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Genetic Predisposition to Disease

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[From AI to polygenic risk scores: which innovations will shape the future of psychiatry?].

Tijdschrift voor psychiatrie
BACKGROUND: In recent years, developments have been made in various research domains, from treatments with (es)ketamine to large-scale genome-wide association studies (GWAS).

Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.

International journal of molecular sciences
Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze genomic data from the UK Biobank, aiming to predict the genomic predisposition to complex diseases l...

Genetic association studies using disease liabilities from deep neural networks.

American journal of human genetics
The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort studies often grapple with absent or evolving health-related outcomes. Here, we propose two methods, l...

PNL: a software to build polygenic risk scores using a super learner approach based on PairNet, a Convolutional Neural Network.

Bioinformatics (Oxford, England)
SUMMARY: Polygenic risk scores (PRSs) hold promise for early disease diagnosis and personalized treatment, but their overall discriminative power remains limited for many diseases in the general population. As a result, numerous novel PRS modeling te...

Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning.

Scientific reports
Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. Aiming to systematically characterize candidate genes for a role in accumulation of lipids in adipocy...

MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.

Briefings in bioinformatics
Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide intermediate phenotypes (i.e. endophenotypes) that bridge the genetics and clinical manifestations of human disease. However, the genetic architecture of t...

Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease.

Genome biology
BACKGROUND: Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been limited by phenotyping challenges and ...

[Prophylactic surgery and genetic counselling: What impact of the artificial intelligence?].

Bulletin du cancer
In the area of cancer predisposition, certain situations may lead to the discussion of prophylactic surgery. This is rarely strictly recommended and depends on the patient's choice. The advantages and disadvantages must be weighed up. The main advant...

Exploring the application of deep learning methods for polygenic risk score estimation.

Biomedical physics & engineering express
. Polygenic risk scores (PRS) summarise genetic information into a single number with clinical and research uses. Deep learning (DL) has revolutionised multiple fields, however, the impact of DL on PRSs has been less significant. We explore how DL ca...

ASiDentify (ASiD): a machine learning model to predict new autism spectrum disorder risk genes.

Genetics
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects nearly 3% of children and has a strong genetic component. While hundreds of ASD risk genes have been identified through sequencing studies, the genetic heterogeneity of ASD ...