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Skiba SA, Hansen A, McCall R, Byers A, Waldron S, Epping AJ, Taglialatela JP, Hudson ML. Linked OXTR Variants Are Associated with Social Behavior Differences in Bonobos ( Pan paniscus). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.22.573122. [PMID: 38187727 PMCID: PMC10769379 DOI: 10.1101/2023.12.22.573122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Single-nucleotide polymorphisms (SNPs) in forkhead box protein P2 (FOXP2) and oxytocin receptor (OXTR) genes have been associated with linguistic and social development in humans, as well as to symptom severity in autism spectrum disorder (ASD). Studying biobehavioral mechanisms in the species most closely related to humans can provide insights into the origins of human communication, and the impact of genetic variation on complex behavioral phenotypes. Here, we aimed to determine if bonobos (Pan paniscus) exhibit individual variation in FOXP2 and OXTR loci that have been associated with human social development and behavior. Although the ASD-related variants were reported in 13-41% of the human population, we did not find variation at these loci in our sample of 13 bonobos. However, we did identify a novel variant in bonobo FOXP2, as well as four novel variants in bonobo OXTR that were 17-184 base pairs from the human ASD variants. We also found the same linked, homozygous allelic combination across the 4 novel OXTR SNPs (homozygous TGTC) in 6 of the 13 bonobos, indicating that this combination may be under positive selection. When comparing the combined OXTR genotypes, we found significant group differences in social behavior; bonobos with zero copies of the TGTC combination were less social than bonobos with one copy of the TGTC combination. Taken together, our findings suggest that these OXTR variants may influence individual-level social behavior in bonobos and support the notion that linked genetic variants are promising risk factors for social communication deficits in humans.
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Affiliation(s)
- Sara A. Skiba
- Ape Cognition and Conservation Initiative (Ape Initiative), Des Moines, IA
| | - Alek Hansen
- Kennesaw State University, Department of Molecular and Cellular Biology, Kennesaw, GA
| | - Ryan McCall
- Kennesaw State University, Department of Molecular and Cellular Biology, Kennesaw, GA
| | - Azeeza Byers
- Kennesaw State University, Department of Molecular and Cellular Biology, Kennesaw, GA
- Kennesaw State University, Department of Ecology, Evolution, and Organismal Biology, Kennesaw, GA
| | - Sarah Waldron
- Kennesaw State University, Department of Molecular and Cellular Biology, Kennesaw, GA
| | - Amanda J. Epping
- Ape Cognition and Conservation Initiative (Ape Initiative), Des Moines, IA
| | - Jared P. Taglialatela
- Ape Cognition and Conservation Initiative (Ape Initiative), Des Moines, IA
- Kennesaw State University, Department of Ecology, Evolution, and Organismal Biology, Kennesaw, GA
| | - Martin L. Hudson
- Kennesaw State University, Department of Molecular and Cellular Biology, Kennesaw, GA
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Brand CM, Kuang S, Gilbertson EN, McArthur E, Pollard KS, Webster TH, Capra JA. Sequence-based machine learning reveals 3D genome differences between bonobos and chimpanzees. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.564272. [PMID: 37961120 PMCID: PMC10634871 DOI: 10.1101/2023.10.26.564272] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Phenotypic divergence between closely related species, including bonobos and chimpanzees (genus Pan), is largely driven by variation in gene regulation. The 3D structure of the genome mediates gene expression; however, genome folding differences in Pan are not well understood. Here, we apply machine learning to predict genome-wide 3D genome contact maps from DNA sequence for 56 bonobos and chimpanzees, encompassing all five extant lineages. We use a pairwise approach to estimate 3D divergence between individuals from the resulting contact maps in 4,420 1 Mb genomic windows. While most pairs were similar, ∼17% were predicted to be substantially divergent in genome folding. The most dissimilar maps were largely driven by single individuals with rare variants that produce unique 3D genome folding in a region. We also identified 89 genomic windows where bonobo and chimpanzee contact maps substantially diverged, including several windows harboring genes associated with traits implicated in Pan phenotypic divergence. We used in silico mutagenesis to identify 51 3D-modifying variants in these bonobo-chimpanzee divergent windows, finding that 34 or 66.67% induce genome folding changes via CTCF binding motif disruption. Our results reveal 3D genome variation at the population-level and identify genomic regions where changes in 3D folding may contribute to phenotypic differences in our closest living relatives.
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Affiliation(s)
- Colin M. Brand
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA
| | - Shuzhen Kuang
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA
| | - Erin N. Gilbertson
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
- Biomedical Informatics Graduate Program, University of California San Francisco, San Francisco, CA
| | - Evonne McArthur
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN
| | - Katherine S. Pollard
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA
- Biomedical Informatics Graduate Program, University of California San Francisco, San Francisco, CA
- Chan Zuckerberg Biohub, San Francisco, CA
| | | | - John A. Capra
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA
- Biomedical Informatics Graduate Program, University of California San Francisco, San Francisco, CA
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