When it comes to covering a conference as large as the annual meeting of the American Society of Human Genetics (ASHG), one of the biggest challenges is capturing the full breadth of content in a handful of stories. Last week, GEN’s editorial team published three special edition newsletters covering the biggest news from the conference, including breaking news just prior to the event—the FDA Advisory Committee’s appraisal of Vertex Pharmaceuticals’ CRISPR-based therapy for sickle cell disease. We also covered PacBio’s evolution as a company in an exclusive interview with CEO Christian Henry.

But it’s important to acknowledge all the transformative science taking place in academic and industry laboratories that were the subject of numerous talks and posters throughout the meeting in Washington, DC. Here are just a few highlights of some other noteworthy research presented this year.

Diversity matters for large datasets

Results from research done by scientists at AstraZeneca (AZ) underscored the importance of diversifying participants in large-scale initiatives like biobanks. Xiao Jiang, PhD, a statistical geneticist at AZ, presented the data at the conference. For their study, the team evaluated evidence for CYP2D6—an important pharmacogene—genotypes in over 490,000 genomes in the UK Biobank. They had access to prescription data from more than 222,000 biobank participants, categorized as either poor, normal, intermediate, or ultra-rapid metabolizers of codeine based on specific star allele patterns.

The team found limited genomic information from individuals of non-European ancestry in the biobank and limited data on how specific genes are metabolized in these populations. It meant that between 5–6% of African American, biracial, and South Asian individuals could not be assigned into any one of these categories, compared to about 2% of individuals from Europe and East Asia. This makes it difficult to provide evidence-based advice about drug doses and treatment options.

Findings like this feed into broader conversations about the importance of diversity in large scale studies, something also underscored by Brendan Lee, MD, PhD, ASHG’s president, in his opening address at the meeting.

A cell-free cancer detection strategy

Improved methods for detecting cancer that don’t require invasive surgeries are always a welcome development. One presentation focused on the possibility of using nullomers, which are short segments of DNA found in cell-free RNA, as a noninvasive biomarker for cancers like hepatocellular carcinomas (HCCs). The work was presented by Austin Montgomery, an MD/PhD student at Penn State College of Medicine and is being done in collaboration with scientists at the National Technical University of Athens and University of California, San Francisco. Their findings indicate that nullomers may be more sensitive biomarkers than cell-free DNA because they are only detected in the presence of genetic mutations. In fact, about 80% of mutations cause a nullomer to surface, according to the team. Also some cancer cell hotspots contain mutational signatures that seem more likely to cause a nullomer to appear.

In the context of HCC, the researchers linked over 90% of the nullomers identified to liver associated genes. They also connected nullomers to genes involved in stomach and lung cancers.

Editing Huntington’s disease

In addition to sickle cell disease, scientists are applying CRISPR-based editing to a range of conditions. This includes Huntington’s disease (HD), an inherited condition that affects nerve cells in the brain. At the University of California, Berkeley (UCB), scientists have used gene editing technology and other tools to edit the mutant HTT gene responsible for the development of the fatal neurodegenerative disease. Details from the study were presented by Camelle Catamura, a computational biology graduate student at UCB. The team’s approach involves deleting sections of HTT gene and replacing them with an engineered DNA sequence to stop the production of abnormal proteins. To be clear, there is still work to be done but strategically editing a target section of a gene to create a new exon is proving to be a way of feasibly limiting or reducing abnormal mRNA expression.

A viral link to Alzheimer’s disease

One of the studies presented at the conference linked several herpes viruses to Alzheimer’s disease (AD) risk. The findings, presented by Marlene Tejeda, a doctoral student in bioinformatics at Boston University School of Medicine, also includes several genes linked to AD. Scientists from Case Western Reserve University, University of Miami, and University of Pennsylvania are also working on the project. For the study, the team used data generated by the Alzheimer Disease Sequencing Projects which includes data from people of European, Caribbean Hispanic, and African American ancestry. According to the presentation, they observed significant associations between herpes viruses and novel genes like HLA-DQA1 and POTEE. They also identified links between AD risk-associated genes like CASS4 and FOXF1 and herpes viruses.

GnomAD 4.0 is out

Lastly, version 4.0 of the Genome Aggregation database (gnomAD)—a large-scale aggregate dataset of human genetic variation—is now available. Efforts to build and maintain the resource is spearheaded by researchers at the Broad Institute. According to its developers, this version of the resource is nearly five times larger than some previous releases combined. It has sequence data from over 730,000 exomes and data from over 76,000 individual genomes that were in the previous version of the dataset. In total, it includes sequence data from over 807,000 humans including about 138,000 individuals of non-European ancestry. Full details of the release can be found on the project website.

At ASHG, at least two presentations focused on gnomAD. One discussed the bioinformatics frameworks used to process and quality control the large quantities of data generated by the initiative. The second presentation explored some of the insights into gene function that can be gleaned from a large, more diverse dataset.

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