Ancestral haplotype-based association mapping with generalized linear mixed models accounting for stratification.
BACKGROUND
In many situations, genome-wide association studies are performed in populations presenting stratification. Mixed models including a kinship matrix accounting for genetic relatedness among individuals have been shown to correct for population and/or family structure. Here we extend this methodology to generalized linear mixed models which properly model data under various distributions. In addition we perform association with ancestral haplotypes inferred using a hidden Markov model.


RESULTS
The method was shown to properly account for stratification under various simulated scenari presenting population and/or family structure. Use of ancestral haplotypes resulted in higher power than SNPs on simulated datasets. Application to real data demonstrates the usefulness of the developed model. Full analysis of a dataset with 4600 individuals and 500 000 SNPs was performed in 2 h 36 min and required 2.28 Gb of RAM.


BACKGROUND
The software GLASCOW can be freely downloaded from www.giga.ulg.ac.be/jcms/prod_381171/software.


BACKGROUND
francois.guillaume@jouy.inra.fr


BACKGROUND
Supplementary data are available at Bioinformatics online.
Unit of Animal Genomics, GIGA-R, University of Liège, Liège, Belgium.
Z
Z
Zhang
F
F
Guillaume
A
A
Sartelet
C
C
Charlier
M
M
Georges
F
F
Farnir
T
T
Druet
D000818:Animals; D002417:Cattle; D002874:Chromosome Mapping / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006239:Haplotypes; D016014:Linear Models; D008297:Male; D008390:Markov Chains; D020641:Polymorphism, Single Nucleotide; D012984:Software