Sunday, November 25, 2018
Using Genomes to Predict Height
Despite new genetic paradigms, predicting height is still regarded as difficult. The height trait is a complex trait controlled by not only genes but by the environment which is a completely different topic. There are in fact many genes responsible for height in the genome sequence. Identifying these specific genetic variations causing height is a challenge. But Stephen Hsu from Michigan State University has developed an algorithm using newly available genomic sequencing data to predict height. Instead of using a genome wide assessment as others have done, Hsu and his team use a genomic prediction approach which decodes all SNPs at once rather than individually. It ultimately optimizes SNPs that are prone to affect the height trait the most.
The team used a UK Biobank that contained 500,000 genotype and phenotypic data to identify the smallest combination of SNPs responsible for height. Once they constructed an algorithm all that was needed was to test their algorithm. A regression line identified that there was a .65 correlation with actual height and predicted heights. The deviation or the error between true and estimated heights was due to only a difference of a few centimeters. This is still only the heritable portion of the height trait and the environmental aspect is still unaccounted for.
This method is only the beginning to better understanding polygenic traits. It allows scientists to focus on the genes that are most affecting the phenotype rather than giving the false impression that all genes share equal responsibility. Hopefully this knowledge can be useful in gene mutations and in gene therapy. It would be interesting to add on to this knowledge by testing other polygenic traits.