With high throughput sequencing technologies available, we can sequence a large number of genomes now and look at the variations in DNA sequences between various organisms and in a population. But how do we identify whether the changes in DNA sequence between two organisms do translate to functional changes or have no effect? This question was taken up for the Eighth IBSE Colloquium talk which was co-organized by RBCDSAI.

The talk entitled “Are DNA variants bugs or features” was delivered by Dr. Yana Bromberg, Professor at the Department of Biochemistry and Microbiology and Department of Genetics at Rutgers University, was organized on 15th September 2021. Prof. Yana started the talk with a quote from Professor J. Von Neuman’s lecture that error is viewed not as an extraneous and misdirected or misdirecting accident but is an essential part of the process under consideration which means that we need to consider genetic error as not a problem but a part of the process. The speaker then listed various types of genetic variations such as Single Nucleotide Polymorphisms (SNP), deletion, insertion, tandem duplication, Interspersed duplication, inversion, translocation and copy number variations and told that SNPs are quite common among these variants. She mentioned that two individuals are 99.9% identical which implies that small differences lead to the wide human variety and her lab was interested in identifying such variants that are responsible for human differences. Further, she told that human genomes have shown approximately 20,000 Single nucleotide variants in the coding region and among these around 10,000 are non-synonymous single nucleotide variants, another 10,000 are synonymous single nucleotide variants and around 100-200 are nonsense single-nucleotide variants in the coding region. She further told that around 200 computational predictor tools are available to predict the effect of variations but none of these is better than all of them. She next explained that experimentally we get a range of effects when we test the effect of variant using parameters such as stability of protein, the catalytic activity of the protein, cell growth etc. and we see effects like loss of function, no effect and gain of function more as distribution. She told that her research group found that common variants affect molecular function more than rare ones and common variants have more effect than rare ones. She also pointed out that experiments are the gold standard but they are not always better than computed results and predictions that reflect the size of the effect (not only reliability) may be more useful for biologists studying the effect of non-synonymous single-nucleotide Polymorphisms.

While talking about her research on synonymous single nucleotide variants, she said that it is generally considered that they have no effect and are not important but they can affect transcription, splicing and translation events. Prof. Yana then discussed the SynVep (synonymous Variant effect predictor) tool developed by their laboratory which utilizes positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. SynVep was able to identify sSNV’s that can have an effect. Next, she discussed another tool named Ava,Dx ( Analysis of Variation for association with Disease X) which takes genome data from people who have the disease and don’t have a disease and tries to identify genes and pathways involved in such a disease. The tool helps in making predictions if a person is predisposed to disease and has been tested for Crohn’s disease. She concluded by mentioning that rare and common variants contribute to disease. The talk was well-received by the audience and was followed by an interesting question session.

The video is available on our YouTube channel: Link.