An illustration of two scientists carrying a larger-than-life DNA strand on their shoulders.
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Community Newsletter: Autism gene lists, genetic diversity in mouse models, an autism biomarker

This week, we’re bringing you some labors of love: a thread lamenting the autism field’s focus on gene lists, a study introducing genetic diversity in mouse models, and long-awaited results from a biomarker study.

Starting things off this week is a thread by Jonathan Sebat, professor of psychiatry and cellular and molecular medicine at the University of California, San Diego, calling out autism research’s focus on gene lists.

He tweeted that much needs to be understood “about genes -> mechanisms -> phenotype IN HUMANS that requires well powered human studies. But somehow the human research has been reduced to ‘studies that produce gene lists.’” Sebat explained further, “Once a gene is found, the human genetics becomes an afterthought. I got news for you. One gene doesn’t cause autism. Genetics is just as important in translation!”

“With all these gene lists we have hundreds and hundreds of genes to functionally characterize,” replied Maddie Gillentine, a geneticist at Seattle Children’s Hospital in Washington. She also pointed out the high cost and difficulty of designing studies that will impact a large number of genetic conditions.

Sebat responded, “Clinical studies should not focus on 1 or 2 disorders. It’s a big science problem that requires large scale collaboration.”

Michael Gandal, assistant professor of psychiatry and biobehavioral sciences at the University of California, Los Angeles, tweeted in agreement that “the major lesson human genetics has taught us is sample size absolutely critical to robustly assess allelic effects in population.”

In a separate thread, Joseph Gleeson, professor of neurosciences at the University of California, San Diego, shared an unpublished study that used 33 genetically diverse strains of mice to model mutations in CHD8, a top autism candidate gene.

Gleeson tweeted that the paper “uncovers both susceptibility and resiliency of individual strains towards #autism-related traits.”

“As this study and others nicely show, important aspects of biology and disease are missed with a unitary focus on a single strain,” replied Mark Zylka, professor of cell biology and physiology at the University of North Carolina at Chapel Hill.

“Grateful to work with Pat and Allison and for their groundbreaking work that made this study possible,” tweeted study author Manal Tabbaa, behavioral researcher at the University of Southern California in Los Angeles.

In another thread, James McPartland, professor of child psychiatry and psychology at the Yale Child Study Center, shared his study which used electroencephalography (EEG) to track children’s brain responses at rest and during several tasks. A delay in the brain response to viewing faces — called the N170 — distinguished autistic children from their non-autistic peers.

The results “provide information about EEG biomarker performance that is relevant for the next stage of biomarker development efforts focused on context of use,” according to the study.

“Much-awaited EEG results from #ABCCT finally published, demonstrate pretty robust psychometrics,” wrote Zack Williams, a medical and doctoral student at Vanderbilt University in Nashville, Tennessee, in a quote tweet.

Continuing in the same thread, Williams tweeted, “The N170 ERP in particular has great potential as a “stratification biomarker” that could be used to subgroup autistic people into potentially more biologically homogeneous groups, making it easier to detect treatment effects in clinical trials.”

Twitter user @asdhominem brought up a concern that “instead of stratification this will become a means of excluding people based on ‘bio markers’ discovered from biased samples.”

That’s it for this week’s Community Newsletter! If you have any suggestions for interesting social posts you saw in the autism research sphere, feel free to send an email to [email protected].

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