A noncoding RNA gene will solve genetic diagnoses for thousands

Until I was in academia, I worked within a small group of researchers who all pretty much had the same expertise as I had and so, I was never used to explaining certain genetic concepts during the scientific discussions. This worsened my—what advocates of good writing like Steven Pinker describe as—"curse of knowledge". I only realized this after I started working in industry. In my day job, I communicate genetic results to scientists whose expertise differ drastically from mine. One of the important concepts that I often find myself explaining again and again to my neuroscientist colleagues is “mutational constraint”, that is, why certain genes are depleted of deleterious mutations. We often get requests to check if mutations in a gene is associated with any disease. Being in a neuroscience area, often such genes will be related to brain development and so, will be constrained. As a result, we won't find that many carriers of deleterious mutations, despite having access to a genotype-phenotype database of more than 2 million individuals. Every time, when we deliver the gene look up results, we go through certain basic statistics such as expected vs observed loss of function variants and conclude that the gene is mutationally constrained and so, we couldn't find an enough number of carriers to confidently interpret the phenotypic associations.

Genetics of human brain development has always fascinated me. It is one of those areas of human genetics where you get a first row seat to watch natural selection in action. When I write about papers on Twitter, I never miss an opportunity to share this famous world warplane illustration, while explaining results related to mutational constraint.

One of the uses of sequencing the general population in large numbers is to predict which parts of the genome are critical for human survival. The logic is that if you see fewer variations in a part of the genome than what you'd expect to see in a sample of, say, 500,000 humans, you infer