publication . Preprint . Other literature type . 2020

Improving the informativeness of Mendelian disease pathogenicity scores for common disease

Samuel S. Kim; Kushal K. Dey; Omer Weissbrod; Carla Marquez-Luna; Steven Gazal; Alkes L. Price;
Open Access English
  • Published: 03 Jan 2020
  • Publisher: Cold Spring Harbor Laboratory
Abstract
<jats:title>Abstract</jats:title><jats:p>Despite considerable progress on pathogenicity scores prioritizing both coding and non-coding variants for Mendelian disease, little is known about the utility of these pathogenicity scores for common disease. Here, we sought to assess the informativeness of Mendelian disease pathogenicity scores for common disease, and to improve upon existing scores. We first applied stratified LD score regression to assess the informativeness of annotations defined by top variants from published Mendelian disease pathogenicity scores across 41 independent common diseases and complex traits (average <jats:italic>N</jats:italic> = 320K)....
Subjects
free text keywords: Single-nucleotide polymorphism, Pathogenicity, Mendelian disease, Biology, Candidate gene, Regression, Genetics, Gradient boosting, Disease
Funded by
NIH| Functionally specialized components of disease heritability in ENCODE data
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U01HG009379-03
  • Funding stream: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
,
NIH| Network-based prediction and validation of causal schizophrenia genes and variants
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 7R01MH109978-03
  • Funding stream: NATIONAL INSTITUTE OF MENTAL HEALTH
,
NIH| Statistical methods for studies of rare variants
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01MH101244-02
  • Funding stream: NATIONAL INSTITUTE OF MENTAL HEALTH
,
NIH| Methods for linking GWAS peaks to function in psychiatric disease
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01MH107649-02
  • Funding stream: NATIONAL INSTITUTE OF MENTAL HEALTH

1. Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., and Sunyaev, S. R. (2010). A method and server for predicting damaging 2. Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., and Shendure, J.

33. Choi, Y., Sims, G. E., Murphy, S., Miller, J. R., and Chan, A. P. (2012). Predicting the 35. Qi, H., Chen, C., Zhang, H., Long, J. J., Chung, W. K., Guan, Y., and Shen, Y. (2018).

37. Stenson, P. D., Mort, M., Ball, E. V., Evans, K., Hayden, M., Heywood, S., Hussain, M., Phillips, A. D., and Cooper, D. N. (2017). The human gene mutation database: towards a 38. Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O'Donnell-Luria, A. H., Ware, J. S., Hill, A. J., Cummings, B. B., et al. (2016). Anal39. Dey, K. K., Van de Geijn, B., Kim, S. S., Hormozdiari, F., Kelley, D. R., and Price, A. L.

40. Hormozdiari, F., van de Geijn, B., Nasser, J., Weissbrod, O., Gazal, S., Ju, C. J.-T., O'Connor, L., Hujoel, M. L., Engreitz, J., Hormozdiari, F., et al. (2019). Functional disease 41. Lundberg, S. M. and Lee, S.-I. (2017). A uni ed approach to interpreting model predictions. 43. Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J., and Kircher, M. (2018). Cadd: 46. Zhou, J. and Troyanskaya, O. G. (2015). Predicting e ects of noncoding variants with deep 47. Zhou, J., Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G.

48. Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., et al. (2019). Whole-genome deep-learning analysis identi es 55. Weissbrod, O., Hormozdiari, F., Benner, C., Cui, R., Ulirsch, J., Gazal, S., Schoech, A. P., Van De Geijn, B., Reshef, Y., Marquez-Luna, C., et al. (2019). Functionally-informed 56. MacArthur, J., Bowler, E., Cerezo, M., Gil, L., Hall, P., Hastings, E., Junkins, H., McMahon, A., Milano, A., Morales, J., et al. (2016). The new nhgri-ebi catalog of published genome-wide McMahon, A., Morales, J., Mountjoy, E., Sollis, E., et al. (2018). The nhgri-ebi gwas catalog 59. Chen, W., McDonnell, S. K., Thibodeau, S. N., Tillmans, L. S., and Schaid, D. J. (2016). InS., Loh, P.-R., Lareau, C., Shoresh, N., et al. (2018). Heritability enrichment of speci cally Grossman, S. R., Anyoha, R., Doughty, B. R., Patwardhan, T. A., et al. (2019). Activity67. Liu, X., Jian, X., and Boerwinkle, E. (2011). dbnsfp: a lightweight database of human 68. Liu, X., Wu, C., Li, C., and Boerwinkle, E. (2016). dbnsfp v3. 0: A one-stop database 73. Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P., and Price, A. L. (2018). Mixed-model L., Perry, J. R., Patterson, N., Robinson, E. B., et al. (2015). An atlas of genetic correlations

Abstract
<jats:title>Abstract</jats:title><jats:p>Despite considerable progress on pathogenicity scores prioritizing both coding and non-coding variants for Mendelian disease, little is known about the utility of these pathogenicity scores for common disease. Here, we sought to assess the informativeness of Mendelian disease pathogenicity scores for common disease, and to improve upon existing scores. We first applied stratified LD score regression to assess the informativeness of annotations defined by top variants from published Mendelian disease pathogenicity scores across 41 independent common diseases and complex traits (average <jats:italic>N</jats:italic> = 320K)....
Subjects
free text keywords: Single-nucleotide polymorphism, Pathogenicity, Mendelian disease, Biology, Candidate gene, Regression, Genetics, Gradient boosting, Disease
Funded by
NIH| Functionally specialized components of disease heritability in ENCODE data
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5U01HG009379-03
  • Funding stream: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
,
NIH| Network-based prediction and validation of causal schizophrenia genes and variants
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 7R01MH109978-03
  • Funding stream: NATIONAL INSTITUTE OF MENTAL HEALTH
,
NIH| Statistical methods for studies of rare variants
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01MH101244-02
  • Funding stream: NATIONAL INSTITUTE OF MENTAL HEALTH
,
NIH| Methods for linking GWAS peaks to function in psychiatric disease
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01MH107649-02
  • Funding stream: NATIONAL INSTITUTE OF MENTAL HEALTH

1. Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., and Sunyaev, S. R. (2010). A method and server for predicting damaging 2. Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., and Shendure, J.

33. Choi, Y., Sims, G. E., Murphy, S., Miller, J. R., and Chan, A. P. (2012). Predicting the 35. Qi, H., Chen, C., Zhang, H., Long, J. J., Chung, W. K., Guan, Y., and Shen, Y. (2018).

37. Stenson, P. D., Mort, M., Ball, E. V., Evans, K., Hayden, M., Heywood, S., Hussain, M., Phillips, A. D., and Cooper, D. N. (2017). The human gene mutation database: towards a 38. Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O'Donnell-Luria, A. H., Ware, J. S., Hill, A. J., Cummings, B. B., et al. (2016). Anal39. Dey, K. K., Van de Geijn, B., Kim, S. S., Hormozdiari, F., Kelley, D. R., and Price, A. L.

40. Hormozdiari, F., van de Geijn, B., Nasser, J., Weissbrod, O., Gazal, S., Ju, C. J.-T., O'Connor, L., Hujoel, M. L., Engreitz, J., Hormozdiari, F., et al. (2019). Functional disease 41. Lundberg, S. M. and Lee, S.-I. (2017). A uni ed approach to interpreting model predictions. 43. Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J., and Kircher, M. (2018). Cadd: 46. Zhou, J. and Troyanskaya, O. G. (2015). Predicting e ects of noncoding variants with deep 47. Zhou, J., Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., and Troyanskaya, O. G.

48. Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., et al. (2019). Whole-genome deep-learning analysis identi es 55. Weissbrod, O., Hormozdiari, F., Benner, C., Cui, R., Ulirsch, J., Gazal, S., Schoech, A. P., Van De Geijn, B., Reshef, Y., Marquez-Luna, C., et al. (2019). Functionally-informed 56. MacArthur, J., Bowler, E., Cerezo, M., Gil, L., Hall, P., Hastings, E., Junkins, H., McMahon, A., Milano, A., Morales, J., et al. (2016). The new nhgri-ebi catalog of published genome-wide McMahon, A., Morales, J., Mountjoy, E., Sollis, E., et al. (2018). The nhgri-ebi gwas catalog 59. Chen, W., McDonnell, S. K., Thibodeau, S. N., Tillmans, L. S., and Schaid, D. J. (2016). InS., Loh, P.-R., Lareau, C., Shoresh, N., et al. (2018). Heritability enrichment of speci cally Grossman, S. R., Anyoha, R., Doughty, B. R., Patwardhan, T. A., et al. (2019). Activity67. Liu, X., Jian, X., and Boerwinkle, E. (2011). dbnsfp: a lightweight database of human 68. Liu, X., Wu, C., Li, C., and Boerwinkle, E. (2016). dbnsfp v3. 0: A one-stop database 73. Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P., and Price, A. L. (2018). Mixed-model L., Perry, J. R., Patterson, N., Robinson, E. B., et al. (2015). An atlas of genetic correlations

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