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This week, scientists from the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog published a paper in Nature introducing the Polygenic Risk Score Reporting Standards (PRS-RS). The work outlines the minimum set of data to allow scores to be faithfully interpreted and transparently evaluated.

An update of the now 10 year-old (and now rarely used for PRS) Genetic Risk Prediction Studies (GRIPS) statement, the PRS-RS lay out many of the essential pieces of information that are required to understand how and why a PRS was developed. These include information such as the background and…


Cardiovascular disease is the leading cause of death worldwide, with recent estimates putting the global death toll at around 18 million each year. It is also a major burden on populations and health systems, contributing to a reduction in the quality of life for millions of people and treatment costs of billions of dollars. We know a lot about the drivers of cardiovascular disease (CVD). Years of research have implicated the key roles that physical activity, cholesterol control, BMI, smoking and associated diseases like diabetes play in an individual’s risk of suffering the adverse effects of CVD.

There is also…


In our previous articles on Allelica’s PRS pipeline, we ran through how you can use our DISCOVER module to build your own PRS for a disease or trait of interest, as well as how our VALIDATE module can be used to test the predictive power of a new or existing PRS on an independent or new population.

In this final article in the series we’ll cover our PREDICT module. In many ways, the overarching aim of the entire pipeline is to use genetic information from a new individual or set of individuals to predict their genetic liability for a disease…


Essential testing of your PRS on an independent population

In our previous articles outlining Allelica’s PRS pipeline we described how users can impute genotype or low coverage sequencing data and build their own PRS using our cloud-based computing platform. A key aspect of the DISCOVER module is that it allows you to trial four different methodologies for computing a PRS so that the one that has the best predictive power can be chosen.

The next critical step in developing a PRS is validating it on an independent dataset. In this article, we’ll cover how Allelica’s VALIDATE module can be used to help users understand the applicability of their PRS…


A user friendly, cloud-based computing solution to build state-of-the-art, publication ready polygenic risk scores

The DISCOVER module allows users to build their own polygenic risk scores (PRS) from genetic data and summary statistics from a Genome Wide Association Study (GWAS).

In this piece, we’ll run through what the DISCOVER module does and the outputs that it can provide to help users understand the strengths and weaknesses of their PRS.

The main aim of the DISCOVER module is to run a suite of different PRS methodologies on a dataset to find the PRS with the best predictive performance. …


Fast and scalable genome imputation of Low-Coverage WGS and Microarray data in just minutes

Allelica’s PRS pipeline is built around a set of interconnected modules. Each module is designed to give the user enough control to run it as needed whilst leaving all the hard computational work behind the scenes. Which modules a user needs will depend on their specific needs, but the complete PRS pipeline has been developed to be able to run a full workflow on a starting dataset containing just two things: genomic data from your sample population and a set of summary statistics from a genome-wide association study (GWAS) for a disease or trait of interest. …


Within the last two decades, and upon completion of the world’s largest biological project in 2003, interest in the human genome and its effect on health and disease risk has soared. Mapping of the human DNA sequence has allowed for the identification of fundamental errors in the genetic code that facilitated the prediction and thus preventative treatment of disease.

Coronary artery disease (CAD) is the most prevalent form of heart disease across the globe with more than 18 million people over the age of 20 living with the condition. CAD presents with multiple complications and places a significant burden on…


A central goal of preventative medicine is to identify the people most at risk of disease early enough that something can be done. Spotting those at higher risk of getting cancer, for example, means that screening programs can be targeted at this group, allowing the disease to be caught early and dealt with whilst it’s relatively cheap and the cancer is small. …


We previously wrote about the enormous impact that the UK Biobank (UKBB) is having to translational research. Although the scientific outputs from this project are now beginning to stack up — as of February 2020, 1053 scientific papers had been published using the resource — the project was originally established almost 15 years ago, so these are the fruits of many years of labour.

The UKBB took several years of negotiation and planning to set up, followed by a further four years of participant engagement and recruitment, and then additional time for the subsequent data generation. From developing appropriate participant…


At Allelica we’re very excited about the incredible advances that analyses of large DNA datasets are bringing to medicine.

In addition to the increasingly nuanced view of the polygenic complexity of common disease, these studies confirm that your DNA doesn’t, on its own, determine disease.

It’s usually the case that your risk of disease is modulated by a combination of multiple factors including your lifestyle, diet, general health and age. And, of course, genetics also plays a role.

We also know that modulating these non-genetic factors can lead to improvements in lifetime risk of disease.

To use a well known…

Allelica

Allelica is a Software Genomics Company developing algorithms and digital tools to accelerate the integration of Polygenic Risk Score in the clinical practice

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