Comparison of Methods to Account for Relatedness in Genome-Wide Association Studies with Family-Based Data
Publication Date
July 17, 2014
Journal
PLOS Genetics
Authors
Jakris Eu Ahsunthornwattana, E. Nancy Miller, Michaela Fakiola, Wellcome Trust Case Control Consortium 2, et al
Volume
10
Issue
7
Pages
e1004445
DOI
https://dx.plos.org/10.1371/journal.pgen.1004445
Publisher URL
http://journals.plos.org/plosgenetics/article?id=10.1371%2Fjournal.pgen.1004445
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25033443
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102448
Europe PMC
http://europepmc.org/abstract/MED/25033443
Web of Science
000339902600008
Scopus
84905455421
Mendeley
http://www.mendeley.com/research/comparison-methods-account-relatedness-genomewide-association-studies-familybased-data
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In the last few years, a bewildering variety of different LMM methods/software packages have been developed, but it is not always clear how (or indeed whether) any newly-proposed method differs from previously-proposed implementations. Here we compare the performance of several LMM approaches (and software implementations, including EMMAX, GenABEL, FaST-LMM, Mendel, GEMMA and MMM) via their application to a genome-wide association study of visceral leishmaniasis in 348 Brazilian families comprising 3626 individuals (1972 genotyped). The implementations differ in precise details of methodology implemented and through various user-chosen options such as the method and number of SNPs used to estimate the kinship (relatedness) matrix. We investigate sensitivity to these choices and the success (or otherwise) of the approaches in controlling the overall genome-wide error-rate for both real and simulated phenotypes. We compare the LMM results to those obtained using traditional family-based association tests (based on transmission of alleles within pedigrees) and to alternative approaches implemented in the software packages MQLS, ROADTRIPS and MASTOR. We find strong concordance between the results from different LMM approaches, and all are successful in controlling the genome-wide error rate (except for some approaches when applied naively to longitudinal data with many repeated measures). We also find high correlation between LMMs and alternative approaches (apart from transmission-based approaches when applied to SNPs with small or non-existent effects). We conclude that LMM approaches perform well in comparison to competing approaches. 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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1599513"], "description"=>"a<p>FBATaff, MQLS and ROADTRIPS are only applicable to binary traits and so do not have results in the ‘Simulated quantitative’ column.</p>b<p>In the simulated data sets, MQLS and RT could only be based on the 1972 individuals with simulated phenotypes, and so no simulated trait results are displayed in the MQLS3626 and RT3626 rows.</p>", "links"=>[], "tags"=>["genetics", "Genetics of disease", "Human genetics", "mathematics", "Statistics (mathematics)", "Biostatistics", "achieved", "replicate", "simulated"], "article_id"=>1109396, "categories"=>["Biological Sciences"], "users"=>["Jakris Eu-ahsunthornwattana", "E. Nancy Miller", "Michaela Fakiola", "Selma M. B. Jeronimo", "Jenefer M. Blackwell", "Heather J. Cordell"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004445.t002", "stats"=>{"downloads"=>5, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genomic_control_inflation_factors_achieved_in_real_data_or_in_a_single_replicate_of_the_simulated_data_sets_/1109396", "title"=>"Genomic control inflation factors achieved in real data or in a single replicate of the simulated data sets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-17 04:00:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/1599533", "https://ndownloader.figshare.com/files/1599534", "https://ndownloader.figshare.com/files/1599535", "https://ndownloader.figshare.com/files/1599536", "https://ndownloader.figshare.com/files/1599537", "https://ndownloader.figshare.com/files/1599538", "https://ndownloader.figshare.com/files/1599540", "https://ndownloader.figshare.com/files/1599541", "https://ndownloader.figshare.com/files/1599542", "https://ndownloader.figshare.com/files/1599543", "https://ndownloader.figshare.com/files/1599544", "https://ndownloader.figshare.com/files/1599545", "https://ndownloader.figshare.com/files/1599546", "https://ndownloader.figshare.com/files/1599547", "https://ndownloader.figshare.com/files/1599548"], "description"=>"<div><p>Approaches based on linear mixed models (LMMs) have recently gained popularity for modelling population substructure and relatedness in genome-wide association studies. In the last few years, a bewildering variety of different LMM methods/software packages have been developed, but it is not always clear how (or indeed whether) any newly-proposed method differs from previously-proposed implementations. Here we compare the performance of several LMM approaches (and software implementations, including EMMAX, GenABEL, FaST-LMM, Mendel, GEMMA and MMM) via their application to a genome-wide association study of visceral leishmaniasis in 348 Brazilian families comprising 3626 individuals (1972 genotyped). The implementations differ in precise details of methodology implemented and through various user-chosen options such as the method and number of SNPs used to estimate the kinship (relatedness) matrix. We investigate sensitivity to these choices and the success (or otherwise) of the approaches in controlling the overall genome-wide error-rate for both real and simulated phenotypes. We compare the LMM results to those obtained using traditional family-based association tests (based on transmission of alleles within pedigrees) and to alternative approaches implemented in the software packages MQLS, ROADTRIPS and MASTOR. We find strong concordance between the results from different LMM approaches, and all are successful in controlling the genome-wide error rate (except for some approaches when applied naively to longitudinal data with many repeated measures). We also find high correlation between LMMs and alternative approaches (apart from transmission-based approaches when applied to SNPs with small or non-existent effects). We conclude that LMM approaches perform well in comparison to competing approaches. Given their strong concordance, in most applications, the choice of precise LMM implementation cannot be based on power/type I error considerations but must instead be based on considerations such as speed and ease-of-use.</p></div>", "links"=>[], "tags"=>["genetics", "Genetics of disease", "Human genetics", "mathematics", "Statistics (mathematics)", "Biostatistics", "methods", "relatedness", "genome-wide", "studies", "family-based"], "article_id"=>1109410, "categories"=>["Biological Sciences"], "users"=>["Jakris Eu-ahsunthornwattana", "E. Nancy Miller", "Michaela Fakiola", "Selma M. B. Jeronimo", "Jenefer M. Blackwell", "Heather J. Cordell"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1004445.s001", "https://dx.doi.org/10.1371/journal.pgen.1004445.s002", "https://dx.doi.org/10.1371/journal.pgen.1004445.s003", "https://dx.doi.org/10.1371/journal.pgen.1004445.s004", "https://dx.doi.org/10.1371/journal.pgen.1004445.s005", "https://dx.doi.org/10.1371/journal.pgen.1004445.s006", "https://dx.doi.org/10.1371/journal.pgen.1004445.s007", "https://dx.doi.org/10.1371/journal.pgen.1004445.s008", "https://dx.doi.org/10.1371/journal.pgen.1004445.s009", "https://dx.doi.org/10.1371/journal.pgen.1004445.s010", "https://dx.doi.org/10.1371/journal.pgen.1004445.s011", "https://dx.doi.org/10.1371/journal.pgen.1004445.s012", "https://dx.doi.org/10.1371/journal.pgen.1004445.s013", "https://dx.doi.org/10.1371/journal.pgen.1004445.s014", "https://dx.doi.org/10.1371/journal.pgen.1004445.s015"], "stats"=>{"downloads"=>42, "page_views"=>88, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_Methods_to_Account_for_Relatedness_in_Genome_Wide_Association_Studies_with_Family_Based_Data_/1109410", "title"=>"Comparison of Methods to Account for Relatedness in Genome-Wide Association Studies with Family-Based Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-07-17 04:00:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/1599507"], "description"=>"<p>Powers (left hand plots) are defined as the proportion of replicates (out of 1000) in which both simulated disease loci are detected, with ‘detection’ corresponding to any SNP within 40 kb of the simulated disease locus reaching the specified <i>p</i>-value threshold. Type 1 errors (right hand plots) are defined as the proportion of null SNPs (out of 20,000 = 20 null SNPs times 1000 simulation replicates) that reach the specified <i>p</i>-value threshold. Horizontal dashed lines indicate the target <i>p</i>-value thresholds (i.e. the expected type 1 error rates).</p>", "links"=>[], "tags"=>["genetics", "Genetics of disease", "Human genetics", "mathematics", "Statistics (mathematics)", "Biostatistics"], "article_id"=>1109390, "categories"=>["Biological Sciences"], "users"=>["Jakris Eu-ahsunthornwattana", "E. Nancy Miller", "Michaela Fakiola", "Selma M. B. Jeronimo", "Jenefer M. Blackwell", "Heather J. Cordell"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004445.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Power_and_type_1_error_of_different_methods_/1109390", "title"=>"Power and type 1 error of different methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-17 04:00:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/1599503"], "description"=>"<p>Plots above the diagonal show a comparison of kinship measures, with correlations between the kinship measures indicated below the diagonal. EM_BN = EMMAX (Balding-Nichols), EM_IBS = EMMAX (IBS method), FLMM_C = FaST-LMM using covariance matrix, FLMM_R = FaST-LMM using realised relationship matrix, GA = GenABEL, GMA_C = GEMMA using centred genotypes, GMA_S = GEMMA using standardised genotypes, KING_H = KING with homogeneous population assumption, KING_R = KING with robust estimation.</p>", "links"=>[], "tags"=>["genetics", "Genetics of disease", "Human genetics", "mathematics", "Statistics (mathematics)", "Biostatistics", "kinship", "estimates"], "article_id"=>1109386, "categories"=>["Biological Sciences"], "users"=>["Jakris Eu-ahsunthornwattana", "E. Nancy Miller", "Michaela Fakiola", "Selma M. B. Jeronimo", "Jenefer M. Blackwell", "Heather J. Cordell"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004445.g001", "stats"=>{"downloads"=>2, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_kinship_estimates_pruned_SNPs_using_different_software_packages_/1109386", "title"=>"Comparison of kinship estimates (pruned SNPs) using different software packages.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-17 04:00:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/1599515"], "description"=>"<p>Summary of methods/software packages investigated.</p>", "links"=>[], "tags"=>["genetics", "Genetics of disease", "Human genetics", "mathematics", "Statistics (mathematics)", "Biostatistics", "packages"], "article_id"=>1109398, "categories"=>["Biological Sciences"], "users"=>["Jakris Eu-ahsunthornwattana", "E. Nancy Miller", "Michaela Fakiola", "Selma M. B. Jeronimo", "Jenefer M. Blackwell", "Heather J. Cordell"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004445.t001", "stats"=>{"downloads"=>2, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_methods_software_packages_investigated_/1109398", "title"=>"Summary of methods/software packages investigated.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-17 04:00:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/1599512"], "description"=>"a<p>See <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004445#pgen-1004445-t002\" target=\"_blank\">Table 2</a> for description of methods.</p>", "links"=>[], "tags"=>["genetics", "Genetics of disease", "Human genetics", "mathematics", "Statistics (mathematics)", "Biostatistics", "achieved", "naive", "replicate", "simulated", "longitudinal"], "article_id"=>1109395, "categories"=>["Biological Sciences"], "users"=>["Jakris Eu-ahsunthornwattana", "E. Nancy Miller", "Michaela Fakiola", "Selma M. B. Jeronimo", "Jenefer M. Blackwell", "Heather J. Cordell"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004445.t004", "stats"=>{"downloads"=>5, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genomic_control_factors_achieved_in_naive_analysis_of_a_single_replicate_of_the_simulated_longitudinal_data_sets_/1109395", "title"=>"Genomic control factors achieved in naive analysis of a single replicate of the simulated longitudinal data sets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-17 04:00:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/1599511"], "description"=>"<p>The points marked in red denote the confirmed significant region from Fakiola et al. (2013). FLMM_E = FaST-LMM using exact calculation, MQLS1972 = MQLS using 1972 genotyped individuals, RT1972 = ROADTRIPS using 1972 genotyped individuals, FBATaff = FBAT using transmissions to affecteds only, FBATboth = FBAT using transmissions to both affecteds and unaffecteds. Results from all other LMM methods were indistinguishable from FLMM_E and so are not shown.</p>", "links"=>[], "tags"=>["genetics", "Genetics of disease", "Human genetics", "mathematics", "Statistics (mathematics)", "Biostatistics", "phenotype", "fast-lmm"], "article_id"=>1109394, "categories"=>["Biological Sciences"], "users"=>["Jakris Eu-ahsunthornwattana", "E. Nancy Miller", "Michaela Fakiola", "Selma M. B. Jeronimo", "Jenefer M. Blackwell", "Heather J. Cordell"], "doi"=>"https://dx.doi.org/10.1371/journal.pgen.1004445.g004", "stats"=>{"downloads"=>5, "page_views"=>34, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Manhattan_plots_for_the_real_phenotype_using_FaST_LMM_exact_and_alternative_software_packages_/1109394", "title"=>"Manhattan plots for the real phenotype using FaST-LMM exact and alternative software packages.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-17 04:00:15"}

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Relative Metric

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