Publications by Charleen Adams
Cis-MR of 183 UKB-PPP proteins and CHD
1 Methods 1.1 Study Overview We conducted a two-sample Mendelian Randomization (MR) analysis to evaluate the causal effect of 183 UKB-PPP Proteins circulating protein levels (1 Mb region surrounding their respective transcription start sites [TSSs]), on coronary heart disease (CHD) using CARDIoGRAMplusC4D and FinnGen summary statistics. 1.2 Data ...
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MR of NAT and BMI
1 MR of NAT (PTER region) on Jurgens BMI 1.1 Data Data # NAT: # on GRCh38 wget https://pheweb.org/metsim-metab/download/C100005466 # BMI: wget https://personal.broadinstitute.org/ryank/Jurgens_Pirruccello_2022_GWAS_Sumstats.zip # select: GWAS_sumstats_EUR__invnorm_bmi__TOTALsample.tsv # use LiftOver to obtain GRCh38 coordinates 1.2 Suite of M...
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GenomicSEM for 40 Chemokines
1 Summary The analysis uses Genomic Structural Equation Modeling (GenomicSEM) to run a multivariate GWAS of 40 chemokines. The value of GenomicSEM lies in its ability to enhance statistical power and uncover pleiotropic effects that may remain undetected in pairwise or univariate analyses. 2 Data 2.1 UK Biobank Pharma Proteomics Project (UKB-PPP)...
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Pipeline Development for Colocalization (HyPrColoc) Across Clinical and Molecular Phenotypes
0.1 Data 0.1.1 UK Biobank Pharma Proteomics Project (UKB-PPP) Proteins 2940 summary statistics; Europeans; both GRCh19/38 Inflammation: 736 Cardiometabolic: 736 Oncology: 735 Neurology: 733 Tally UKB-PPP Categories #!/bin/sh # Directory containing the files dir_path="/Users/charleenadams/ukbppp" # Temporary file for storing category counts...
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Simulated Marginal Structural Model (MSM) with Inverse Probability Weighting and Lagged Proteins for Longitudinal Protein Analysis of Incident T2D
1 Marginal Structural Models (MSMs) MSMs estimate the causal effect of time-varying exposures (e.g., protein levels) on outcomes (e.g., T2D). Standard regression models can introduce bias due to: Time-varying confounding – Past protein levels influence future levels and T2D risk. MSMs can use Inverse Probability Weighting (IPW) to adjust for the...
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Simulated Marginal Structural Model (MSM) with Inverse Probability Weighting and Lagged Proteins for Longitudinal Protein Analysis of Incident T2D
1 Marginal Structural Models (MSMs) MSMs estimate the causal effect of time-varying exposures (e.g., protein levels) on outcomes (e.g., T2D). Standard regression models can introduce bias due to: Time-varying confounding – Past protein levels influence future levels and T2D risk. MSMs can use Inverse Probability Weighting (IPW) to adjust for the...
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xQTLbiolinks hack for PCSK9 and METSIM metabolites
I partially hacked xQTLbiolinks—Jeremy’s suggestion for retrieving GTEx eQTLs and running eQTL-pQTL coloc—to run HyPrColoc on our cis-regions, linking PCSK9 pQTL and METSIM metQTL data. It’s a hack because xQTLbiolinks is designed for one GWAS trait (e.g., one pQTL) with one or more eQTLs, not multiple GWAS traits. While it correctly identi...
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HyPrColoc vignette (by Foley and Staley)
1 Introduction HyPrColoc is Bayesian divisive clustering algorithm for identifying clusters of traits which colocalize at distinct causal variants in a genomic region. The default algorithm can identify clusters of putatively colocalized traits within a vast collection of traits, e.g. 1000s, quickly. For each set of putatively colocalized traits, ...
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Colocalization Across Clinical and Molecular Phenotypes
1 Data 1.1 UKB-PPP Proteins (2940 Summary Statistics) Inflammation: 736 Cardiometabolic: 736 Oncology: 735 Neurology: 733 Tally UKB-PPP Categories #!/bin/sh # Directory containing the files dir_path="/Users/charleenadams/ukbppp" # Temporary file for storing category counts temp_counts=$(mktemp) # Loop through all .tar files in the directo...
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LDSC
1 Syllables Syllables An🔸ti🔸dis🔸es🔸tab🔸lish🔸men🔸tar🔸i🔸an🔸is🔸m 2 Post-GWAS Method Summary statistics from GWAS are the data source. Mini-Me Post-GWAS Ingests GWAS summary statistics as its data Like two-sample Mendelian randomization (MR),but uses the pattern of association between SNPs Implemented originally i...
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