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| Title |
ARG-walker: inference of individual specific strengths of meiotic recombination hotspots by population genomics analysis
|
|---|---|
| Published in |
BMC Genomics, December 2015
|
| DOI | 10.1186/1471-2164-16-s12-s1 |
| Pubmed ID | |
| Authors |
Hao Chen, Peng Yang, Jing Guo, Chee Keong Kwoh, Teresa M Przytycka, Jie Zheng |
| Abstract |
Meiotic recombination hotspots play important roles in various aspects of genomics, but the underlying mechanisms for regulating the locations and strengths of recombination hotspots are not yet fully revealed. Most existing algorithms for estimating recombination rates from sequence polymorphism data can only output average recombination rates of a population, although there is evidence for the heterogeneity in recombination rates among individuals. For genome-wide association studies (GWAS) of recombination hotspots, an efficient algorithm that estimates the individualized strengths of recombination hotspots is highly desirable. In this work, we propose a novel graph mining algorithm named ARG-walker, based on random walks on ancestral recombination graphs (ARG), to estimate individual-specific recombination hotspot strengths. Extensive simulations demonstrate that ARG-walker is able to distinguish the hot allele of a recombination hotspot from the cold allele. Integrated with output of ARG-walker, we performed GWAS on the phased haplotype data of the 22 autosome chromosomes of the HapMap Asian population samples of Chinese and Japanese (JPT+CHB). Significant cis-regulatory signals have been detected, which is corroborated by the enrichment of the well-known 13-mer motif CCNCCNTNNCCNC of PRDM9 protein. Moreover, two new DNA motifs have been identified in the flanking regions of the significantly associated SNPs (single nucleotide polymorphisms), which are likely to be new cis-regulatory elements of meiotic recombination hotspots of the human genome. Our results on both simulated and real data suggest that ARG-walker is a promising new method for estimating the individual recombination variations. In the future, it could be used to uncover the mechanisms of recombination regulation and human diseases related with recombination hotspots. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 7% |
| Unknown | 14 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 33% |
| Student > Ph. D. Student | 4 | 27% |
| Student > Master | 2 | 13% |
| Student > Bachelor | 1 | 7% |
| Unknown | 3 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 33% |
| Biochemistry, Genetics and Molecular Biology | 4 | 27% |
| Medicine and Dentistry | 2 | 13% |
| Psychology | 1 | 7% |
| Unknown | 3 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 August 2016.
All research outputs
#14,242,730
of 22,835,198 outputs
Outputs from BMC Genomics
#5,703
of 10,655 outputs
Outputs of similar age
#203,689
of 389,036 outputs
Outputs of similar age from BMC Genomics
#208
of 342 outputs
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