Evan Eichler is a Professor of Genome Sciences and Howard Hughes Medical Institute Investigator. He received his Ph.D. from Baylor College of Medicine. After his postdoctoral fellowship at Lawrence Livermore National Laboratory, he joined Case Western Reserve University in 1997 and the University of Washington in 2004. He is a co-chair and PI of the Human Genome Structural Variation, Human Pangenome Reference, and Telomere-to-Telomere (T2T) Primate Sequencing Consortia. His research group provided the first genome-wide view of segmental duplications within human and other primate genomes, and he is a leader in an effort to identify and sequence normal and disease-causing structural variation in the human genome. His lab has contributed to the discovery of large-effect copy number variants and genes associated with autism, and he is leading efforts to completely sequence and assemble human genomes. The long-term goal of his research is to understand the evolution and mechanisms of recent gene duplication and its relationship to copy number variation and human disease with a specific emphasis on understanding the genetic architecture of autism and neurodevelopmental delay.

Complex structural variation and disease

Advances in long-read sequencing have enabled telomere-to-telomere (T2T) sequencing of genomes, essentially providing, for the first time, sequence-resolved chromosomes. This advance has enabled the discovery of all forms of genetic variation, including underappreciated complex patterns of structural variation (SV) in regions previously regarded as inaccessible to sequence and assembly. I will present our most recent work with respect to long-read sequencing of ~300 diverse human genomes as part of the Human Pangenome Reference Consortium (HPRC) and show how these data are being used to fully characterize the structure and population distribution of more complex patterns of human genetic variation. I will present data on long-read sequencing of >1000 individuals recruited through the All of Us program with electronic health record data. I will show how this data has been used to discover new candidate SVs that can be imputed or directly genotyped in larger short-read datasets to discover novel disease association candidates and to identify expressed quantitative loci that affect gene expression. This approach provides a strategy to discover causal variants more likely associated with human phenotypic traits.