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On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows
Loehlein Fier Heide, Prokopenko Dmitry, Hecker Julian, Cho Michael H, Silverman Edwin K, Weiss Scott T, Tanzi Rudolph E, Lange Christoph. Genetic epidemiology, 2017-03-21
Topic: Brain proteins, microglia and lab models
For the association analysis of whole-genome sequencing (WGS) studies, we propose an efficient and fast spatial-clustering algorithm. Compared to existing analysis approaches for WGS data, that define the tested regions either by sliding or consecutive windows of fixed sizes along variants, a meaningful grouping of nearby variants into consecutive regions has the advantage that, compared to sliding window approaches, the number of tested regions is likely to be smaller. In comparison to consecutive, fixed-window approaches, our approach is likely to group nearby variants together. Given existing biological evidence that disease-associated mutations tend to physically cluster in specific regions along the chromosome, the identification of meaningful groups of nearby located variants could thus lead to a potential power gain for association analysis. Our algorithm defines consecutive genomic regions based on the physical positions of the variants, assuming an inhomogeneous Poisson process and groups together nearby variants. As parameters are estimated locally, the algorithm takes the differing variant density along the chromosome into account and provides locally optimal partitioning of variants into consecutive regions. An R-implementation of the algorithm is provided. We discuss the theoretical advances of our algorithm compared to existing, window-based approaches and show the performance and advantage of our introduced algorithm in a simulation study and by an application to Alzheimer's disease WGS data. Our analysis identifies a region in the ITGB3 gene that potentially harbors disease susceptibility loci for Alzheimer's disease. The region-based association signal of ITGB3 replicates in an independent data set and achieves formally genome-wide significance. Software Implementation: An implementation of the algorithm in R is available at: https://github.com/heidefier/cluster_wgs_data.
Key findings
- Compared to existing analysis approaches for WGS data, that define the tested regions either by sliding or consecutive windows of fixed sizes along variants, a meaningful grouping of nearby variants into consecutive regions has the advantage that, compared to sliding window approaches, the number of tested regions is likely to be smaller.
- We discuss the theoretical advances of our algorithm compared to existing, window-based approaches and show the performance and advantage of our introduced algorithm in a simulation study and by an application to Alzheimer's disease WGS data.
Why it matters
This paper proposes a statistical method for grouping genetic variants into regions when analyzing whole-genome sequencing data. It is a methods paper and reports no findings about treating, curing, or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 35/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Genetic epidemiology) |
| Journal's citation record | 1 / 4 | Genetic Epidemiology: journal h-index 132 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2017 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 9 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.8 citations a year over 11 year(s) since 2017 |
| Impact for its field and year | 1 / 6 | Cited 0.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Christoph Lange: h-index 71 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Harvard University: institution h-index 3,235 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28318110) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 35 / 100 | — |
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Heide, L. F., Dmitry, P., Julian, H., Cho Michael, H., Silverman Edwin, K., Weiss Scott, T., et al. (2017). On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows. Genetic epidemiology. https://doi.org/10.1002/gepi.22040
Vancouver
Heide LF, Dmitry P, Julian H, Cho Michael H, Silverman Edwin K, Weiss Scott T, et al. On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows. Genetic epidemiology. 2017. doi:10.1002/gepi.22040. PMID: 28318110.
BibTeX
@article{heide2017-28318110,
author = {Heide, Loehlein Fier and Dmitry, Prokopenko and Julian, Hecker and Cho Michael, H. and Silverman Edwin, K. and Weiss Scott, T. and Tanzi Rudolph, E. and Christoph, Lange},
title = {{On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows}},
journal = {Genetic epidemiology},
year = {2017},
doi = {10.1002/gepi.22040},
pmid = {28318110},
url = {https://doi.org/10.1002/gepi.22040}
}
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