dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Sex-dimorphic and sex-combined meta-analyses for FG/FI. We obtained FG/FI sex-specific results for up to 73,089/50,404 women and 67,506/47,806 men from population-based studies; sex-combined meta-analyses for these traits additionally included 13,613 individuals from four family-based studies. All studies were of European ancestry, and were based on GWAS imputed to the HapMap II CEU reference panel  16  or Metabochip array data  17  (Supplementary Data 1). We further improved the genetic variant genome-wide coverage by imputing the summary statistics of FG/FI sex-dimorphic and sex-combined meta-analyses to 1000 Genomes Project density using the SS-imp software ("Methods")  18. We investigated the sex-dimorphic and homogeneous effects of 8. 7 million autosomal single-nucleotide polymorphisms (SNPs) on FG/FI under an additive genetic model.
4 × 10 -4 ) in the current dataset, but no nominal effect on T2D risk in European ancestry populations (P rs2866413 = 8. 8 × 10 -4 , P rs227368 = 4. 1 × 10 -5 ). Approximate conditional analyses using GCTA  33, 34  showed that the rs223486 association with FG was only partially driven by rs228614 variant at the same locus for which previously a significant association with multiple sclerosis has been reported (rs223486, P conditional_rs228614 = 0. 00035, r 2 EUR = 0. 46) ("Methods")  31. Conversely, the rs223486 association with FG was not explained by rs3774959 variant at MANBA previously associated with ulcerative colitis (rs223486, P conditional_rs3774959 = 9. 6 × 10 -8 , r 2 EUR = 0. 12)  32  (Supplementary Fig. 1f-h ), suggesting a genetic relationship between glucose homeostasis and neurodegeneration.
More specifically, we tested three scenarios of allelic effects on the two sexes: (1) no heterogeneity between the two sexes; (2) effects on both sexes with the presence of heterogeneity between them; and (3) effect specific to one sex only, where we used women as an example. Within each scenario, we evaluated a range of CAF (ranging from 0. 05 to 0. 5) and effect sizes (ranging from 0 to 0. 1 in SD units). In addition, we estimated the power (P < 5 × 10 -8 ) of the Cochran's Q-test for heterogeneity (implemented in the GWAMA software  14, 15  ) under these three different models. We performed simulations on 70,000 men and 70,000 women, a sample similar by size and sex ratio to our study ("Methods"), to evaluate the power of our analysis to detect sex dimorphism at established FG (n = 36) and FI (n = 19) loci after Bonferroni correction for multiple testing (P heterogeneity < 0.
We provided support for ZNF12 as a potential candidate in this locus through its expression in human beta cells and pancreatic islets, as well as higher RNA expression levels in women than in men in whole blood. Furthermore, ZNF12 is a quantitative trait locus for glucose and insulin levels in rats (Rat Genome Database: IDs 1643535, 2303575, 1357337  43  ). In humans, the lead SNP rs7798471 overlaps with the DNaseI hypersensitivity site from pancreatic adenocarcinoma (PA-TU-8988T, https://www. encodeproject. org/), which maps near the ZNF12 alternative transcript start site. Interestingly, the ZNF12non-coding variant rs7798471 lies within a conserved DNA region. It is in high LD with a number of Neanderthal methylated variants, and is present in the archaic genome of a Denisova individual, suggesting that this genomic region might have introgressed into modern humans through admixture with Neanderthals and Denisovans  44.
Methods Participating studies. The following collection of studies were used: (1) 38 GWAS, including up to 80,512 individuals genotyped using either Illumina or Affymetrix genome-wide SNP arrays; (2) 27 studies with up to 47,150 individuals genotyped using the iSELECT Metabochip array (~197 K SNPs) designed to support efficient large-scale follow-up of putative associations for glycemic and other metabolic and cardiovascular traits; (3) 8 studies, including up to 21,173 individuals genotyped for custom variant sets; and (4) 4 studies, including up to 13,613 individuals from four family-based studies (sex-combined meta-analyses only, as detailed below). Detailed descriptions on the participating studies are provided in Supplementary Data 1. All participants were of European ancestry, without diabetes and mostly adults, although data from a total of 8,222 adolescents were also included in the meta-analyses (ALSPAC, French Young controls/obese, Leipzig-childhood and NFBC86 studies).
All studies were approved by local ethics committees and all participants gave informed consent. Traits. Data were collected from participating studies with FG measured in mmol/ L (N maxmen = 67,506, N maxwomen = 73,089) and FI measured in pmol/L (N maxmen = 47,806, N maxwomen = 50,404). Measures of FG made in whole blood were corrected to plasma level using the correction factor of 1. 13  49. FI was measured in serum. Similar to previous MAGIC efforts  22, 50, 51  , individuals were excluded from the analysis if they had a physician diagnosis of diabetes, were on diabetes treatment (oral or insulin), or had a fasting plasma glucose equal to or greater than 7 mmol/L. Individual studies applied further sample exclusions, including pregnancy, nonfasting individuals, and type 1 diabetes. Individuals from case-control studies were excluded if they had hospitalization or blood transfusion in the 2-3 months before phenotyping took place.
Untransformed FG and natural logarithm transformed FI were analyzed at a study level. Detailed descriptions of study-specific glycemic measurements are given in Supplementary Data 1. Untransformed FG and natural logarithm transformed FI, HOMA-B, and HOMA-IR were analyzed at a study level. Genotyping and quality control. Commercial genome-wide arrays, the Metabochip  17  or platforms with custom variant sets were used by individual studies for genotyping. Studies with genome-wide arrays undertook imputation of missing genotypes using the HapMap II CEU reference panel via MACH  52, 53  , IMPUTE  54, 55  , or BEAGLE 56 software (Supplementary Data 1). For each study, samples reflecting duplicates, low call rate, gender mismatch, or population outliers were excluded. Low-quality SNPs were excluded by the following criteria: call rate <0.
95, minor allele frequency (MAF) < 0. 01, minor allele count < 10, Hardy-Weinberg P value < 10 -4. After imputation, SNPs were also excluded for imputation quality score <0. 5. Imputation to the 1000G reference panel. We imputed the summary statistics for FG and FI (combined and sex-stratified) to the 1000 Genomes reference panel  57  using the summary statistics imputation method implemented in the SS-Imp v0. 5. 5 software  18, 58. We used the all-ancestries reference panel. SNPs with imputation quality score <0. 7 were excluded after imputation. Statistical analysis. Each study performed single SNP association for men and women separately (sex-specific). The additive genetic effect of each SNP was estimated using a linear regression model adjusting for age (if applicable), study site (if applicable), and principal components.
In case-control studies, the cases and controls were analyzed separately. Individual study results were corrected for residual inflation of the test statistics using genomic control (GC)  59. The GC lambda values were estimated using test statistics from all SNPs for the GWAS. In Metabochip studies, GC values were estimated from test statistics from 5,041 SNPs selected for follow-up of QT-interval associations, as we perceived these to have the lowest likelihood of common architecture of associations with glycemic traits  59. SNP effect estimates and their standard errors were combined by a fixed effect model with inverse variance weighting using the GWAMA v2. 2. 3 software within the following three meta-analysis strategies: (1) sex-specific, where allelic effect estimates were combined separately within each sex (male-specific or femalespecific), (2) sex-dimorphic, where male-and female-specific estimates were combined by allowing for heterogeneity in allelic effects between women and men (chi-squared distribution with two-degrees of freedom)  14  and (3) sex-combined, where allelic effect estimates from men and women were combined.
Studies with highly related individuals (Dundee, FamHS, FHS and Sardinia) were included only in the sex-combined meta-analysis (men and women were analyzed together at a study-level and an additional adjustment for sex was made). In addition, the heterogeneity of allelic effects between sexes was assessed using Cochran's Q-test. Cochran's statistic provides a test of heterogeneity of allelic effects at the jth SNP, and has an approximate chi-squared distribution with N j-1 degrees of freedom under the null hypothesis of consistency where N j denotes the number of studies for which an allelic effect is reported. Both the sex-dimorphic meta-analysis framework and Cochran's Q test for heterogeneity have been implemented in the GWAMA software  15. The lambda values for FG and FI sex-differentiated and Cochran's Q test were as follows: FG (λ sex-differentiated_test = 1.
SNPs were considered as novel if located more than 500 kb from, and not in LD (HapMap CEU/ 1000 Genomes EUR: r 2 < 0. 01) with any variant already known to be associated with the trait. Approximate conditional analysis. We performed approximate conditional analysis by using the Genome-Wide Complex Trait Analysis (GCTA) v1. 24. 4 tool to assess whether the signals within the MANBA/UBE2D3 genomic region associated with FG represented independent associations or the same shared signal with multiple sclerosis and ulcerative colitis  33, 34. GCTA implements an approximate conditional analysis of phenotype associations using GWAS summary statistics while incorporating LD information from a reference sample. Here, we used individual level genotype data from the PIVUS study (European ancestry) as the LD reference.
05 for the Cochran's Q test. Bidirectional two-sample MR analyses. We applied bidirectional MR to investigate the causality between WHRadjBMI and FI. MR provides estimates of the effect of modifiable exposures on disease unaffected by classical confounding or reverse causation, whenever randomized clinical trials are not feasible  [62] [63] [64]. Genetic and phenotype data were available from the UK Biobank cohort (214,924 women and 183,739 men) for obtaining genetic instruments for WHRadjBMI from the general population. To look at the reverse, i. e. , the potential causal effect of FI on WHRadjBMI, we used genetic instruments for FI and genome-wide summary results from the present study (50,404 women and 47,806 men). We used independent (r 2 < 0. 001) SNPs that reached genome-wide significance (P ≤ 5 × 10 -8 ) in the combined (women and men) WHR GWAS as instruments for WHR.
We obtained 222 WHRadjBMI SNPs for women and 222 WHRadjBMI SNPs for men. SNP-WHRadjBMI associations were expressed in terms of Z-scores. For FI, we used as instruments the 19 SNPs established for FI by MAGIC (Supplementary Data 3). The random-effect inverse-variance weighted (IVW) method was used to obtain the combined MR estimate from the causal estimates of each individual variant in the instrument derived by the ratio method  65. Standard errors were calculated using the Delta method  66. We employed MR-Egger regression to obtain causal estimates that are more robust to the inclusion of invalid instruments  67. We tested for the presence of a causal effect of (1) WHRadjBMI on FI in women, (2) FI on WHRadjBMI in women, (3) WHRadjBMI on FI in men, and (4) FI on WHRadjBMI in men. Heterogeneity in the IVW estimates from each individual variant was tested using Cochran's Q test.
The presence of directional pleiotropy was tested with the MR-Egger intercept test where a significant non-zero intercept term can be indicative of directional pleiotropy. We have additionally performed analyses of four causal relationships: WC adjBMI on FI in women and men and HCadjBMI on FI in women and men to assess, which fat depot drives the causal relationship between central adiposity and FI. All MR analyses were performed using the R package TwoSampleMR v0. 5. 4. Simulations to assess the power of tests to detect sex-heterogeneity under different scenarios. A range of scenarios of effects on the two sexes were considered and the power of three types of analysis (sex-combined, 2df sex-dimorphic and female-specific) to pick any associations with evidence for sex-heterogeneity was assessed.
Total RNA was extracted using Nucleospin RNA II kit (Macherey Nagel). For each sample, 1 µg of total RNA was transcribed into cDNA using the cDNA Archive Kit (Applied Biosystems ® ) or random primed first strand synthesis (Applied Biosystems ® ). Resulting cDNAs were diluted ten-fold and 4 µl of each sample were used in a 20 µl quantitative RT-PCR reaction including 10 µl of TaqMan gene expression master mix (Applied Biosystems ® ) and 1 µl of TaqMan gene expression assay (Applied Biosystems). Quantitative RT-PCR analyses were performed using the ABI 7900 HT SDS 2. 4, RQ manager v1. 2. 1, and DataAssist v3. 0 software and each sample was run in triplicate. Expression of genes was reported as a relationship to the respective tissue expression of three housekeeping genes (PPIA, B2M and HPRT).
RNA expression in blood. Look-ups for novel and known genes with evidence of sex heterogeneity were done in the whole blood RNA expression data from NTR and NESDA. For the NTR participants, venous (7-11 a. m) blood samples were drawn after overnight fasting. Within 20 min of sampling, heparinized whole blood was transferred into PAXgene Blood RNA tubes (Qiagen) and stored at -20 °C. The PAXgene tubes were shipped to the Rutgers University Cell and DNA Repository (RUCDR), USA, where RNA was extracted using Qiagen Universal liquid handling system (PAXgene extraction kits as per the manufacturer's protocol). For the NESDA subjects, venous overnight fasting (8-10 a. m. ) blood samples were obtained in one 7-ml heparin-coated tube (Greiner Bio-One, Monroe, NC). Between 10 and 60 min after blood draw, 2.
Products purified from single primer isothermal amplification were then fragmented and labeled with biotin using Encore Biotin Module (NuGEN). Prior to hybridization, the labeled cDNA was analyzed using electrophoresis to verify the appropriate size distribution (Caliper AMS90 with a HT DNA 5 K/RNA LabChip). Samples were hybridized to Affymetrix U219 array plates (GeneTitan). The U219 array contains 530,467 probes for 49,293 transcripts. All probes are 25 bases in length and designed to be "perfect match" complements to a designated transcript. Array hybridization, washing, staining, and scanning were carried out in an Affymetrix GeneTitan System per the manufacturer's protocol. Gene expression data were required to pass standard Affymetrix quality control metrics (Affymetrix expression console) before further analysis.
Quality assessment was made preand post-sample preparation on the 2100 Bioanalyzer (Agilent). Illumina Casava v. 1. 8. 2 software was used for base calling. Paired-end 101 bp length output reads were aligned to the human reference genome (hg19) with TopHat v. 2. 0. 2 70 using Bowtie v. 0. 12. 8  71. The TopHat parameters explicitly used are tophat -p 30 -G genes. gtf --library-type fr-unstranded -r 100 -F 0. 05 --microexon-search. Gene expression was measured as the normalized sum of expression of all exons. Exons were defined as non-overlapping unique exonic units  72. The dexseq_count python script ("Data availability") was used by counting uniquely mapped reads in each exon. Gene and exon expression normalizations were then performed using the TMM method  73  , and further normalization was applied by adjusting the expression to gene or exon length, respectively.
In addition, only the genes and exons that had reads mapped to them in at least 5% of the samples were kept. The Cufflinks tool v. 1. 3. 0 74 was used to detect novel genetic loci. Intergenic gene loci were kept if they did not overlap any GENCODE v. 12 gene  75  , UCSC and Ensembl gene structures, had exon-exon junction reads mapped to them, had at least two exons with no Ns, and were expressed (non-null read coverage) in at least 5% of the samples. Coding potential of these novel intergenic loci was assessed with the Coding Potential Assessment Tool v1. 2. 2  76. Gene expression in islet donors. Samples were stratified based upon glucose tolerance estimated from HbA1c, i. e. , donors with normal glucose tolerance (HbA1c < 6%, n = 51), IGT (6% ≤ HbA1c < 6. 5%, n = 15), and T2D (HbA1c ≥ 6.
5%, n = 12). A model adjusting for age and sex as implemented in the R Matrix eQTL package  77  was used to determine the expression of genes associated with T2D status. Genotyping was performed on the Illumina HumanOmniExpress 12v1 C chips and genotype calling was done with the Illumina Genome studio v2. 0 software. All the samples passed standard genotype quality control metrics: sample call rate >98%, only European ancestry assessed by principal component analysis comparisons with HapMap populations, gender matched, no relatedness, and no genome-wide heterozygosity outliers. SNPs were removed if SNP call rate <98% and Hardy-Weinberg equilibrium test P values <5. 7 × 10 -7. Individual genotypes were imputed to 1000 Genomes data, using IMPUTE2 and the March 2012 release of the 1000 Genomes Phase I panel.
The program SHAPEIT v2  78  was used for the pre-phasing. Probabilistic genotypes were used for the subsequent analyses and after imputation, SNPs were filtered using a MAF > 5% and an IMPUTE2 info value of >0. 8. eQTL analyses were carried out on samples from 89 individuals. Associations were computed between gene expression levels (eQTL) and top SNPs within 250 kb up-or downstream of each of these genes. We used a linear model adjusting for age and sex as implemented in the R Matrix eQTL v2 package  77. Results are show with P values with age and sex as covariates, after the false discovery rate (FDR) and obtained after doing 10,000 permutations. grant PUT PRG687 and institutional grant PP1GI19935 from Institute of Genomics, University of S. Männistö was supported by Academy of Finland (# 136895 and 141005).
Acknowledgements The participating studies were supported by the following grants:  Academy of Finland  ( 129293 ,  117844 ,  40758 ,  211497 ,  118590 ,  102318 ,  123885 ,  77299 ,  124243 ,  136895 ,  141005 ,  129322 ,  104781 ,  120315 ,  129269 ,  1114194 ); ADA (  7-08-MN-OK );  BBSRC  ( G20234 );  BHF  ( PG/07/133/24260 );  Clinical Translational Science Institute  ( UL1RR033176 );  Croatian Ministry of Science  ( 216-1080315-0302 );  Directorate C -Public Health  and  Risk Assessment , Health & Consumer Protection (2004310);  Dutch Kidney Foundation  ( E033 );  Estonian Research Council  ( IUT20-60 ,  IUT24-6 ,  PUT1660 ); ERC: (284167,  771057 ,  230374 ,  GENETARGET-T2D ,  GA 269045 );  European Regional Development Fund  [ 2014-2020. 4. 01. 15-0012GENTRANSMED ];  EU grants  ( HEALTH-F2-2009-223004 ,  HEALTH-F4-2007-201413 ,  HEALTH-F2-2013-601456 ,  HEALTH-F2 -.
Data availability GWAS summary statistics for FG/FI analyses presented in this manuscript are deposited on https://www. magicinvestigators. org/downloads/ and will be also be available through the NHGRI-EBI GWAS Catalog https://www. ebi. ac. uk/gwas/downloads/summarystatistics. Raw files for RNA-seq mRNA expression in islet donors have been deposited in NCBI GEO database with the accession code GSE50398. Summary-level GWAS results for genetic correlation analysis with glycemic traits were downloaded from the LDHub database (http://ldsc. broadinstitute. org/ldhub/). Islets from 89 cadaver donors were provided by the Nordic Islet Transplantation Programme (http://www. medscinet. com/ nordicislets/). The dexseq_count python script for RNA sequencing analysis in human pancreatic islets was downloaded from http://www-huber.
These situations can result in some underestimation of the relative contribution of BrC to the overall BB aerosol absorption in our analysis but are unlikely to lead to significant (i. e. , exceeding corresponding confidence intervals) biases in our retrievals of k OA and MAE OA. Applying these selection criteria to the available AERONET retrievals yielded a dataset that includes 115 data records, the majority of which (65) come from the Yakutsk site. Different data records normally correspond to different days, but some observations are taken on the same day. The temporal coverage of the selected data is not uniform: the most abundant data (40 data records) correspond to the year 2012, when a major mega-fire event occurred in Siberia  (Konovalov et al. , 2014 (Konovalov et al. , , 2017a (Konovalov et al.
Second, we scaled these estimates of σ 1-3 with a constant factor (f s ), which was optimized by minimizing the mean square difference between the "true" values of k OA from the synthetic dataset and the corresponding retrievals of k OA. The optimal value of f s was found to be equal to 0. 20. Then the error inherent to our Bayesian algorithm (with a look-up table consisting of 10 6 simulations) can roughly be estimated as a fifth part of the observation uncertainty. Note that the dependence of the errors in our retrievals of k OA and other parameters on f s is found to be rather weak. Note also that, since we optimized only one parameter (f s ) and our test dataset is relatively large, the performance statistics for our retrieval algorithm could hardly change significantly if our test dataset were split into optimization and validation subsets (as would probably be necessary if a larger number of parameters of the retrieval algorithm were optimized using the same data).
This mutation is expected to alter the mRNA splicing by affecting the donor site signal, according to the prediction tools Human Splicing Finder (HSF) and MaxEntScan. In particular using HSF, the potential impact of the variation was assessed through attribution of consensus value (CV) according to the matrices from Shapiro and Senepathy  (15). The difference between the wild type and the mutant had a CV of (-32. 71%), and was predicted by the program to abolish the donor site, thereby affecting the splicing process. -cDNA analysis of the splice site mutation To con rm and functionally validate the mutation at the splice site, mRNA extracted from primary broblasts derived from the CS1EA1 patient and a healthy control were compared. PCR ampli cation of cDNA from exon 8 to exon 11, using appropriate primers, resulted in a shorter fragment in CS1EA1 compared to the control.
For data analysis, home-made "STARK" and Polyweb (Université Paris Descartes) pipelines were used to detect both single nucleotide and copy number variations. Pathogenicity of the variants were tested using online prediction tools like MutationTaster (http://www. mutationtaster. org/), Sift (https://sift. bii. a-star. edu. sg/), Polyphen (http://genetics. bwh. harvard. edu/pph2/) MaxEnScan (http://hollywood. mit. edu/burgelab/maxent/Xmaxentscan_scoreseq_acc. html ) and previously publically available Human Splicing Finder version 3. 1_2017 (http://www. umd. be/HSF/). The presence of a variation in a proband was con rmed by Sanger sequencing as well as segregation analysis.
Dams and reservoirs are among the most widespread human-made infrastructure on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e. g. , GOODD) or detailed attributes for a limited dam quantity or region (e. g. , GRanD and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD) maintained by the International Commission on Large Dams (ICOLD), documents nearly 60,000 dams with an extensive suite of attributes. Unfortunately, WRD records are not georeferenced, limiting the benefits of their attributes for spatially explicit applications.