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Local Genealogies in a Linear Mixed Model for Genome-wide Association Mapping in Complex Pedigreed Populations

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Local Genealogies in a Linear Mixed Model for Genome-wide Association Mapping in Complex Pedigreed Populations. / Sahana, Goutam; Mailund, Thomas; Lund, Mogens Sandø; Guldbrandtsen, Bernt.

I: P L o S One, Bind 6, Nr. 11, 11.2011.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{faa9fff12cff46d08b00283765ff6f67,
title = "Local Genealogies in a Linear Mixed Model for Genome-wide Association Mapping in Complex Pedigreed Populations",
abstract = "Introduction: The state-of-the-art for dealing with multiple levels of relationship among the samples in genome-wide association studies (GWAS) is unified mixed model analysis (MMA). This approach is very flexible, can be applied to bothfamily-based and population-based samples, and can be extended to incorporate other effects in a straightforward and rigorous fashion. Here, we present a complementary approach, called {\textquoteleft}GENMIX (genealogy based mixed model){\textquoteright} which combines advantages from two powerful GWAS methods: genealogy-based haplotype grouping and MMA.Subjects and Methods: We validated GENMIX using genotyping data of Danish Jersey cattle and simulated phenotype and compared to the MMA. We simulated scenarios for three levels of heritability (0.21, 0.34, and 0.64), seven levels of MAF (0.05, 0.10, 0.15, 0.20, 0.25, 0.35, and 0.45) and five levels of QTL effect (0.1, 0.2, 0.5, 0.7 and 1.0 in phenotypic standard deviation unit). Each of these 105 possible combinations (3 h2 x 7 MAF x 5 effects) of scenarios was replicated 25 times.Results: GENMIX provides a better ranking of markers close to the causative locus{\textquoteright} location. GENMIX outperformed MMA when the QTL effect was small and the MAF at the QTL was low. In scenarios where MAF was high or the QTL affecting the trait had a large effect both GENMIX and MMA performed similarly.Conclusion: In discovery studies, where high-ranking markers are identified and later examined in validation studies, we therefore expect GENMIX to enrich candidates brought to follow-up studies with true positives over false positives more than the MMA would.",
author = "Goutam Sahana and Thomas Mailund and Lund, {Mogens Sand{\o}} and Bernt Guldbrandtsen",
year = "2011",
month = nov,
language = "English",
volume = "6",
journal = "P L o S One",
issn = "1932-6203",
publisher = "public library of science",
number = "11",

}

RIS

TY - JOUR

T1 - Local Genealogies in a Linear Mixed Model for Genome-wide Association Mapping in Complex Pedigreed Populations

AU - Sahana, Goutam

AU - Mailund, Thomas

AU - Lund, Mogens Sandø

AU - Guldbrandtsen, Bernt

PY - 2011/11

Y1 - 2011/11

N2 - Introduction: The state-of-the-art for dealing with multiple levels of relationship among the samples in genome-wide association studies (GWAS) is unified mixed model analysis (MMA). This approach is very flexible, can be applied to bothfamily-based and population-based samples, and can be extended to incorporate other effects in a straightforward and rigorous fashion. Here, we present a complementary approach, called ‘GENMIX (genealogy based mixed model)’ which combines advantages from two powerful GWAS methods: genealogy-based haplotype grouping and MMA.Subjects and Methods: We validated GENMIX using genotyping data of Danish Jersey cattle and simulated phenotype and compared to the MMA. We simulated scenarios for three levels of heritability (0.21, 0.34, and 0.64), seven levels of MAF (0.05, 0.10, 0.15, 0.20, 0.25, 0.35, and 0.45) and five levels of QTL effect (0.1, 0.2, 0.5, 0.7 and 1.0 in phenotypic standard deviation unit). Each of these 105 possible combinations (3 h2 x 7 MAF x 5 effects) of scenarios was replicated 25 times.Results: GENMIX provides a better ranking of markers close to the causative locus’ location. GENMIX outperformed MMA when the QTL effect was small and the MAF at the QTL was low. In scenarios where MAF was high or the QTL affecting the trait had a large effect both GENMIX and MMA performed similarly.Conclusion: In discovery studies, where high-ranking markers are identified and later examined in validation studies, we therefore expect GENMIX to enrich candidates brought to follow-up studies with true positives over false positives more than the MMA would.

AB - Introduction: The state-of-the-art for dealing with multiple levels of relationship among the samples in genome-wide association studies (GWAS) is unified mixed model analysis (MMA). This approach is very flexible, can be applied to bothfamily-based and population-based samples, and can be extended to incorporate other effects in a straightforward and rigorous fashion. Here, we present a complementary approach, called ‘GENMIX (genealogy based mixed model)’ which combines advantages from two powerful GWAS methods: genealogy-based haplotype grouping and MMA.Subjects and Methods: We validated GENMIX using genotyping data of Danish Jersey cattle and simulated phenotype and compared to the MMA. We simulated scenarios for three levels of heritability (0.21, 0.34, and 0.64), seven levels of MAF (0.05, 0.10, 0.15, 0.20, 0.25, 0.35, and 0.45) and five levels of QTL effect (0.1, 0.2, 0.5, 0.7 and 1.0 in phenotypic standard deviation unit). Each of these 105 possible combinations (3 h2 x 7 MAF x 5 effects) of scenarios was replicated 25 times.Results: GENMIX provides a better ranking of markers close to the causative locus’ location. GENMIX outperformed MMA when the QTL effect was small and the MAF at the QTL was low. In scenarios where MAF was high or the QTL affecting the trait had a large effect both GENMIX and MMA performed similarly.Conclusion: In discovery studies, where high-ranking markers are identified and later examined in validation studies, we therefore expect GENMIX to enrich candidates brought to follow-up studies with true positives over false positives more than the MMA would.

M3 - Journal article

VL - 6

JO - P L o S One

JF - P L o S One

SN - 1932-6203

IS - 11

ER -