Jonathan Sebat

We are proud to be one of the 13 projects funded by the NIH Autism Data Science Initiative. Our project: "Elucidating the Interplay of Genes and Environment in Autism Using Genomic and Exposure Data from Large Populations" 🧵https://www.nytimes.com/2025/09/26/health/autism-research-trump-kennedy.html

We will also investigate how environmental factors influence clinical outcome of genes in ASD. Samples of many thousands are needed to identify G. Capturing the effects of GxE requires sample sizes of millions. Juicy preliminary data will be posted soon to medrxiv. It takes a village, come join us!

We are proud to be one of the 13 projects funded by the NIH Autism Data Science Initiative. Our project: "Elucidating the Interplay of Genes and Environment in Autism Using Genomic and Exposure Data from Large Populations" 🧵https://www.nytimes.com/2025/09/26/health/autism-research-trump-kennedy.html

Our 2022 paper in NG illustrates one example: the observed association of parental age with ausism consists of a mixture of causal pathway E → de novo mutation → ASD; and noncausal pathway Parental age ← polygenic score → ASD

We are proud to be one of the 13 projects funded by the NIH Autism Data Science Initiative. Our project: "Elucidating the Interplay of Genes and Environment in Autism Using Genomic and Exposure Data from Large Populations" 🧵https://www.nytimes.com/2025/09/26/health/autism-research-trump-kennedy.html

THE major challenge in epidemiological studies of the environment in autism is the pervasive passive correlation of genes and environment (rGE) that happens in the population, also known horizontal pleiotropy. There are causal effects to be found, but we need to separate correlation from causation

We are proud to be one of the 13 projects funded by the NIH Autism Data Science Initiative. Our project: "Elucidating the Interplay of Genes and Environment in Autism Using Genomic and Exposure Data from Large Populations" 🧵https://www.nytimes.com/2025/09/26/health/autism-research-trump-kennedy.html

Leaf blowers on lizards is pretty innovative if you ask me

Structural variants are significant contributor to autism. But many SVs & TRs are hard to detect with short reads. Long read sequencing with @pacbio.bsky.social and @nanoporetech.com captures and maps out alot of what short reads miss. So what can LR-WGS tell us about autism? 🧵

Power is limited in N=243. But the signal is quantifiable. Rare SVs, TRs and rare SNVs combined explained 7.6% of the heritability. We will need larger samples to get precise estimates, but the combined common and rare is starting to reach a substantial fraction.

Structural variants are significant contributor to autism. But many SVs & TRs are hard to detect with short reads. Long read sequencing with @pacbio.bsky.social and @nanoporetech.com captures and maps out alot of what short reads miss. So what can LR-WGS tell us about autism? 🧵

Skewed methylation was a characteristic of all "gray zone" alleles of FMR1 (>35 repeats). But in all but one sample, the skewing was specific to FMR1. X chromosome inactivation was NOT skewed. 10/N

Structural variants are significant contributor to autism. But many SVs & TRs are hard to detect with short reads. Long read sequencing with @pacbio.bsky.social and @nanoporetech.com captures and maps out alot of what short reads miss. So what can LR-WGS tell us about autism? 🧵

We and @abesterman.bsky.social detected a TAN-DUP-DEL in a clinical case. Assembly of the complex SV was essential for determining the genetic diagnosis of RFX3 haploinsufficiency. It showed that the DEL occured within the FUNCTIONAL copy of the gene pubmed.ncbi.nlm.nih.gov/40200712/

Structural variants are significant contributor to autism. But many SVs & TRs are hard to detect with short reads. Long read sequencing with @pacbio.bsky.social and @nanoporetech.com captures and maps out alot of what short reads miss. So what can LR-WGS tell us about autism? 🧵

Phase information of individual reads ends up being useful for a variety of purposes including assembling SVs, genotyping and detection of de novo and mosaic SVs and TRs (more to come on this) 6/N

Structural variants are significant contributor to autism. But many SVs & TRs are hard to detect with short reads. Long read sequencing with @pacbio.bsky.social and @nanoporetech.com captures and maps out alot of what short reads miss. So what can LR-WGS tell us about autism? 🧵

Panel C (coding SVs in constrained genes) are a class variants with well-established associations with ASD. To maximize detection of these, one needs a combination of short reads and long reads. For large (>50 bp) tandem repeat variants, long reads alone do a good job 4/N.

Structural variants are significant contributor to autism. But many SVs & TRs are hard to detect with short reads. Long read sequencing with @pacbio.bsky.social and @nanoporetech.com captures and maps out alot of what short reads miss. So what can LR-WGS tell us about autism? 🧵

We WGS'd 267 subjects from 63 ASD families, 109 with ONT (GridION) and 158 with PacBio HiFi platform (Sequel IIe) and 109 with ONT(GridION). Standard structural variant (SV) and tandem repeat (TR) calling pipelines were applied to both platforms. 2/N