A spectral approach integrating functional genomic annotations for coding and noncoding variants

被引：0
|
作者：
Iuliana Ionita-Laza
Kenneth McCallum
Bin Xu
Joseph D Buxbaum
机构：
[1] Columbia University,Department of Biostatistics
[2] Columbia University,Department of Psychiatry
[3] Seaver Autism Center for Research and Treatment,Department of Psychiatry
[4] Icahn School of Medicine at Mount Sinai,Department of Genetics and Genomic Sciences
[5] Icahn School of Medicine at Mount Sinai,Department of Neuroscience
[6] Icahn School of Medicine at Mount Sinai,undefined
[7] Icahn School of Medicine at Mount Sinai,undefined
[8] Mindich Child Health and Development Institute,undefined
[9] Icahn School of Medicine at Mount Sinai,undefined
来源：
Nature Genetics | 2016年 / 48卷
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Iuliana Ionita-Laza, Kenneth McCallum and colleagues developed an unsupervised statistical approach, Eigen, that integrates different functional annotations into a single measure of functional importance for coding and noncoding variants. Their meta-score can outperform the recently proposed CADD score and can be applied to fine-mapping studies.
引用
收藏
页码：214 / 220
页数：6
相关论文
共 50 条
  • [1] A spectral approach integrating functional genomic annotations for coding and noncoding variants
    Ionita-Laza, Iuliana
    McCallum, Kenneth
    Xu, Bin
    Buxbaum, Joseph D.
    [J]. NATURE GENETICS, 2016, 48 (02) : 214 - 220
  • [2] INTEGRATING FUNCTIONAL GENOMIC DATA TO PRIORITIZE CANDIDATE NON-CODING VARIANTS IN MIGRAINE SUSCEPTIBILITY
    Felicio, Daniela
    Martins, Sandra
    Santos, Mariana
    Lopes, Alexandra M.
    Lemos, Carolina
    Pinto, Nadia
    Alves-Ferreira, Miguel
    [J]. MEDICINE, 2023, 102 (13)
  • [3] Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize
    Ramstein, Guillaume P.
    Buckler, Edward S.
    [J]. GENOME BIOLOGY, 2022, 23 (01)
  • [4] Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize
    Guillaume P. Ramstein
    Edward S. Buckler
    [J]. Genome Biology, 23
  • [5] Analysis of Gene Variants for Functional Annotations
    Al-Mubaid, Hisham
    [J]. 2019 16TH IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY - CIBCB 2019, 2019, : 121 - 127
  • [6] Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data
    Huang, Yi-Fei
    Gulko, Brad
    Siepel, Adam
    [J]. NATURE GENETICS, 2017, 49 (04) : 618 - +
  • [7] Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data
    Yi-Fei Huang
    Brad Gulko
    Adam Siepel
    [J]. Nature Genetics, 2017, 49 : 618 - 624
  • [8] Functional annotation of noncoding sequence variants
    Ritchie, Graham R. S.
    Dunham, Ian
    Zeggini, Eleftheria
    Flicek, Paul
    [J]. NATURE METHODS, 2014, 11 (03) : 294 - U351
  • [9] Functional annotation of noncoding sequence variants
    Graham R S Ritchie
    Ian Dunham
    Eleftheria Zeggini
    Paul Flicek
    [J]. Nature Methods, 2014, 11 : 294 - 296
  • [10] Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations
    Gazal, Steven
    Loh, Po-Ru
    Finucane, Hilary K.
    Ganna, Andrea
    Schoech, Armin
    Sunyaev, Shamil
    Price, Alkes L.
    [J]. NATURE GENETICS, 2018, 50 (11) : 1600 - +
12345