Forskning
Udskriv Udskriv
Switch language
Region Hovedstaden - en del af Københavns Universitetshospital
Udgivet

AA9int: SNP interaction pattern search using non-hierarchical additive model set

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. scVAE: variational auto-encoders for single-cell gene expression data

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Stronger findings for metabolomics through Bayesian modeling of multiple peaks and compound correlations

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Modeling tissue contamination to improve molecular identification of the primary tumor site of metastases

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  5. Multivariate multi-way analysis of multi-source data

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  • PRACTICAL consortium
Vis graf over relationer

Motivation: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions.

Results: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies.

Availability and implementation: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.

Supplementary information: Supplementary data are available at Bioinformatics online.

OriginalsprogEngelsk
TidsskriftBioinformatics
Vol/bind34
Udgave nummer24
Sider (fra-til)4141-4150
Antal sider10
ISSN1367-4803
DOI
StatusUdgivet - 15 dec. 2018

ID: 56619752