bioRxivGenomicsPreprint
Pangenome discovery and characterization of human protein-coding duplicated genes
Luyao ReniD, DongAhn YooiD, Katarina VlajiciD, Philip C. DishuckiD, Xavi GuitartiD, Youngjun KwoniD, Jiadong LiniD, Katherine M. MunsoniD, Kendra HoekzemaiD, Andrew B. StergachisiD, Mitchell Robert VollgeriD, Devin K. SchweppeiD, Evan E. EichleriD
Abstract
Protein-coding genes mapping to high-identity segmental duplications (SDs) have been difficult to annotate and characterize and are the source of most previously unknown protein-coding genes being discovered as part of the human pangenome. Here, we combine long-read assembled human genomes (298) and long-read transcriptome data (5.6 billion full-length cDNA from 83 tissues) to phylogenetically interrogate 493 gene families discovering 2713 potentially copy number polymorphic genes not present in the human reference genome. For reference SD gene families where paralog specificity can be assigned, we find that 60.0% are expressed and maintain open reading frames, with 45.7% showing high expression in brain, embryo, or testis. We revise 386 gene models, including 150 that absent or different from current T2T-CHM13 gene annotation and 236 (35.1%) pseudogenes as protein-coding where we find evidence of transcription, an open reading frame, and chromatin-accessible promoters. We find that 24.2% of SD genes show evidence of constraint for both copy number and amino acid mutation. The majority of these constraint genes are ancestral, whereas only 16.2% of derived duplicated genes that emerged recently in the human lineage show evidence of constraint. The pangenome provides unparalleled specificity to understand genetic variation in SD genes allowing us to distinguish functional genes from pseudogenes and highlighting potential gene innovations that arose most recently in human evolution.
- organism: human
- assay: whole-genome sequencing
- assay: long-read sequencing
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant calling
- approach: statistical model
- Citations
- 0
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