In the original version of this Article, Supplementary Table 10 contained incorrect primer sequences for the mobility shift assay for SNP rs4776984. These errors have now been fixed and the corrected version of the Supplementary Information PDF is available to download from the HTML version of the Article.
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http://dx.doi.org/10.1038/s41467-018-05849-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6105720PMC
August 2018
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