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AbstractGenome-wide association studies (GWAS) are not fully comprehensive as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implemented an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels, includes the analysis of the X chromosome and non-additive models to test for association. We applied this methodology to 62,281 subjects across 22 age-related diseases and identified 94 genome-wide associated loci, including 26 previously unreported. We observed that 27.6% of the 94 loci would be missed if we only used standard imputation strategies and only tested the additive model. Among the new findings, we identified three novel low-frequency recessive variants with odds ratios larger than 4, which would need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases.
:Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC], Aging, Genotype, Science, Genetic predisposition to disease, Diseases, Genome-wide association studies, Polymorphism, Single Nucleotide, Article, X chromosome, Cromosoma X, Gene Frequency, Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica, GWAS, Humans, Disease, Genetic Predisposition to Disease, Genome, Human, Q, Age Factors, Genomics, Genòmica, Phenotype, Haplotypes, Malalties, Genome-Wide Association Study
:Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC], Aging, Genotype, Science, Genetic predisposition to disease, Diseases, Genome-wide association studies, Polymorphism, Single Nucleotide, Article, X chromosome, Cromosoma X, Gene Frequency, Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica, GWAS, Humans, Disease, Genetic Predisposition to Disease, Genome, Human, Q, Age Factors, Genomics, Genòmica, Phenotype, Haplotypes, Malalties, Genome-Wide Association Study
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