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      Large-scale functional screen identifies genetic variants with splicing effects in modern and archaic humans

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          Significance

          Our study reveals the impact of genetic differences in pre-mRNA splicing between modern and archaic humans. We used a massively parallel experiment to identify 962 splicing variants that differ between modern and archaic humans, including potentially pathogenic splicing variants unique to Neanderthals and Denisovans, and introgressed splicing variants that may explain differences in modern human traits. Our findings indicate that purifying selection acted against splicing variants in modern human populations, whereas positive selection favored splicing variants in adaptive introgression. By distinguishing causal from linked variants, our study contributes to understanding the functional consequences of genetic variation within extant and extinct hominins and of introgressed variation in modern human populations.

          Abstract

          Humans coexisted and interbred with other hominins which later became extinct. These archaic hominins are known to us only through fossil records and for two cases, genome sequences. Here, we engineer Neanderthal and Denisovan sequences into thousands of artificial genes to reconstruct the pre-mRNA processing patterns of these extinct populations. Of the 5,169 alleles tested in this massively parallel splicing reporter assay (MaPSy), we report 962 exonic splicing mutations that correspond to differences in exon recognition between extant and extinct hominins. Using MaPSy splicing variants, predicted splicing variants, and splicing quantitative trait loci, we show that splice-disrupting variants experienced greater purifying selection in anatomically modern humans than that in Neanderthals. Adaptively introgressed variants were enriched for moderate-effect splicing variants, consistent with positive selection for alternative spliced alleles following introgression. As particularly compelling examples, we characterized a unique tissue-specific alternative splicing variant at the adaptively introgressed innate immunity gene TLR1, as well as a unique Neanderthal introgressed alternative splicing variant in the gene HSPG2 that encodes perlecan. We further identified potentially pathogenic splicing variants found only in Neanderthals and Denisovans in genes related to sperm maturation and immunity. Finally, we found splicing variants that may contribute to variation among modern humans in total bilirubin, balding, hemoglobin levels, and lung capacity. Our findings provide unique insights into natural selection acting on splicing in human evolution and demonstrate how functional assays can be used to identify candidate causal variants underlying differences in gene regulation and phenotype.

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          Most cited references109

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            The variant call format and VCFtools

            Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk
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              The mutational constraint spectrum quantified from variation in 141,456 humans

              Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                16 May 2023
                23 May 2023
                16 November 2023
                : 120
                : 21
                : e2218308120
                Affiliations
                [1] aCenter for Computational Molecular Biology, Brown University , Providence, RI 02912
                [2] bDepartment of Molecular Biology, Cell Biology, and Biochemistry, Brown University , Providence, RI 02912
                [3] cDepartment of Biology, McMaster University , Hamilton, ON L8S 4K1, Canada
                [4] dHassenfeld Child Health Innovation Institute of Brown University , Providence, RI 02912
                Author notes
                1To whom correspondence may be addressed. Email: william_fairbrother@ 123456brown.edu .

                Edited by Manuel Irimia, Centre de Regulacio Genomica, Barcelona, Spain; received November 7, 2022; accepted April 12, 2023 by Editorial Board Member Alberto R. Kornblihtt

                Author information
                https://orcid.org/0000-0002-9522-9558
                https://orcid.org/0000-0002-9512-8845
                Article
                202218308
                10.1073/pnas.2218308120
                10214146
                37192163
                9f7a0c8c-9ab0-4d37-82a3-8783603fb962
                Copyright © 2023 the Author(s). Published by PNAS.

                This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 07 November 2022
                : 12 April 2023
                Page count
                Pages: 12, Words: 8920
                Funding
                Funded by: HHS | National Institutes of Health (NIH), FundRef 100000002;
                Award ID: R01 GM127472
                Award Recipient : William G. Fairbrother
                Funded by: National Science Foundation (NSF), FundRef 100000001;
                Award ID: 0966060
                Award Recipient : Stephen Rong Award Recipient : Christopher R. Neil Award Recipient : Anastasia Welch Award Recipient : Chaorui Duan Award Recipient : Samantha Maguire Award Recipient : Ijeoma C. Meremikwu Award Recipient : Malcolm Meyerson Award Recipient : Ben J Evans Award Recipient : William G. Fairbrother
                Funded by: National Science Foundation (NSF), FundRef 100000001;
                Award ID: 1644760
                Award Recipient : Stephen Rong Award Recipient : Christopher R. Neil Award Recipient : Anastasia Welch Award Recipient : Chaorui Duan Award Recipient : Samantha Maguire Award Recipient : Ijeoma C. Meremikwu Award Recipient : Malcolm Meyerson Award Recipient : Ben J Evans Award Recipient : William G. Fairbrother
                Categories
                dataset, Dataset
                research-article, Research Article
                genetics, Genetics
                419
                Biological Sciences
                Genetics

                rna splicing,modern humans,archaic humans,archaic introgression,massively parallel splicing assay

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