Massively Parallel Gene Function Assays Reduce Genetic Diagnoses Ambiguity

Genetic tests increasingly use more comprehensive multi-gene and whole-genome sequencing methods and it is becoming more common for patients to learn they carry a variant of unknown significance.

People who carry a known pathogenic or disease-linked breast cancer 1, early onset (BRCA1) variant have a higher risk of breast and ovarian cancers because the encoded protein is faulty as it fails to properly regulate DNA repair and allows cancer-causing mutations to accumulate.


Scientists at the University of Washington (Seattle, WA, USA) characterized nearly 2,000 variants of the breast cancer-associated gene BRCA1, demonstrating the potential of a new approach for sorting out which variants are harmful and which are harmless. They measured the function of the gene's protein product. The encoded protein is tested with relatively simple laboratory assays that gauge whether the protein retains its normal biochemical functions. By performing many thousands of these tests at once, biologists can assess all possible variants of a gene quickly and efficiently. This method of performing protein function assays in a massively parallel format is called deep mutational scanning.

The team combined data from two different tests of a key part of the BRCA1 protein called the Really Interesting New Gene (RING) domain. Around 58% of known pathogenic BRCA1 variants affect this part of the protein. One of the tests measured the ability of the RING domain to attach small proteins called ubiquitin tags to other proteins. The second test measured whether the RING domain could bind to part of another protein called BRCA1 Associated RING Domain 1 (BARD1) when both proteins were produced in a yeast cell. If BRCA1 cannot bind to BARD1, it no longer prevents tumor formation. The data from both massively parallel assays were largely consistent with previous studies. The amplicons were sequenced on a HiSeq2000 (Illumina; San Diego, CA, USA).

Combined scores from the massively parallel tests were also used to predict results from the so-called “gold standard” of BRCA1 functional assays. This more comprehensive test assesses the full-length BRCA1 protein's ability to regulate DNA repair in cells, and is the measure that best correlates with disease risk in patients. The investigators found they could use data from the deep mutational scan to predict how a variant would perform in the gold standard test. The predictions made in this way were substantially more reliable than those made by widely-used computational methods that are currently used in genomic studies to predict the severity of mutations.

Lea Starita, PhD, the lead author of the study said, “As genetic testing becomes both cheaper and more comprehensive, we will need a variety of approaches to translate the deluge of genetic data into practical information on individual health risks. Deep mutational scans are one tool to help meet this urgent need.” The study was published on June 1, 2015, in the journal Genetics.

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