Ancestry-specific association mapping in admixed populations

Research output: Contribution to journalJournal articlepeer-review

Standard

Ancestry-specific association mapping in admixed populations. / Skotte, Line; Jørsboe, Emil; Korneliussen, Thorfinn S; Moltke, Ida; Albrechtsen, Anders.

In: Genetic Epidemiology, Vol. 43, No. 5, 2019, p. 506-521.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Skotte, L, Jørsboe, E, Korneliussen, TS, Moltke, I & Albrechtsen, A 2019, 'Ancestry-specific association mapping in admixed populations', Genetic Epidemiology, vol. 43, no. 5, pp. 506-521. https://doi.org/10.1002/gepi.22200

APA

Skotte, L., Jørsboe, E., Korneliussen, T. S., Moltke, I., & Albrechtsen, A. (2019). Ancestry-specific association mapping in admixed populations. Genetic Epidemiology, 43(5), 506-521. https://doi.org/10.1002/gepi.22200

Vancouver

Skotte L, Jørsboe E, Korneliussen TS, Moltke I, Albrechtsen A. Ancestry-specific association mapping in admixed populations. Genetic Epidemiology. 2019;43(5):506-521. https://doi.org/10.1002/gepi.22200

Author

Skotte, Line ; Jørsboe, Emil ; Korneliussen, Thorfinn S ; Moltke, Ida ; Albrechtsen, Anders. / Ancestry-specific association mapping in admixed populations. In: Genetic Epidemiology. 2019 ; Vol. 43, No. 5. pp. 506-521.

Bibtex

@article{0b70d0b871854732a288ef04f9b3c8f4,
title = "Ancestry-specific association mapping in admixed populations",
abstract = "During the last decade genome-wide association studies have proven to be a powerful approach to identifying disease-causing variants. However, for admixed populations, most current methods for association testing are based on the assumption that the effect of a genetic variant is the same regardless of its ancestry. This is a reasonable assumption for a causal variant but may not hold for the genetic variants that are tested in genome-wide association studies, which are usually not causal. The effects of noncausal genetic variants depend on how strongly their presence correlate with the presence of the causal variant, which may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies. Motivated by this, we here introduce a new statistical method for association testing in recently admixed populations, where the effect size is allowed to depend on the ancestry of a given allele. Our method does not rely on accurate inference of local ancestry, yet using simulations we show that in some scenarios it gives a substantial increase in statistical power to detect associations. In addition, the method allows for testing for difference in effect size between ancestral populations, which can be used to help determine if a given genetic variant is causal. We demonstrate the usefulness of the method on data from the Greenlandic population.",
author = "Line Skotte and Emil J{\o}rsboe and Korneliussen, {Thorfinn S} and Ida Moltke and Anders Albrechtsen",
note = "{\textcopyright} 2019 Wiley Periodicals, Inc.",
year = "2019",
doi = "10.1002/gepi.22200",
language = "English",
volume = "43",
pages = "506--521",
journal = "Genetic Epidemiology",
issn = "0741-0395",
publisher = "JohnWiley & Sons, Inc.",
number = "5",

}

RIS

TY - JOUR

T1 - Ancestry-specific association mapping in admixed populations

AU - Skotte, Line

AU - Jørsboe, Emil

AU - Korneliussen, Thorfinn S

AU - Moltke, Ida

AU - Albrechtsen, Anders

N1 - © 2019 Wiley Periodicals, Inc.

PY - 2019

Y1 - 2019

N2 - During the last decade genome-wide association studies have proven to be a powerful approach to identifying disease-causing variants. However, for admixed populations, most current methods for association testing are based on the assumption that the effect of a genetic variant is the same regardless of its ancestry. This is a reasonable assumption for a causal variant but may not hold for the genetic variants that are tested in genome-wide association studies, which are usually not causal. The effects of noncausal genetic variants depend on how strongly their presence correlate with the presence of the causal variant, which may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies. Motivated by this, we here introduce a new statistical method for association testing in recently admixed populations, where the effect size is allowed to depend on the ancestry of a given allele. Our method does not rely on accurate inference of local ancestry, yet using simulations we show that in some scenarios it gives a substantial increase in statistical power to detect associations. In addition, the method allows for testing for difference in effect size between ancestral populations, which can be used to help determine if a given genetic variant is causal. We demonstrate the usefulness of the method on data from the Greenlandic population.

AB - During the last decade genome-wide association studies have proven to be a powerful approach to identifying disease-causing variants. However, for admixed populations, most current methods for association testing are based on the assumption that the effect of a genetic variant is the same regardless of its ancestry. This is a reasonable assumption for a causal variant but may not hold for the genetic variants that are tested in genome-wide association studies, which are usually not causal. The effects of noncausal genetic variants depend on how strongly their presence correlate with the presence of the causal variant, which may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies. Motivated by this, we here introduce a new statistical method for association testing in recently admixed populations, where the effect size is allowed to depend on the ancestry of a given allele. Our method does not rely on accurate inference of local ancestry, yet using simulations we show that in some scenarios it gives a substantial increase in statistical power to detect associations. In addition, the method allows for testing for difference in effect size between ancestral populations, which can be used to help determine if a given genetic variant is causal. We demonstrate the usefulness of the method on data from the Greenlandic population.

U2 - 10.1002/gepi.22200

DO - 10.1002/gepi.22200

M3 - Journal article

C2 - 30883944

VL - 43

SP - 506

EP - 521

JO - Genetic Epidemiology

JF - Genetic Epidemiology

SN - 0741-0395

IS - 5

ER -

ID: 216920405