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This post is a review on a paper in American Association of Cancer Research, which describes association of PRS and lifestyle with developement of 20 types of cancer.
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This post is a review on a paper published in Lancet Oncology that discusses PRS applied to gastric cancer.
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Today, I’m reviewing a paper that examines the use of PRS across ancestries
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Today I reviewed 10 of papers on PRS literature. Part 1.
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This post is a very short learning point on how to identify pathogenic variants in a given gene. I have not yet verified whether this resource is reputable, however I hope that it provides to be a great resource. It was previously used by Joe Park in another area of the PMBB lab, an utilized in his paper in Nature here.
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Below is a post to identify the first few steps in a project in which I aim to identify the proportion of pathogenic variants present in our population.
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``` #!/usr/bin/env Rscript
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In this posting, I created a script in R that goes through the 3 most important phenotype files in PMBB.
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This post shows the R script I’ve written to calculate AUC and OR for the PRS generated in plink in previous blog posts. It has undergone several iterations as I adjust our desired outcomes.
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This post shows the script I wrote to convert LD pruned lists of SNPs, apply them to the Penn Medicine Biobank on a specific population, and use plink -score to create Polygenic Risk scores. Below I have pasted the .sh script I wrote today to create PRS.
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In this blog post, I’ll outline the script I made to conduct Linkage Disequilibrium Pruning with a given list of SNPs on the 1000 genomes dataset.
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This blog post describes how to create a list of SNPs associated with Lung Cancer. This will be used in future steps to build PRSs.
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This blog post is Part 2 of outlining the materials and methods used in our lab’s most recent work on generating polygenic risk scores (PRSs). This article is entitled “Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank”. It was published in October 2021 and led by Dr. Louise Wang and Heena Desai.
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In creating polygenic risk scores in Wang et al., 2021, patient population was subdivided into those with European and African descent.
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I have never understood genotype imputation. This blog post serves to describe the concept of genetic imputation and how it’s carried out using TOPMed.
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This blog post will be Part 1 of outlining the materials and methods used in our lab’s most recent work on generating polygenic risk scores (PRSs). This article is entitled “Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank”. It was published in October 2021 and led by Dr. Louise Wang and Heena Desai.
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This blog post exists because I tried to create a blog post on study methods and failed.
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Today I’m exploring the data available within PMBB. This blog is a data dump of the directories and README files, which might not be pleasant to read. However, I’m interested in copying it here as a resource that I can check back on when navigating this complicated database.
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In this blog post, I’ll cover a paper published by Montgomery Slatkin in Nature Reviews genetics in 2008, entitled “Linkage disequilibrium - understanding the evolutionary past and mapping the medical future”. It can be found on Nature’s website here and free on Pubmed here.
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In this posting, I’ll review an analytical paper in Nature genetics entitled “An atlas of genetic correlations across human disease and traits”, by Bulik-Sullivan et al. published 2015. It can be found here on Nature’s website and here on PubMed.
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In the upcoming days, I’m going to review background literature on the topic of my future research.
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Welcome to the blog. In this first posting, I aim to describe the Penn Medicine Biobank. Although it remains an unfamiliar resource within Penn Medicine, it contains a fantastic array of electronic health record and genetic information with tremendous research potential.