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A comparison of association statistics between pooled and individual genotypes

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A comparison of association statistics between pooled and individual genotypes. / Knight, Jo; Saccone, Scott F.; Zhang, Zhehao et al.
In: Human Heredity, Vol. 67, No. 4, 03.2009, p. 219-225.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Knight, J, Saccone, SF, Zhang, Z, Ballinger, DG & Rice, JP 2009, 'A comparison of association statistics between pooled and individual genotypes', Human Heredity, vol. 67, no. 4, pp. 219-225. https://doi.org/10.1159/000194975

APA

Knight, J., Saccone, S. F., Zhang, Z., Ballinger, D. G., & Rice, J. P. (2009). A comparison of association statistics between pooled and individual genotypes. Human Heredity, 67(4), 219-225. https://doi.org/10.1159/000194975

Vancouver

Knight J, Saccone SF, Zhang Z, Ballinger DG, Rice JP. A comparison of association statistics between pooled and individual genotypes. Human Heredity. 2009 Mar;67(4):219-225. Epub 2009 Jan 27. doi: 10.1159/000194975

Author

Knight, Jo ; Saccone, Scott F. ; Zhang, Zhehao et al. / A comparison of association statistics between pooled and individual genotypes. In: Human Heredity. 2009 ; Vol. 67, No. 4. pp. 219-225.

Bibtex

@article{4f24bbec657e4586acc78cd0c7217da7,
title = "A comparison of association statistics between pooled and individual genotypes",
abstract = "BACKGROUND: Markers for individual genotyping can be selected using quantitative genotyping of pooled DNA. This strategy saves time and money.METHODS: To determine the efficacy of this approach, we investigated the bivariate distribution of association test statistics from pooled and individual genotypes. We used a sample of approximately 1,000 samples with individual and pooled genotyping on 40,000 SNPs.RESULTS AND CONCLUSIONS: We found that the distribution of the joint test statistics can be modelled as a mixture of two bivariate normal distributions. One distribution has a correlation of zero, and is probably due to SNPs whose pooled genotyping was unsuccessful. The other distribution has a correlation of approximately 0.65 in our data. This latter distribution is probably accounted for by SNPs whose pooled genotyping accurately predicts the underlying allele frequency. Approximately 87% of the data belongs to this distribution. We also derived a method to investigate the effect of both the correlation and selection cut-off on the relative power of pooling studies. We demonstrate that pooled genotyping has good power to detect SNPs that are truly associated with disease-causing variants for SNPs showing good correlation between pooled and individual genotyping. Therefore, this approach is a cost effective tool for association studies.",
keywords = "Computational Biology, Gene Frequency, Genome, Human, Genotype, Humans, Models, Statistical, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide",
author = "Jo Knight and Saccone, {Scott F.} and Zhehao Zhang and Ballinger, {Dennis G.} and Rice, {John P.}",
year = "2009",
month = mar,
doi = "10.1159/000194975",
language = "English",
volume = "67",
pages = "219--225",
journal = "Human Heredity",
issn = "0001-5652",
publisher = "S. Karger AG",
number = "4",

}

RIS

TY - JOUR

T1 - A comparison of association statistics between pooled and individual genotypes

AU - Knight, Jo

AU - Saccone, Scott F.

AU - Zhang, Zhehao

AU - Ballinger, Dennis G.

AU - Rice, John P.

PY - 2009/3

Y1 - 2009/3

N2 - BACKGROUND: Markers for individual genotyping can be selected using quantitative genotyping of pooled DNA. This strategy saves time and money.METHODS: To determine the efficacy of this approach, we investigated the bivariate distribution of association test statistics from pooled and individual genotypes. We used a sample of approximately 1,000 samples with individual and pooled genotyping on 40,000 SNPs.RESULTS AND CONCLUSIONS: We found that the distribution of the joint test statistics can be modelled as a mixture of two bivariate normal distributions. One distribution has a correlation of zero, and is probably due to SNPs whose pooled genotyping was unsuccessful. The other distribution has a correlation of approximately 0.65 in our data. This latter distribution is probably accounted for by SNPs whose pooled genotyping accurately predicts the underlying allele frequency. Approximately 87% of the data belongs to this distribution. We also derived a method to investigate the effect of both the correlation and selection cut-off on the relative power of pooling studies. We demonstrate that pooled genotyping has good power to detect SNPs that are truly associated with disease-causing variants for SNPs showing good correlation between pooled and individual genotyping. Therefore, this approach is a cost effective tool for association studies.

AB - BACKGROUND: Markers for individual genotyping can be selected using quantitative genotyping of pooled DNA. This strategy saves time and money.METHODS: To determine the efficacy of this approach, we investigated the bivariate distribution of association test statistics from pooled and individual genotypes. We used a sample of approximately 1,000 samples with individual and pooled genotyping on 40,000 SNPs.RESULTS AND CONCLUSIONS: We found that the distribution of the joint test statistics can be modelled as a mixture of two bivariate normal distributions. One distribution has a correlation of zero, and is probably due to SNPs whose pooled genotyping was unsuccessful. The other distribution has a correlation of approximately 0.65 in our data. This latter distribution is probably accounted for by SNPs whose pooled genotyping accurately predicts the underlying allele frequency. Approximately 87% of the data belongs to this distribution. We also derived a method to investigate the effect of both the correlation and selection cut-off on the relative power of pooling studies. We demonstrate that pooled genotyping has good power to detect SNPs that are truly associated with disease-causing variants for SNPs showing good correlation between pooled and individual genotyping. Therefore, this approach is a cost effective tool for association studies.

KW - Computational Biology

KW - Gene Frequency

KW - Genome, Human

KW - Genotype

KW - Humans

KW - Models, Statistical

KW - Oligonucleotide Array Sequence Analysis

KW - Polymorphism, Single Nucleotide

U2 - 10.1159/000194975

DO - 10.1159/000194975

M3 - Journal article

C2 - 19172081

VL - 67

SP - 219

EP - 225

JO - Human Heredity

JF - Human Heredity

SN - 0001-5652

IS - 4

ER -