Genes That Smell

The Lab for Neurogenetics and Behavior at Rockefeller University has an ongoing Smell Study that states:

Given almost any odor, some people will find it pleasant, others unpleasant. Ripe cheese or garlic may smell delicious to some, but repulsive to others. The scientific basis of this variation has not been well-studied. Despite the clear evidence for culture-based preferences for food and aromas, the nature-versus-nurture debate for smell remains unresolved. We believe there may be a genetic basis for our unique senses of smell.

Inspired by the above, we took several Exome Sequencing and Whole Genome Sequencing datasets obtained from Human subjects, imported the associated reads into Avadis NGS, filtered these reads to retain only reads with very good Mapping Quality, called SNPs, and ran SNP Effect Analysis on these SNPs to identify Non-Synonymous SNPs and their associated genes. Running GO Analysis on these genes identified either Olfactory Receptor Activity or Sensory Perception of Chemical Stimulus/Smell as the most significantly enriched category for every single subject without fail. So genes involved in the perception of smell and their associated proteins vary substantially from person to person, possibly laying the ground for the above variation.

Which genes show the maximum variation? The family of Olfactory Receptors is among the largest of the gene families comprising more than 700 receptors. About 300 of these have at least one Non-Synonymous variant locus in at least one of our samples. The following graph shows those receptors which have 6 or more Non-Synonymous variant loci in our samples; there are 29 such receptors.

Overall, the mutation rates in the Olfactory Receptor family are only slightly higher than the expected genome-wide rate. What is surprising though is the relatively high rate of Non-Synonymous SNPs here. Could it be the myriad combinatorial possibilities generated by these variant loci that yield many different phenotypes with different perception profiles?

Also, a quick preview to the new Heatmap and Clustering functions for SNPs in Avadis NGS 1.3; the above heatmap shows the percentage of Variant Reads at various loci in the OR4C3 receptor (columns) for 7 Human samples (rows).

About Ramesh Hariharan

Dr. Ramesh Hariharan is an academic entrepreneur responsible for the software-based technology development and implementation at Strand Life Sciences. He is also the chief architect for all of Strand's products, including the award winning Avadis® platform. He is a recipient of the TR100 Award (2002) of Young Innovators by MIT's Technology Review Magazine and in 2003 received the Global Indus Technovator Award from MIT, instituted to recognize the top 20 Indian technology innovators worldwide. Ramesh is an IIT - Delhi Computer Science alumnus, has a Ph.D. in Computer Science from the Courant Institute of Mathematical Science, New York University and a postdoctoral degree in Computer Science from the Max Planck Institute, Saarbrücken, Germany. His research interests are in sequence analysis, string algorithms, computational biology, computational geometry and foundations of computing.