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Classifying pathogenic variants in amyloid beta using intramolecular genetic interaction profiling

ABSTRACT: Classifying pathogenic variants in amyloid beta using intramolecular genetic interaction profiling

PROVIDER: PRJNA1039772 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR26805707_1.fastq.gz	Fastqsanger.gz
	SRR26805707_2.fastq.gz	Fastqsanger.gz
	SRR26805708_1.fastq.gz	Fastqsanger.gz
	SRR26805708_2.fastq.gz	Fastqsanger.gz
	SRR26805709_1.fastq.gz	Fastqsanger.gz

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Classifying pathogenic variants in amyloid beta using intramolecular genetic interaction profiling

Project description:Changes in the amino acid sequences of proteins cause thousands of human genetic diseases. However, only a subset of variants in any protein is typically pathogenic, with variants having a diversity of molecular consequences. Determining which of the thousands of possible variants in any protein have similar molecular effects is very challenging, but crucial for identifying pathogenic variants, determining disease mechanisms, understanding clinical phenotypic variation, and developing targeted therapeutics. Here we present a general method to classify variants by their molecular effects that we term intramolecular genetic interaction profiling. The approach relies on the principle that variants with similar molecular consequences have similar genetic interactions with other variants in the same protein. These intramolecular genetic interactions are straightforward to quantify for any protein with a selectable function. We apply intramolecular genetic interaction profiling to amyloid beta, the protein that aggregates in Alzheimer’s disease (AD) and is mutated in familial AD (fAD). Genetic interactions identify two classes of gain-of-function variants, with all known familial Alzheimer’s disease variants having very similar genetic interaction profiles, consistent with a common gain-of-function mechanism leading to pathology. We believe that intramolecular genetic interaction profiling is a powerful approach for classifying variants in disease genes that will empower rare variant association studies and the discovery of disease mechanisms.

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