Somatic Mosaicism Across Human Tissues (SMaHT)

Our DNA is not always identical in the cells throughout our body. As a one-celled embryo, we inherit unique DNA from our parents. But as our cells multiply and become the various tissues of our bodies, the DNA in these “somatic cells” can acquire new changes. Though somatic changes (called variants) in our DNA can occur in all tissues and may lead to disease, genetics research mainly studies variants in blood and saliva, resulting in an incomplete picture of the impact somatic variation has on health.

The NIH Common Fund’s Somatic Mosaicism across Human Tissues (SMaHT) Network will create knowledge to accelerate research on the impact of somatic variation on human development, aging, and a variety of diseases. 

A genome characterization center will be established at Baylor’s Human Genome Sequencing Center. Dr. Richard Gibbs, founding director of the Human Genome Sequencing Center and Wofford Cain Chair and Professor in Molecular and Human Genetics at Baylor, will serve as co-principal investigator of the project, along with Dr. Harsha Doddapaneni, associate professor at the Human Genome Sequencing Center, and Dr. Rui Chen, professor of molecular and human genetics at Baylor. The center will characterize somatic variation in 550 of the SMaHT program’s 2,250 tissue samples. Sample tissues will come from approximately 150 human donors from diverse ancestry backgrounds and stages of life and will represent different tissue types, including brain, blood, skin, muscle, colon, spleen, uterus, vas deferens, ovaries and testis.

A project led by principal investigator Dr. Fritz Sedlazeck, associate professor at the Human Genome Sequencing Center, will focus on developing novel computational methods for studying somatic structural variation based on long-read sequencing that use new algorithmic and machine learning approaches. The team will focus on the identification of transposon movement and their epigenetic consequences across the genome. To discover this, the team will innovate novel algorithms using long-read data. All methods also will be made available across the SMaHT Network.

This research is supported by the NIH Common Fund grants (1 UM1 DA058229-01, 1 UG3 NS132132-01 and 1 UG3 NS132105-01).