Active retrotransposons play important roles during evolution and continue to shape our genomes today, especially in genetic polymorphisms underlying a diverse set of diseases. However, studies of human retrotransposon insertion polymorphisms (RIPs) based on whole-genome deep sequencing at the population level have not been sufficiently undertaken, despite the obvious need for a thorough characterization of RIPs in the general population.
Herein, we present a novel and efficient computational tool named Specific Insertions Detector (SID) for the detection of non-reference RIPs. We demonstrate that SID is suitable for high depth whole-genome sequencing (WGS) data using paired-end reads obtained from simulated and real datasets. We construct a comprehensive RIP database using a large population of 90 Han Chinese individuals with a mean 68× depth per individual. In total, we identify 9342 recent RIPs, and 8433 of these RIPs are novel compared with dbRIP, including 5826 Alu, 2169 long interspersed nuclear element 1 (L1), 383 SVA, and 55 long terminal repeats (LTR). Among the 9342 RIPs, 4828 were located in gene regions and five were located in protein-coding regions. We demonstrate that RIPs can, in principle, be an informative resource to perform population evolution and phylogenetic analyses. Taking the demographic effects into account, we identify a weak negative selection on SVA and L1 but approximately neutral selection for Alu elements based on the frequency spectrum of RIPs.
SID is a powerful open-source program for the detection of non-reference RIPs. We built a non-reference RIP dataset that greatly enhanced the diversity of RIPs detected in the general population and should be invaluable to researchers interested in many aspects of human evolution, genetics, and disease. As a proof-of-concept, we demonstrate that the RIPs can be used as biomarkers in a similar way as single nucleotide polymorphisms (SNPs).

Keywords:

• Transposable element
• retrotransposon insertion polymorphism
• next-generation sequencing
• whole-genome sequencing

Read the peer-reviewed publication(s):

(PubMed: 28938719)
(PubMed: 30753694)

Related datasets:

doi:10.5524/100318 Cites doi:10.5524/100096

Additional information:

https://github.com/Jonathanyu2014/SID

Accessions (data referenced by this study):

BioProject: PRJEB11005