The functional impact of rare variation across the regulatory cascade

Taibo Li; Nicole Ferraro; Benjamin J Strober; Francois Aguet; Silva Kasela; Marios Arvanitis; Bohan Ni; Laurens Wiel; Elliot Hershberg; Kristin Ardlie; Dan E Arking; Rebecca L Beer; Jennifer Brody; Thomas W Blackwell; Clary Clish; Stacey Gabriel; Robert Gerszten; Xiuqing Guo; Namrata Gupta; W Craig Johnson; Tuuli Lappalainen; Henry J Lin; Yongmei Liu; Deborah A Nickerson; George Papanicolaou; Jonathan K Pritchard; Pankaj Qasba; Ali Shojaie; Josh Smith; Nona Sotoodehnia; Kent D Taylor; Russell P Tracy; David Van Den Berg; Matthew T Wheeler; Stephen S Rich; Jerome I Rotter; Alexis Battle; Stephen B Montgomery

doi:10.1016/j.xgen.2023.100401

The functional impact of rare variation across the regulatory cascade

Cell Genom. 2023 Sep 6;3(10):100401. doi: 10.1016/j.xgen.2023.100401. eCollection 2023 Oct 11.

Authors

Taibo Li¹, Nicole Ferraro², Benjamin J Strober^{1

3}, Francois Aguet⁴, Silva Kasela^{5

6}, Marios Arvanitis^{1

7}, Bohan Ni⁸, Laurens Wiel⁹, Elliot Hershberg¹⁰, Kristin Ardlie⁴, Dan E Arking¹¹, Rebecca L Beer¹², Jennifer Brody¹³, Thomas W Blackwell¹⁴, Clary Clish⁴, Stacey Gabriel⁴, Robert Gerszten^{4

15}, Xiuqing Guo¹⁶, Namrata Gupta⁴, W Craig Johnson¹⁷, Tuuli Lappalainen^{5

6}, Henry J Lin¹⁶, Yongmei Liu¹⁸, Deborah A Nickerson¹⁹, George Papanicolaou¹², Jonathan K Pritchard²⁰, Pankaj Qasba¹², Ali Shojaie²¹, Josh Smith¹⁹, Nona Sotoodehnia¹³, Kent D Taylor¹⁶, Russell P Tracy²², David Van Den Berg²³, Matthew T Wheeler⁹, Stephen S Rich²⁴, Jerome I Rotter¹⁶, Alexis Battle^{1

8

11

25}, Stephen B Montgomery^{10

26}

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
² Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
³ Harvard School of Public Health, Epidemiology Department, Boston, MA, USA.
⁴ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ New York Genome Center, New York, NY, USA.
⁶ Department of Systems Biology, Columbia University, New York, NY, USA.
⁷ Department of Medicine, Division of Cardiology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
⁸ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
⁹ Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹⁰ Department of Genetics, Stanford University, Stanford, CA, USA.
¹¹ McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
¹² National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
¹³ Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA.
¹⁴ Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
¹⁵ Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
¹⁶ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
¹⁷ Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, USA.
¹⁸ Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
¹⁹ Department of Genome Sciences, University of Washington, Seattle, WA, USA.
²⁰ Department of Genetics and Biology, Stanford University, Palo Alto, CA, USA.
²¹ Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA.
²² Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, USA.
²³ Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.
²⁴ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
²⁵ Malone Center for Engineering of Healthcare, Johns Hopkins University, Baltimore, MD, USA.
²⁶ Department of Pathology, Stanford University, Stanford, CA, USA.

Abstract

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Keywords: functional genomics; machine learning; methylome; multi-omics; proteome; rare variants; transcriptome.

Abstract

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