Characterization of Coding/Noncoding Variants for SHROOM3 in Patients with CKD
Interpreting genetic variants is one of the greatest challenges impeding analysis of rapidly increasing volumes of genomic data from patients. For example, is an associated risk gene for CKD, yet causative mechanism(s) of allele(s) are unknown. We used our analytic pipeline that integrates genetic,...
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| Veröffentlicht in: | Journal of the American Society of Nephrology Jg. 29; H. 5; S. 1525 |
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01.05.2018
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| Abstract | Interpreting genetic variants is one of the greatest challenges impeding analysis of rapidly increasing volumes of genomic data from patients. For example,
is an associated risk gene for CKD, yet causative mechanism(s) of
allele(s) are unknown.
We used our analytic pipeline that integrates genetic, computational, biochemical, CRISPR/Cas9 editing, molecular, and physiologic data to characterize coding and noncoding variants to study the human
risk locus for CKD.
We identified a novel
transcriptional start site, which results in a shorter isoform lacking the PDZ domain and is regulated by a common noncoding sequence variant associated with CKD (rs17319721, allele frequency: 0.35). This variant disrupted allele binding to the transcription factor TCF7L2 in podocyte cell nuclear extracts and altered transcription levels of
in cultured cells, potentially through the loss of repressive looping between rs17319721 and the novel start site. Although common variant mechanisms are of high utility, sequencing is beginning to identify rare variants involved in disease; therefore, we used our biophysical tools to analyze an average of 112,849 individual human genome sequences for rare SHROOM3 missense variants, revealing 35 high-effect variants. The high-effect alleles include a coding variant (P1244L) previously associated with CKD (
=0.01, odds ratio=7.95; 95% CI, 1.53 to 41.46) that we find to be present in East Asian individuals at an allele frequency of 0.0027. We determined that P1244L attenuates the interaction of
with 14-3-3, suggesting alterations to the Hippo pathway, a known mediator of CKD.
These data demonstrate multiple new
-dependent genetic/molecular mechanisms that likely affect CKD. |
|---|---|
| AbstractList | Interpreting genetic variants is one of the greatest challenges impeding analysis of rapidly increasing volumes of genomic data from patients. For example,
is an associated risk gene for CKD, yet causative mechanism(s) of
allele(s) are unknown.
We used our analytic pipeline that integrates genetic, computational, biochemical, CRISPR/Cas9 editing, molecular, and physiologic data to characterize coding and noncoding variants to study the human
risk locus for CKD.
We identified a novel
transcriptional start site, which results in a shorter isoform lacking the PDZ domain and is regulated by a common noncoding sequence variant associated with CKD (rs17319721, allele frequency: 0.35). This variant disrupted allele binding to the transcription factor TCF7L2 in podocyte cell nuclear extracts and altered transcription levels of
in cultured cells, potentially through the loss of repressive looping between rs17319721 and the novel start site. Although common variant mechanisms are of high utility, sequencing is beginning to identify rare variants involved in disease; therefore, we used our biophysical tools to analyze an average of 112,849 individual human genome sequences for rare SHROOM3 missense variants, revealing 35 high-effect variants. The high-effect alleles include a coding variant (P1244L) previously associated with CKD (
=0.01, odds ratio=7.95; 95% CI, 1.53 to 41.46) that we find to be present in East Asian individuals at an allele frequency of 0.0027. We determined that P1244L attenuates the interaction of
with 14-3-3, suggesting alterations to the Hippo pathway, a known mediator of CKD.
These data demonstrate multiple new
-dependent genetic/molecular mechanisms that likely affect CKD. Background Interpreting genetic variants is one of the greatest challenges impeding analysis of rapidly increasing volumes of genomic data from patients. For example, SHROOM3 is an associated risk gene for CKD, yet causative mechanism(s) of SHROOM3 allele(s) are unknown.Methods We used our analytic pipeline that integrates genetic, computational, biochemical, CRISPR/Cas9 editing, molecular, and physiologic data to characterize coding and noncoding variants to study the human SHROOM3 risk locus for CKD.Results We identified a novel SHROOM3 transcriptional start site, which results in a shorter isoform lacking the PDZ domain and is regulated by a common noncoding sequence variant associated with CKD (rs17319721, allele frequency: 0.35). This variant disrupted allele binding to the transcription factor TCF7L2 in podocyte cell nuclear extracts and altered transcription levels of SHROOM3 in cultured cells, potentially through the loss of repressive looping between rs17319721 and the novel start site. Although common variant mechanisms are of high utility, sequencing is beginning to identify rare variants involved in disease; therefore, we used our biophysical tools to analyze an average of 112,849 individual human genome sequences for rare SHROOM3 missense variants, revealing 35 high-effect variants. The high-effect alleles include a coding variant (P1244L) previously associated with CKD (P=0.01, odds ratio=7.95; 95% CI, 1.53 to 41.46) that we find to be present in East Asian individuals at an allele frequency of 0.0027. We determined that P1244L attenuates the interaction of SHROOM3 with 14-3-3, suggesting alterations to the Hippo pathway, a known mediator of CKD.Conclusions These data demonstrate multiple new SHROOM3-dependent genetic/molecular mechanisms that likely affect CKD.Background Interpreting genetic variants is one of the greatest challenges impeding analysis of rapidly increasing volumes of genomic data from patients. For example, SHROOM3 is an associated risk gene for CKD, yet causative mechanism(s) of SHROOM3 allele(s) are unknown.Methods We used our analytic pipeline that integrates genetic, computational, biochemical, CRISPR/Cas9 editing, molecular, and physiologic data to characterize coding and noncoding variants to study the human SHROOM3 risk locus for CKD.Results We identified a novel SHROOM3 transcriptional start site, which results in a shorter isoform lacking the PDZ domain and is regulated by a common noncoding sequence variant associated with CKD (rs17319721, allele frequency: 0.35). This variant disrupted allele binding to the transcription factor TCF7L2 in podocyte cell nuclear extracts and altered transcription levels of SHROOM3 in cultured cells, potentially through the loss of repressive looping between rs17319721 and the novel start site. Although common variant mechanisms are of high utility, sequencing is beginning to identify rare variants involved in disease; therefore, we used our biophysical tools to analyze an average of 112,849 individual human genome sequences for rare SHROOM3 missense variants, revealing 35 high-effect variants. The high-effect alleles include a coding variant (P1244L) previously associated with CKD (P=0.01, odds ratio=7.95; 95% CI, 1.53 to 41.46) that we find to be present in East Asian individuals at an allele frequency of 0.0027. We determined that P1244L attenuates the interaction of SHROOM3 with 14-3-3, suggesting alterations to the Hippo pathway, a known mediator of CKD.Conclusions These data demonstrate multiple new SHROOM3-dependent genetic/molecular mechanisms that likely affect CKD. |
| Author | McDermott-Roe, Chris Leysen, Seppe Lazar, Jozef Link, Brian A Rauscher, 3rd, Frank J Yeo, Nan Cher Sosonkina, Nadiya Jacob, Howard J Milroy, Lech-Gustav Ottmann, Christian Geurts, Aron M Freedman, Barry I Meijer, Femke A Mendenhall, Eric M Ross, Emily J Coppola, Candice J Ramaker, Ryne Prokop, Jeremy W Chhetri, Surya B Florus, Kacie L Flister, Michael J |
| Author_xml | – sequence: 1 givenname: Jeremy W surname: Prokop fullname: Prokop, Jeremy W email: jprokop54@gmail.com, jlazar@hudsonalpha.org organization: Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, Michigan – sequence: 2 givenname: Nan Cher surname: Yeo fullname: Yeo, Nan Cher organization: Department of Genetics, Harvard Medical School, Boston, Massachusetts – sequence: 3 givenname: Christian surname: Ottmann fullname: Ottmann, Christian organization: Department of Chemistry, University of Duisburg-Essen, Essen, Germany – sequence: 4 givenname: Surya B surname: Chhetri fullname: Chhetri, Surya B organization: Department of Biological Sciences, The University of Alabama in Huntsville, Huntsville, Alabama – sequence: 5 givenname: Kacie L surname: Florus fullname: Florus, Kacie L organization: HudsonAlpha Institute for Biotechnology, Huntsville, Alabama – sequence: 6 givenname: Emily J surname: Ross fullname: Ross, Emily J organization: Department of Chemical and Physical Biology, Vanderbilt University, Nashville, Tennessee – sequence: 7 givenname: Nadiya surname: Sosonkina fullname: Sosonkina, Nadiya organization: HudsonAlpha Institute for Biotechnology, Huntsville, Alabama – sequence: 8 givenname: Brian A surname: Link fullname: Link, Brian A organization: Department of Cell Biology, Neurobiology and Anatomy and – sequence: 9 givenname: Barry I surname: Freedman fullname: Freedman, Barry I organization: Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina; and – sequence: 10 givenname: Candice J surname: Coppola fullname: Coppola, Candice J organization: Department of Biological Sciences, The University of Alabama in Huntsville, Huntsville, Alabama – sequence: 11 givenname: Chris surname: McDermott-Roe fullname: McDermott-Roe, Chris organization: Department of Physiology, Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, Wisconsin – sequence: 12 givenname: Seppe surname: Leysen fullname: Leysen, Seppe organization: Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands – sequence: 13 givenname: Lech-Gustav surname: Milroy fullname: Milroy, Lech-Gustav organization: Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands – sequence: 14 givenname: Femke A surname: Meijer fullname: Meijer, Femke A organization: Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands – sequence: 15 givenname: Aron M surname: Geurts fullname: Geurts, Aron M organization: Department of Physiology, Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, Wisconsin – sequence: 16 givenname: Frank J surname: Rauscher, 3rd fullname: Rauscher, 3rd, Frank J organization: Gene Expression & Regulation Program, Wistar Institute, Philadelphia, Pennsylvania – sequence: 17 givenname: Ryne surname: Ramaker fullname: Ramaker, Ryne organization: HudsonAlpha Institute for Biotechnology, Huntsville, Alabama – sequence: 18 givenname: Michael J surname: Flister fullname: Flister, Michael J organization: Department of Physiology, Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, Wisconsin – sequence: 19 givenname: Howard J surname: Jacob fullname: Jacob, Howard J organization: HudsonAlpha Institute for Biotechnology, Huntsville, Alabama – sequence: 20 givenname: Eric M surname: Mendenhall fullname: Mendenhall, Eric M organization: Department of Biological Sciences, The University of Alabama in Huntsville, Huntsville, Alabama – sequence: 21 givenname: Jozef surname: Lazar fullname: Lazar, Jozef email: jprokop54@gmail.com, jlazar@hudsonalpha.org organization: HudsonAlpha Institute for Biotechnology, Huntsville, Alabama; jprokop54@gmail.com jlazar@hudsonalpha.org |
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| Keywords | Genomic Variants TCF7L2 SHROOM3 Rare Variants GWAS CRISPR/Cas9 |
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| Snippet | Interpreting genetic variants is one of the greatest challenges impeding analysis of rapidly increasing volumes of genomic data from patients. For example,
is... Background Interpreting genetic variants is one of the greatest challenges impeding analysis of rapidly increasing volumes of genomic data from patients. For... |
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| Title | Characterization of Coding/Noncoding Variants for SHROOM3 in Patients with CKD |
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