Literature DB >> 32500240

Rare variant association testing in the non-coding genome.

Ozvan Bocher1, Emmanuelle Génin2,3.   

Abstract

The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.

Entities:  

Year:  2020        PMID: 32500240     DOI: 10.1007/s00439-020-02190-y

Source DB:  PubMed          Journal:  Hum Genet        ISSN: 0340-6717            Impact factor:   4.132


  5 in total

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Authors:  Xuan Song; Hai Yun Gao; Karl Herrup; Ronald P Hart
Journal:  J Bioinform Comput Biol       Date:  2022-01-06       Impact factor: 1.204

2.  Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources.

Authors:  Neel Patel; William S Bush
Journal:  BMC Bioinformatics       Date:  2021-04-19       Impact factor: 3.307

3.  Mining massive genomic data of two Swiss Braunvieh cattle populations reveals six novel candidate variants that impair reproductive success.

Authors:  Irene M Häfliger; Franz R Seefried; Mirjam Spengeler; Cord Drögemüller
Journal:  Genet Sel Evol       Date:  2021-12-16       Impact factor: 4.297

4.  Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score.

Authors:  Ozvan Bocher; Thomas E Ludwig; Marie-Sophie Oglobinsky; Gaëlle Marenne; Jean-François Deleuze; Suryakant Suryakant; Jacob Odeberg; Pierre-Emmanuel Morange; David-Alexandre Trégouët; Hervé Perdry; Emmanuelle Génin
Journal:  PLoS Genet       Date:  2022-09-16       Impact factor: 6.020

5.  A haplotype in the dipeptidyl peptidase 4 gene impacts glycemic-related traits of Brazilian older adults.

Authors:  E S Alves; A C Tonet-Furioso; V P Alves; C F Moraes; D I V Pérez; I M D Bastos; C Córdova; O T Nóbrega
Journal:  Braz J Med Biol Res       Date:  2022-10-03       Impact factor: 2.904

  5 in total

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