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Home Data Analysis

The mutational constraint spectrum quantified from variation in 141,456 humans

globalresearchsyndicate by globalresearchsyndicate
May 27, 2020
in Data Analysis
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The mutational constraint spectrum quantified from variation in 141,456 humans
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  • 1.

    MacArthur, D. G. et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012).

  • 2.

    Schneeberger, K. Using next-generation sequencing to isolate mutant genes from forward genetic screens. Nat. Rev. Genet. 15, 662–676 (2014).

  • 3.

    Zambrowicz, B. P. & Sands, A. T. Knockouts model the 100 best-selling drugs—will they model the next 100? Nat. Rev. Drug Discov. 2, 38–51 (2003).

  • 4.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  • 5.

    Chong, J. X. et al. The genetic basis of mendelian phenotypes: discoveries, challenges, and opportunities. Am. J. Hum. Genet. 97, 199–215 (2015).

  • 6.

    Cohen, J. C., Boerwinkle, E., Mosley, T. H., Jr & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006).

  • 7.

    Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014).

  • 8.

    Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S. & Goldstein, D. B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013).

  • 9.

    Cassa, C. A. et al. Estimating the selective effects of heterozygous protein-truncating variants from human exome data. Nat. Genet. 49, 806–810 (2017).

  • 10.

    Petrovski, S. et al. The intolerance of regulatory sequence to genetic variation predicts gene dosage sensitivity. PLoS Genet. 11, e1005492 (2015).

  • 11.

    Collins, R. L. et al. A structural variation reference for medical and population genetics. Nature https://doi.org/10.1038/s41586-020-2287-8 (2020).

  • 12.

    Minikel, E. V. et al. Evaluating drug targets through human loss-of-function genetic variation. Nature https://doi.org/10.1038/s41586-020-2267-z (2020).

  • 13.

    Whiffin, N. et al. The effect of LRRK2 loss-of-function variants in humans. Nature Med. https://doi.org/10.1038/s41591-020-0893-5 (2020).

  • 14.

    GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

  • 15.

    Cummings, B. B. et al. Transcript expression-aware annotation improves rare variant interpretation. Nature https://doi.org/10.1038/s41586-020-2329-2 (2020).

  • 16.

    Wang, Q. et al. Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes. Nat. Commun. https://doi.org/10.1038/s41467-019-12438-5 (2020).

  • 17.

    Whiffin, N. et al. Characterising the loss-of-function impact of 5′ untranslated region variants in whole genome sequence data from 15,708 individuals. Nat. Commun. https://doi.org/10.1038/s41467-019-10717-9 (2019).

  • 18.

    Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.19.33 (2013).

  • 19.

    Hail Team. Hail 0.2.19; https://github.com/hail-is/hail/releases/tag/0.2.19 (released 2 August 2019).

  • 20.

    Jónsson, H. et al. Parental influence on human germline de novo mutations in 1,548 trios from Iceland. Nature 549, 519–522 (2017).

  • 21.

    Motenko, H., Neuhauser, S. B., O’Keefe, M. & Richardson, J. E. MouseMine: a new data warehouse for MGI. Mamm. Genome 26, 325–330 (2015).

  • 22.

    Eppig, J. T., Blake, J. A., Bult, C. J., Kadin, J. A. & Richardson, J. E. The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease. Nucleic Acids Res. 43, D726–D736 (2015).

  • 23.

    Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 (Bethesda) 7, 2719–2727 (2017).

  • 24.

    Feiglin, A., Allen, B. K., Kohane, I. S. & Kong, S. W. Comprehensive analysis of tissue-wide gene expression and phenotype data reveals tissues affected in rare genetic disorders. Cell Syst. 5, 140–148.e2 (2017).

  • 25.

    Gravel, S. When is selection effective? Genetics 203, 451–462 (2016).

  • 26.

    Henn, B. M., Botigué, L. R., Bustamante, C. D., Clark, A. G. & Gravel, S. Estimating the mutation load in human genomes. Nat. Rev. Genet. 16, 333–343 (2015).

  • 27.

    Bamshad, M. J., Nickerson, D. A. & Chong, J. X. mendelian gene discovery: fast and furious with no end in sight. Am. J. Hum. Genet. 105, 448–455 (2019).

  • 28.

    Walters, J. T. R. et al. The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability. Nat. Genet. 511, 421 (2017).

  • 29.

    Ganna, A. et al. Quantifying the impact of rare and uTheltra-rare coding variation across the phenotypic spectrum. Am. J. Hum. Genet. 102, 1204–1211 (2018).

  • 30.

    Ganna, A. et al. Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat. Neurosci. 19, 1563–1565 (2016).

  • 31.

    Genovese, G. et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19, 1433–1441 (2016).

  • 32.

    Eilbeck, K., Quinlan, A. & Yandell, M. Settling the score: variant prioritization and Mendelian disease. Nat. Rev. Genet. 18, 599–612 (2017).

  • 33.

    DeBoever, C. et al. Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. Nat. Commun. 9, 1612 (2018).

  • 34.

    Emdin, C. A. et al. Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease. Nat. Commun. 9, 1613 (2018).

  • 35.

    Satterstrom, F. K. et al. Autism spectrum disorder and attention deficit hyperactivity disorder have a similar burden of rare protein-truncating variants. Nat. Neurosci. 22, 1961–1965 (2019).

  • 36.

    de Andrade, K. C. et al. Variable population prevalence estimates of germline TP53 variants: a gnomAD-based analysis. Hum. Mutat. 40, 97–105 (2019).

  • 37.

    Laver, T. W. et al. Analysis of large-scale sequencing cohorts does not support the role of variants in UCP2 as a cause of hyperinsulinaemic hypoglycaemia. Hum. Mutat. 38, 1442–1444 (2017).

  • 38.

    Sundaram, L. et al. Predicting the clinical impact of human mutation with deep neural networks. Nat. Genet. 50, 1161–1170 (2018).

  • 39.

    Glassberg, E. C., Lan, X. & Pritchard, J. K. Evidence for weak selective constraint on human gene expression. Genetics 211, 757–772 (2019).

  • 40.

    El-Brolosy, M. A. et al. Genetic compensation triggered by mutant mRNA degradation. Nature 568, 193–197 (2019).

  • 41.

    Tuladhar, R. et al. CRISPR-Cas9-based mutagenesis frequently provokes on-target mRNA misregulation. Nat. Commun. 10, 4056 (2019).

  • 42.

    Findlay, G. M. et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562, 217–222 (2018).

  • 43.

    Short, P. J. et al. De novo mutations in regulatory elements in neurodevelopmental disorders. Nature 555, 611–616 (2018).

  • 44.

    Martin, A. R., Kanai, M., Kamatani, Y., Neale, B. M. & Daly, M. J. Hidden ‘risk’ in polygenic scores: clinical use today could exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

  • 45.

    Fuller, Z., Berg, J. J., Mostafavi, H., Sella, G. & Przeworski, M. Measuring intolerance to mutation in human genetics. Nat. Genet. 51, 772–776 (2019).

  • 46.

    McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw. 3, 861 (2018).

  • 47.

    Diaz-Papkovich, A., Anderson-Trocme, L. & Gravel, S. UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts. PLoS Genet. (2018). https://doi.org/10.1371/journal.pgen.1008432

  • 48.

    Finucane, H. K. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

  • 49.

    Zook, J. M. et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat. Biotechnol. 32, 246–251 (2014).

  • 50.

    Li, H. et al. A synthetic-diploid benchmark for accurate variant-calling evaluation. Nat. Methods 15, 595–597 (2018).

  • 51.

    Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).

  • 52.

    Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Naturey 485, 242–245 (2012).

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