Publications by Charleen Adams
MR Pipeline of 5 Acetyl-amino Acids (1MB around ACY1 TSS) on 7 Metabolic Outcomes
1 Methods 1.1 Study Overview We conducted a two-sample Mendelian Randomization (MR) analysis to evaluate the causal effect of acetyl-amino acid levels within a 1 Mb region surrounding the transcription start site (TSS) of the ACY1 gene on 7 metabolic ouctomes. Genetic instruments were derived from the METSIM cohort, while outcome summary statistic...
11380 sym R (52966 sym/16 pcs)
FAS
import fitz # PyMuPDF import re import pandas as pd import os # === Woke keywords === WOKE_TERMS = [ "social justice", "colonialism", "land acknowledgement", "oppression", "marginalized", "intersectional", "equity", "inclusivity", "diversity", "privilege", "systemic", "anti-racism", "decolonization", "reparations", "intersectional...
14 sym R (11913 sym/9 pcs)
Mendelian randomization of N-Acetylglutamine (1MB around ACY1 TSS) and body mass index
1 Methods 1.1 Study Overview We conducted a two-sample Mendelian Randomization (MR) analysis to evaluate the causal effect of N-acetylguanine (NAG) levels, within a 1 Mb region surrounding the transcription start site (TSS) of the ACY1 gene, on body mass index (BMI). Genetic instruments for NAG were derived from the METSIM cohort, while BMI summar...
10969 sym R (44183 sym/8 pcs)
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 ...
3460 sym R (80624 sym/23 pcs)
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...
745 sym R (35018 sym/3 pcs)
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)...
1339 sym R (35603 sym/22 pcs) 1 tbl
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...
9028 sym R (89702 sym/26 pcs) 1 tbl
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...
4763 sym R (5249 sym/2 pcs) 1 tbl
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...
4581 sym R (7861 sym/11 pcs) 1 tbl
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...
687 sym R (19756 sym/71 pcs)