UC Davis

Studying structural variants (SV) in populations is crucial since they cause more diversity and have more effects on gene function than small variants. However, population scale studies are challenging since finding SV from inexpensive short-read sequencing(SRS) methods have a high false positive rate. Conversely, long-read sequencing(LRS) are more accurate for SV discovery but are expensive at a population scale. Here, I develop new unbiased techniques to study SV in populations that are more scalable than the state-of-the-art. I show their utility in creating an SV catalog for the cattle breed augmented with their allele frequency.