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Ultrarare variants drive substantial cis heritability of human gene expression

Nature Genetics volume 51, pages 1349–1355 (2019)Cite this article

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Abstract

The vast majority of human mutations have minor allele frequencies under 1%, with the plurality observed only once (that is, ‘singletons’). While Mendelian diseases are predominantly caused by rare alleles, their cumulative contribution to complex phenotypes is largely unknown. We develop and rigorously validate an approach to jointly estimate the contribution of all alleles, including singletons, to phenotypic variation. We apply our approach to transcriptional regulation, an intermediate between genetic variation and complex disease. Using whole-genome DNA and lymphoblastoid cell line RNA sequencing data from 360 European individuals, we conservatively estimate that singletons contribute approximately 25% of cis heritability across genes (dwarfing the contributions of other frequencies). The majority (approximately 76%) of singleton heritability derives from ultrarare variants absent from thousands of additional samples. We develop an inference procedure to demonstrate that our results are consistent with pervasive purifying selection shaping the regulatory architecture of most human genes.

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Fig. 1: Simulation results.
Fig. 2: Partitioning heritability.
Fig. 3: Pervasive purifying selection drives the genetic architecture of gene expression.

Data availability

RNA-seq gene expression data were downloaded from http://www.internationalgenome.org/data-portal/data-collection/geuvadis. This dataset contains 375 individuals of European descent from 4 locations. Each of these individuals are contained in the 1KGP and genome sequence data were downloaded from www.1000genomes.org (ref. 24).

Code availability

Three open source software tools are being made available as part of this study; all are available on GitHub: (1) HEh2.R—R code that performs all the Haseman–Elston analyses and simulations discussed in this paper. It also implements the artificial intelligence algorithm for parameter inference of linear mixed models. It is available from https://github.com/hernrya/HEh2; (2) SingHer R package discussed in the Supplementary Note, with performance statistics and available from https://github.com/andywdahl/SingHer; and (3) rejection sampling: scripts demonstrating how we used rejection sampling to infer parameters of the phenotype model are available from https://github.com/uricchio/HE_scripts.

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Acknowledgements

We thank H. M. Kang, A. Auton and S. Gusev for discussions about possible confounders that improved our analysis; members of the Pritchard laboratory for comments on rejection sampling; J. Barrett and K. Karczewski for peer-reviewing our preprint; R. Torres for assistance with data analysis; J. Wall for assistance with the Neanderthal-introgressed alleles; and A. Hernandez for discussions on figure colors. Research reported in this publication was supported by the National Human Genome Research Institute of the National Institutes of Health (award no. R01HG007644 to R.D.H. and award no. K25HL121295). L.H.U. was supported by an Institutional Research and Academic Career Development Award (National Institute of General Medical Sciences, grant no. K12GM088033). K.H. was supported by a Gilliam Fellowship for Advanced Study; A.D. was supported by NIH (award nos. U01HG009080 and R01HG006399).

Author information

Authors and Affiliations

  1. Bioengineering & Therapeutic Sciences, UCSF, San Francisco, CA, USA

    Ryan D. Hernandez

  2. Institute for Human Genetics, UCSF, San Francisco, CA, USA

    Ryan D. Hernandez, Chun Ye, Andrew Dahl & Noah Zaitlen

  3. Institute for Quantitative Biosciences, UCSF, San Francisco, CA, USA

    Ryan D. Hernandez, Andrew Dahl & Noah Zaitlen

  4. Institute for Computational Health Sciences, UCSF, San Francisco, CA, USA

    Ryan D. Hernandez

  5. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Ryan D. Hernandez

  6. McGill University and the Genome Quebec Innovation Center, Montreal, Quebec, Canada

    Ryan D. Hernandez

  7. Department of Biology, Stanford University, Stanford, CA, USA

    Lawrence H. Uricchio

  8. Biological and Medical Informatics Graduate Program, UCSF, San Francisco, CA, USA

    Kevin Hartman

  9. Epidemiology & Biostatistics, UCSF, San Francisco, CA, USA

    Chun Ye

  10. Department of Medicine Lung Biology Center, UCSF, San Francisco, CA, USA

    Noah Zaitlen

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  1. Ryan D. Hernandez
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Contributions

R.D.H. and N.Z. conceived and designed the study. L.H.U. and A.D. developed methods. R.D.H., L.H.U., K.H., C.Y., A.D. and N.Z. contributed to data analysis or simulations. R.D.H. and N.Z. wrote the manuscript. All authors read and approved the manuscript.

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Correspondence to Ryan D. Hernandez or Noah Zaitlen.

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Supplementary Information

Supplementary Note, Tables 1 and 2, and Figs. 1–27

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Hernandez, R.D., Uricchio, L.H., Hartman, K. et al. Ultrarare variants drive substantial cis heritability of human gene expression. Nat Genet 51, 1349–1355 (2019). https://doi.org/10.1038/s41588-019-0487-7

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