Clustrat: A structure informed clustering strategy for population stratification

Aritra Bose; Myson C. Burch; Agniva Chowdhury; Peristera Paschou; Petros Drineas

doi:10.1007/978-3-030-45257-5_19

Clustrat: A structure informed clustering strategy for population stratification

Aritra Bose, Myson C. Burch, Agniva Chowdhury, Peristera Paschou, Petros Drineas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Genome-wide association studies (GWAS) have been extensively used to estimate the signed effects of trait-associated alleles. One of the key challenges in GWAS are confounding factors, such as population stratification, which can lead to spurious genotype-trait associations. Recent independent studies [1, 8, 10] failed to replicate the strong evidence of previously reported signals of directional selection on height in Europeans in the UK Biobank cohort, and attributed the loss of signal to cryptic relatedness in populations.

Original language	English
Title of host publication	Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings
Editors	Russell Schwartz
Publisher	Springer
Pages	234-236
Number of pages	3
ISBN (Print)	9783030452568
DOIs	https://doi.org/10.1007/978-3-030-45257-5_19
State	Published - 2020
Externally published	Yes
Event	24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 - Padua, Italy Duration: May 10 2020 → May 13 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12074 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020
Country/Territory	Italy
City	Padua
Period	05/10/20 → 05/13/20

Funding

Supported by NSF IIS 1715202 and NFS DMS 1760353 awarded to PD and PP.

Access to Document

10.1007/978-3-030-45257-5_19

Cite this

Bose, A., Burch, M. C., Chowdhury, A., Paschou, P., & Drineas, P. (2020). Clustrat: A structure informed clustering strategy for population stratification. In R. Schwartz (Ed.), Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings (pp. 234-236). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12074 LNBI). Springer. https://doi.org/10.1007/978-3-030-45257-5_19

Bose, Aritra ; Burch, Myson C. ; Chowdhury, Agniva et al. / Clustrat : A structure informed clustering strategy for population stratification. Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings. editor / Russell Schwartz. Springer, 2020. pp. 234-236 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{69186ab111f9490482bbb0ed2bbeb0c7,

title = "Clustrat: A structure informed clustering strategy for population stratification",

abstract = "Genome-wide association studies (GWAS) have been extensively used to estimate the signed effects of trait-associated alleles. One of the key challenges in GWAS are confounding factors, such as population stratification, which can lead to spurious genotype-trait associations. Recent independent studies [1, 8, 10] failed to replicate the strong evidence of previously reported signals of directional selection on height in Europeans in the UK Biobank cohort, and attributed the loss of signal to cryptic relatedness in populations.",

author = "Aritra Bose and Burch, {Myson C.} and Agniva Chowdhury and Peristera Paschou and Petros Drineas",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 ; Conference date: 10-05-2020 Through 13-05-2020",

year = "2020",

doi = "10.1007/978-3-030-45257-5_19",

language = "English",

isbn = "9783030452568",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "234--236",

editor = "Russell Schwartz",

booktitle = "Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings",

}

Bose, A, Burch, MC, Chowdhury, A, Paschou, P & Drineas, P 2020, Clustrat: A structure informed clustering strategy for population stratification. in R Schwartz (ed.), Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12074 LNBI, Springer, pp. 234-236, 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020, Padua, Italy, 05/10/20. https://doi.org/10.1007/978-3-030-45257-5_19

Clustrat: A structure informed clustering strategy for population stratification. / Bose, Aritra; Burch, Myson C.; Chowdhury, Agniva et al.
Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings. ed. / Russell Schwartz. Springer, 2020. p. 234-236 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12074 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Clustrat

T2 - 24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020

AU - Bose, Aritra

AU - Burch, Myson C.

AU - Chowdhury, Agniva

AU - Paschou, Peristera

AU - Drineas, Petros

PY - 2020

Y1 - 2020

N2 - Genome-wide association studies (GWAS) have been extensively used to estimate the signed effects of trait-associated alleles. One of the key challenges in GWAS are confounding factors, such as population stratification, which can lead to spurious genotype-trait associations. Recent independent studies [1, 8, 10] failed to replicate the strong evidence of previously reported signals of directional selection on height in Europeans in the UK Biobank cohort, and attributed the loss of signal to cryptic relatedness in populations.

AB - Genome-wide association studies (GWAS) have been extensively used to estimate the signed effects of trait-associated alleles. One of the key challenges in GWAS are confounding factors, such as population stratification, which can lead to spurious genotype-trait associations. Recent independent studies [1, 8, 10] failed to replicate the strong evidence of previously reported signals of directional selection on height in Europeans in the UK Biobank cohort, and attributed the loss of signal to cryptic relatedness in populations.

UR - http://www.scopus.com/inward/record.url?scp=85084268814&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-45257-5_19

DO - 10.1007/978-3-030-45257-5_19

M3 - Conference contribution

AN - SCOPUS:85084268814

SN - 9783030452568

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 234

EP - 236

BT - Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings

A2 - Schwartz, Russell

PB - Springer

Y2 - 10 May 2020 through 13 May 2020

ER -

Bose A, Burch MC, Chowdhury A, Paschou P, Drineas P. Clustrat: A structure informed clustering strategy for population stratification. In Schwartz R, editor, Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings. Springer. 2020. p. 234-236. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-45257-5_19

Clustrat: A structure informed clustering strategy for population stratification

Abstract

Publication series

Conference

Funding

Access to Document

Other files and links

Fingerprint

Cite this