CY Guo

Eden R Martin

ER Martin

F Dudbridge

Michael A Schmidt

MP Epstein

R Anney

Ren-Hua Chung

RH Chung

S Purcell

TH Hamza

TL Edwards

Wellcome Trust Case Control Consortium

X Zhu

BMC Bioinformatics

English

PubMed

Springer - Publisher Connector

CAPL: an efficient association software package using family and case-control data and accounting for population stratification

Crossref

SOFTWARE Open AccessCAPL: an efficient association software packageusing family and case-control data andaccounting for population stratificationRen-Hua Chung*, Michael A Schmidt and Eden R MartinAbstractBackground: With many genome-wide association study (GWAS) datasets available, it is critical that we havestatistical tools that are both flexible to accommodate different study designs and fast. We recently proposed thecombined APL (CAPL) method, which can use family and case-control datasets and can account for populationstratification in the data. Because computationally intensive algorithms are used in CAPL, implementing CAPL withefficient parallel algorithms is essential.Results: We used a hybrid of open message passing interface (open MPI) and POSIX threads to parallelize CAPL,which enable the program to operate in a cluster environment. We used simulations to demonstrate that theparallel implementation of CAPL can analyze a large GWAS dataset in a reasonable time frame when a parallelcomputing resource is available.Conclusions: As many GWAS datasets based on both family and case-control designs are available, a flexible andefficient tool such as CAPL will be very helpful to combine the datasets to greatly increase statistical power andfinish the analysis in a reasonable time frame.BackgroundFamily-based and case-control association designs havebeen used in many genome-wide association studies(GWAS). For GWAS where ~1 million markers aretested, the major challenge is sorting out true positivesfrom the many false positives. Many GWAS datasetshave been deposited into public databases such as thedatabase of Genotypes and Phenotypes (dbGaP). Alsothe Welcome Trust Case Control Consortium(WTCCC) provides a large number of case-control sam-ples for public analysis [1]. These resources provide thecrucial opportunity to increase power by combiningdatasets. However, this requires flexible analytic meth-ods that can accommodate diverse study designs (e.g.,family and case-control).Current available software for combining case-controland family data all have restrictions. Most of them suchas SCOUT [2], CHRR [3] and UNPHASED [4] requiresampling a homogeneous population, which may not bea reasonable assumption for data from a large consor-tium. FamCC [5] can account for population stratifica-tion and uses nuclear families with arbitrary number ofsiblings but requires parental genotype data, which areoften unavailable for late-onset diseases. To overcomethese restrictions, we have developed the Combined APLtest (CAPL) [6], which is a novel and powerful statisticaltest that can accommodate family and case-control data-sets and can account for population stratification using aclustering algorithm.CAPL is an extension of the family-based Associationin the Presence of Linkage (APL) test [7], which com-pares the difference between the observed number ofalleles in affected siblings and its expected value, condi-tional on parental genotypes, under the null hypothesisof no linkage or no association. CAPL can use nuclearfamilies with one or more affected sibs and can infermissing parental genotypes properly in the presence oflinkage by accounting for the identity-by-descent (IBD)parameters. Unrelated cases and controls in CAPL aretreated as families with one sibling and two missing* Correspondence: rchung@med.miami.eduCenter for Genetic Epidemiology and Statistical Genetics, John P. HussmanInstitute for Human Genomics, University of Miami Miller School of Medicine,Miami, FL, USAChung et al. BMC Bioinformatics 2011, 12:201http://www.biomedcentral.com/1471-2105/12/201© 2011 Chung et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.parents so that they can be integrated into the family-based framework. Ward’s clustering algorithm is used inCAPL to identify subpopulations and parental mating-type probabilities are calculated conditional on the sub-population information. The EM algorithm is used toestimate the allele frequencies, IBD parameters andprobabilities of origin in the presence of population sub-structure. A bootstrap approach is used in CAPL to esti-mate the variance for the CAPL statistic [8]. For eachbootstrap replicate, samples are resampled with replace-ment and the EM algorithm is performed. The cluster-ing algorithm is also included in the bootstrapprocedure to account for the variation from clustering.CAPL has been shown to have correct type I error ratesand has more power than other association tests thatcombine case-control and family data such asUNPHASED, SCOUT, CHRR and FAMCC under var-ious simulation scenarios [6].Generally 20-40 EM iterations are required for theparameter estimates to converge, and 200-1000 boot-strap replicates are performed in CAPL for the varianceestimate. For each bootstrap replicate, the EM algorithmis performed. Therefore, the CAPL algorithm is verycomputationally intensive and can be inefficient for ana-lyzing GWAS data. The same is also true for other asso-ciation methods that infer missing parental mating typesbased on sample allele frequencies such as UNPHASED,which relies on the quasi-Newton algorithm for maxi-mum likelihood estimates [4]. However, because eachmarker in CAPL is analyzed independently, analysis ofeach marker can potentially be parallelized to reducethe run time.We implemented CAPL using the POSIX threads(pthreads) and open message passing interface (openMPI) libraries that can be executed in a computer clus-ter environment. We used computer simulations todemonstrate that CAPL can analyze GWAS datasetswithin a reasonable amount of time. The CAPL softwarepackage will be a useful tool to combine existing familyand case-control GWAS datasets in the presence ofpopulation stratification.ImplementationWe used a hybrid of open MPI and pthreads to paralle-lize CAPL. In a cluster environment, CAPL is first exe-cuted on one node, which we denote as the masternode, that manages the I/O for reading marker andindividual information and writing results. The firststage in the CAPL algorithm is to perform the clusteringprocedure for population stratification, which requires adistance matrix. Calculating the distance matrix is timeconsuming because the distance is calculated for eachpair of individuals over genome-wide markers. We usedpthreads to parallelize the calculations on the masternode. Depending on the number of computing nodesspecified by the user, genotypes for markers along withthe population substructure information are then dis-tributed evenly to each node via MPI. On each nodemarkers are analyzed independently and in parallelusing pthreads with shared memory. The advantage ofusing pthreads instead of MPI on each node is thatpthreads communicate via shared memory which is sig-nificantly faster than MPI that has to communicate viathe network. Figure 1 shows the flowchart for the paral-lel CAPL algorithm. For a standalone machine withmultiple cores, we also implemented a version of CAPLwith pthreads only.To evaluate the performance of CAPL, we used geno-meSIMLA [9] to simulate GWAS datasets. We down-loaded the configuration file for simulating GWAS databased on Affymetrix 550 k chip from the genomeSIMLAwebsite. We simulated two scenarios. For the first sce-nario, we simulated one population based on the 600thgeneration from genomeSIMLA. A total of 2000 cases,2000 controls and 1500 families were simulated, whichare similar to the sample sizes of recent GWAS studies[10,11]. A total of 750 families are triads and 750families are multiplex (parents and two affected sib-lings). For the second scenario, we simulated 1000 cases,1000 controls and 750 trio families from one population(based on the 600th generation in genomeSIMLA) and1000 cases, 1000 controls and 750 multiplex familiesfrom another population (based on the 750th genera-tion). The jobs were distributed across 6 nodes, eachwith 8 Xeon 2.6 GHz cores and 16 GB of memory. Wealso performed the jobs on 50 nodes, each with 8 Xeon2.6 GHz cores and 16 GB of memory.Figure 1 The flowchart for the parallel CAPL algorithm.Chung et al. BMC Bioinformatics 2011, 12:201http://www.biomedcentral.com/1471-2105/12/201Page 2 of 4We then evaluated the performance of CAPL on a sin-gle node for the two scenarios. Because the simulateddatasets are large, we compiled a demonstration versionof CAPL by fixing the number of bootstrap iterations tobe 50 and the number of EM iterations to be 15 toreduce the run time. This demonstration version is forcomparing performance only, as reducing the numbersof bootstrap and EM iterations may cause biased esti-mates of parameters.ResultsThe run time for scenario 1 was 2 days 2 hours and 43minutes and the run time for scenario 2 was 3 days 6hours and 11 minutes using 6 nodes (48 parallelthreads). The run time for scenario 1 was 7 hours and18 minutes and the run time for scenario 2 was 18hours and 14 minutes using 50 nodes (400 parallelthreads). Scenario 2 had longer run time since moreparameters were estimated for population substructureanalysis [6]. We can see that in this example consistingof 9250 samples and half million markers, CAPL cancomplete analysis in a reasonable time frame with theestimates of parental mating types, IBD and populationsubstructure parameters when sufficient parallel com-puting resource is available.For the demonstration version of CAPL (withrestricted 50 bootstrap and 15 EM iterations) using 1node (8 parallel threads), the run time for scenario 1was 1 day 7 hours and 8 minutes and the run time forscenario 2 was 1 day 19 hours and 45 minutes. Withoutthe restrictions on the bootstrap and EM iterations,CAPL averaged about 250 bootstrap iterations andabout 40 EM iterations across markers in the simulatedsamples. Therefore, the total iterations (250 bootstrap ×40 EM) required for CAPL to have unbiased estimatesof parameters is about 13.3 times the number ofrestricted iterations (50 bootstrap × 15 EM). In practice,we expect that CAPL using 1 node would require morethan 17 days for similar sample size with scenario 1 andmore than 24 days for scenario 2. The results demon-strate the importance of implementing CAPL with MPIand pthreads in a distributed system.ConclusionsCAPL is implemented with a hybrid of open MPI andpthreads, which can be performed using a computercluster with shared memory. We also provide a versionof CAPL with pthreads only, so that the program can beperformed on a standalone machine with pthreads.Without the parallelization, CAPL may require monthsto complete a GWAS analysis using one processor. Thesituation is the same for other computationally intensiveassociation software such as UNPHASED. To speed ana-lysis, users may divide the input file into subsets ofmarkers and manually run the subsets of markers ondifferent machines. This is not ideal because extra sto-rage is needed for the subsets of files. Moreover, redu-cing the number of markers in a subset may cause theloss of information about population substructure forthe clustering algorithm in CAPL. Therefore, the paral-lelization for CAPL is essential for GWAS.In conclusion, we developed the efficient softwarepackage CAPL based on open MPI and pthreads, whichis a powerful association test that can accommodatecase-control and family data and account for populationstratification. As many GWAS datasets based on bothfamily and case-control designs are available, a flexibleand efficient tool such as CAPL will be very helpful tocombine the datasets to greatly increase statistical powerand finish the analysis in a reasonable time frame.Moreover, population stratification is properlyaccounted for in CAPL so that datasets from differentpopulations can be jointly analyzed.Software ConfigurationWe compiled the CAPL code for the standalone version,which can be executed on a single machine with multi-ple cores, and the cluster version, which can be exe-cuted in a cluster environment. We distribute binariesfor the standalone version (for Windows, Mac OS, andLinux) and a binary for the cluster version (for Linux).When executing the Windows binary, the pthreadslibrary (pthreadVC2.dll) needs to be in the same direc-tory as the binary. For Linux and Mac OS users, westrongly encourage the users to compile the code ontheir machines, as the configurations of dynamic linksto libraries may vary on different machines.In addition to binaries, we provide source code withsets of makefiles for the standalone and cluster versions.The standalone version can be compiled with GNU’s g++ 4.1.2 compiler or later versions. The cluster versioncan be compiled with mpiCC. We also provide makefilesbased on Intel compilers for Intel hardware users. Basedon our experiments, CAPL has better performance onthe Intel hardware when it was compiled with the Intelcompiler. We provide examples of the submissionscripts for the cluster based on the commonly used LSFand MOAB job schedulers. Users may need to workwith their cluster administrators to set up the para-meters in the submission scripts properly. For example,to achieve the optimal performance for CAPL, for eachcomputing node, the whole node needs to be reservedfor the job and the number of threads to run the job oneach node needs to be specified correctly in the submis-sion script.A control file is also required for the user to runCAPL. The user needs to specify the input file formatand path. Currently the PLINK [12] binary file (bim,Chung et al. BMC Bioinformatics 2011, 12:201http://www.biomedcentral.com/1471-2105/12/201Page 3 of 4bed and fam files) or the text file (map and ped files)formats are accepted by CAPL. Other important para-meters in the control file are the number of threads,number of nodes, number of subpopulations for theclustering algorithm, and the name of the result file.Note that the numbers of threads and nodes need to bethe same as the parameters specified in the submissionscript for the cluster. More details about the configura-tions of the CAPL software can be found in the softwaredocumentation.Availability and requirementsProject name: CAPLProject home page: http://www.mihg.org/software_-download/download_reg.php?software=CAPLOperating system(s): Linux (both standalone andcluster versions), Windows (standalone version), andMac OS (standalone version).Programming language: C++License: GNU GPLAny restrictions to use by non-academics: NoneDownload statistics: 16 unique outside users sincethe launch of the CAPL download siteAcknowledgements and FundingWe thank the Center for Computational Science (CCS) at University of Miamifor providing the computer cluster, which made our intensive simulationspossible. This work was supported by a University of Miami Stanley J. GlaserAward and a grant from the Alzheimer Association (NIRG-09-132667).Authors’ contributionsAll co-authors contributed to writing the manuscript. RHC was the primaryauthor on the manuscript, developed the statistical methods and the designfor their implementation. MAS contributed to the implementation of thesoftware. ERM provided input to study design and interpretation of data. Allauthors read and approved the final manuscript.Received: 22 December 2010 Accepted: 25 May 2011Published: 25 May 2011References1. Wellcome Trust Case Control Consortium: Genome-wide association studyof 14,000 cases of seven common diseases and 3,000 shared controls.Nature 2007, 447(7145):661-678.2. Epstein MP, Veal CD, Trembath RC, Barker JN, Li C, Satten GA: Geneticassociation analysis using data from triads and unrelated subjects. Am JHum Genet 2005, 76:592-608.3. Guo CY, Lunetta KL, Destefano AL, Cupples LA: Combined haplotyperelative risk (CHRR): a general and simple genetic association test thatcombines trios and unrelated case-controls. Genet Epidemiol 2008,33(1):54-62.4. Dudbridge F: Likelihood-based association analysis for nuclear familiesand unrelated subjects with missing genotype data. Hum Hered 2008,66(2):87-98.5. Zhu X, Li S, Cooper RS, Elston RC: A unified association analysis approachfor family and unrelated samples correcting for stratification. Am J HumGenet 2008, 82(2):352-365.6. Chung RH, Schmidt MA, Morris RW, Martin ER: CAPL: A Novel AssociationTest Using Case-Control and Family Data and Accounting for PopulationStratification. Genetic Epidemiology 2010, 34(7):747-755.7. Martin ER, Bass MP, Hauser ER, Kaplan NL: Accounting for linkage infamily-based tests of association with missing parental genotypes. Am JHum Genet 2003, 73:1016-1026.8. Chung RH, Hauser ER, Martin ER: The APL test: extension to generalnuclear families and haplotypes and examination of its robustness. HumHered 2006, 61(4):189-199.9. Edwards TL, Bush WS, Turner SD, Dudek SM, Torstenson ES, Schmidt M,Martin E, Ritchie MD: Generating Linkage Disequilibrium Patterns in DataSimulations using genomeSIMLA. Lect Notes Comput Sci 2008,4973(2008):24-35.10. Hamza TH, Zabetian CP, Tenesa A, Laederach A, Montimurro J, Yearout D,Kay DM, Doheny KF, Paschall J, Pugh E, Kusel VI, Collura R, Roberts J,Griffith A, Samii A, Scott WK, Nutt J, Factor SA, Payami H: Common geneticvariation in the HLA region is associated with late-onset sporadicParkinson’s disease. Nat Genet 2010, 42(9):781-785.11. Anney R, et al: A genome-wide scan for common alleles affecting risk forautism. Hum Mol Genet 2010, 19(20):4072-4082.12. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J,Sklar P, de Bakker PI, Daly MJ, Sham PC: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J HumGenet 2007, 81(3):559-575.doi:10.1186/1471-2105-12-201Cite this article as: Chung et al.: CAPL: an efficient association softwarepackage using family and case-control data and accounting forpopulation stratification. BMC Bioinformatics 2011 12:201.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitChung et al. BMC Bioinformatics 2011, 12:201http://www.biomedcentral.com/1471-2105/12/201Page 4 of 4

SOFTWARE Open Access
CAPL: an efficient association software package
using family and case-control data and
accounting for population stratification
Ren-Hua Chung*, Michael A Schmidt and Eden R Martin
Abstract
Background: With many genome-wide association study (GWAS) datasets available, it is critical that we have
statistical tools that are both flexible to accommodate different study designs and fast. We recently proposed the
combined APL (CAPL) method, which can use family and case-control datasets and can account for population
stratification in the data. Because computationally intensive algorithms are used in CAPL, implementing CAPL with
efficient parallel algorithms is essential.
Results: We used a hybrid of open message passing interface (open MPI) and POSIX threads to parallelize CAPL,
which enable the program to operate in a cluster environment. We used simulations to demonstrate that the
parallel implementation of CAPL can analyze a large GWAS dataset in a reasonable time frame when a parallel
computing resource is available.
Conclusions: As many GWAS datasets based on both family and case-control designs are available, a flexible and
efficient tool such as CAPL will be very helpful to combine the datasets to greatly increase statistical power and
finish the analysis in a reasonable time frame.
Background
Family-based and case-control association designs have
been used in many genome-wide association studies
(GWAS). For GWAS where ~1 million markers are
tested, the major challenge is sorting out true positives
from the many false positives. Many GWAS datasets
have been deposited into public databases such as the
database of Genotypes and Phenotypes (dbGaP). Also
the Welcome Trust Case Control Consortium
(WTCCC) provides a large number of case-control sam-
ples for public analysis [1]. These resources provide the
crucial opportunity to increase power by combining
datasets. However, this requires flexible analytic meth-
ods that can accommodate diverse study designs (e.g.,
family and case-control).
Current available software for combining case-control
and family data all have restrictions. Most of them such
as SCOUT [2], CHRR [3] and UNPHASED [4] require
sampling a homogeneous population, which may not be
a reasonable assumption for data from a large consor-
tium. FamCC [5] can account for population stratifica-
tion and uses nuclear families with arbitrary number of
siblings but requires parental genotype data, which are
often unavailable for late-onset diseases. To overcome
these restrictions, we have developed the Combined APL
test (CAPL) [6], which is a novel and powerful statistical
test that can accommodate family and case-control data-
sets and can account for population stratification using a
clustering algorithm.
CAPL is an extension of the family-based Association
in the Presence of Linkage (APL) test [7], which com-
pares the difference between the observed number of
alleles in affected siblings and its expected value, condi-
tional on parental genotypes, under the null hypothesis
of no linkage or no association. CAPL can use nuclear
families with one or more affected sibs and can infer
missing parental genotypes properly in the presence of
linkage by accounting for the identity-by-descent (IBD)
parameters. Unrelated cases and controls in CAPL are
treated as families with one sibling and two missing
* Correspondence: rchung@med.miami.edu
Center for Genetic Epidemiology and Statistical Genetics, John P. Hussman
Institute for Human Genomics, University of Miami Miller School of Medicine,
Miami, FL, USA
Chung et al. BMC Bioinformatics 2011, 12:201
http://www.biomedcentral.com/1471-2105/12/201
© 2011 Chung et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
parents so that they can be integrated into the family-
based framework. Ward’s clustering algorithm is used in
CAPL to identify subpopulations and parental mating-
type probabilities are calculated conditional on the sub-
population information. The EM algorithm is used to
estimate the allele frequencies, IBD parameters and
probabilities of origin in the presence of population sub-
structure. A bootstrap approach is used in CAPL to esti-
mate the variance for the CAPL statistic [8]. For each
bootstrap replicate, samples are resampled with replace-
ment and the EM algorithm is performed. The cluster-
ing algorithm is also included in the bootstrap
procedure to account for the variation from clustering.
CAPL has been shown to have correct type I error rates
and has more power than other association tests that
combine case-control and family data such as
UNPHASED, SCOUT, CHRR and FAMCC under var-
ious simulation scenarios [6].
Generally 20-40 EM iterations are required for the
parameter estimates to converge, and 200-1000 boot-
strap replicates are performed in CAPL for the variance
estimate. For each bootstrap replicate, the EM algorithm
is performed. Therefore, the CAPL algorithm is very
computationally intensive and can be inefficient for ana-
lyzing GWAS data. The same is also true for other asso-
ciation methods that infer missing parental mating types
based on sample allele frequencies such as UNPHASED,
which relies on the quasi-Newton algorithm for maxi-
mum likelihood estimates [4]. However, because each
marker in CAPL is analyzed independently, analysis of
each marker can potentially be parallelized to reduce
the run time.
We implemented CAPL using the POSIX threads
(pthreads) and open message passing interface (open
MPI) libraries that can be executed in a computer clus-
ter environment. We used computer simulations to
demonstrate that CAPL can analyze GWAS datasets
within a reasonable amount of time. The CAPL software
package will be a useful tool to combine existing family
and case-control GWAS datasets in the presence of
population stratification.
Implementation
We used a hybrid of open MPI and pthreads to paralle-
lize CAPL. In a cluster environment, CAPL is first exe-
cuted on one node, which we denote as the master
node, that manages the I/O for reading marker and
individual information and writing results. The first
stage in the CAPL algorithm is to perform the clustering
procedure for population stratification, which requires a
distance matrix. Calculating the distance matrix is time
consuming because the distance is calculated for each
pair of individuals over genome-wide markers. We used
pthreads to parallelize the calculations on the master
node. Depending on the number of computing nodes
specified by the user, genotypes for markers along with
the population substructure information are then dis-
tributed evenly to each node via MPI. On each node
markers are analyzed independently and in parallel
using pthreads with shared memory. The advantage of
using pthreads instead of MPI on each node is that
pthreads communicate via shared memory which is sig-
nificantly faster than MPI that has to communicate via
the network. Figure 1 shows the flowchart for the paral-
lel CAPL algorithm. For a standalone machine with
multiple cores, we also implemented a version of CAPL
with pthreads only.
To evaluate the performance of CAPL, we used geno-
meSIMLA [9] to simulate GWAS datasets. We down-
loaded the configuration file for simulating GWAS data
based on Affymetrix 550 k chip from the genomeSIMLA
website. We simulated two scenarios. For the first sce-
nario, we simulated one population based on the 600th
generation from genomeSIMLA. A total of 2000 cases,
2000 controls and 1500 families were simulated, which
are similar to the sample sizes of recent GWAS studies
[10,11]. A total of 750 families are triads and 750
families are multiplex (parents and two affected sib-
lings). For the second scenario, we simulated 1000 cases,
1000 controls and 750 trio families from one population
(based on the 600th generation in genomeSIMLA) and
1000 cases, 1000 controls and 750 multiplex families
from another population (based on the 750th genera-
tion). The jobs were distributed across 6 nodes, each
with 8 Xeon 2.6 GHz cores and 16 GB of memory. We
also performed the jobs on 50 nodes, each with 8 Xeon
2.6 GHz cores and 16 GB of memory.
Figure 1 The flowchart for the parallel CAPL algorithm.
Chung et al. BMC Bioinformatics 2011, 12:201
http://www.biomedcentral.com/1471-2105/12/201
Page 2 of 4
We then evaluated the performance of CAPL on a sin-
gle node for the two scenarios. Because the simulated
datasets are large, we compiled a demonstration version
of CAPL by fixing the number of bootstrap iterations to
be 50 and the number of EM iterations to be 15 to
reduce the run time. This demonstration version is for
comparing performance only, as reducing the numbers
of bootstrap and EM iterations may cause biased esti-
mates of parameters.
Results
The run time for scenario 1 was 2 days 2 hours and 43
minutes and the run time for scenario 2 was 3 days 6
hours and 11 minutes using 6 nodes (48 parallel
threads). The run time for scenario 1 was 7 hours and
18 minutes and the run time for scenario 2 was 18
hours and 14 minutes using 50 nodes (400 parallel
threads). Scenario 2 had longer run time since more
parameters were estimated for population substructure
analysis [6]. We can see that in this example consisting
of 9250 samples and half million markers, CAPL can
complete analysis in a reasonable time frame with the
estimates of parental mating types, IBD and population
substructure parameters when sufficient parallel com-
puting resource is available.
For the demonstration version of CAPL (with
restricted 50 bootstrap and 15 EM iterations) using 1
node (8 parallel threads), the run time for scenario 1
was 1 day 7 hours and 8 minutes and the run time for
scenario 2 was 1 day 19 hours and 45 minutes. Without
the restrictions on the bootstrap and EM iterations,
CAPL averaged about 250 bootstrap iterations and
about 40 EM iterations across markers in the simulated
samples. Therefore, the total iterations (250 bootstrap ×
40 EM) required for CAPL to have unbiased estimates
of parameters is about 13.3 times the number of
restricted iterations (50 bootstrap × 15 EM). In practice,
we expect that CAPL using 1 node would require more
than 17 days for similar sample size with scenario 1 and
more than 24 days for scenario 2. The results demon-
strate the importance of implementing CAPL with MPI
and pthreads in a distributed system.
Conclusions
CAPL is implemented with a hybrid of open MPI and
pthreads, which can be performed using a computer
cluster with shared memory. We also provide a version
of CAPL with pthreads only, so that the program can be
performed on a standalone machine with pthreads.
Without the parallelization, CAPL may require months
to complete a GWAS analysis using one processor. The
situation is the same for other computationally intensive
association software such as UNPHASED. To speed ana-
lysis, users may divide the input file into subsets of
markers and manually run the subsets of markers on
different machines. This is not ideal because extra sto-
rage is needed for the subsets of files. Moreover, redu-
cing the number of markers in a subset may cause the
loss of information about population substructure for
the clustering algorithm in CAPL. Therefore, the paral-
lelization for CAPL is essential for GWAS.
In conclusion, we developed the efficient software
package CAPL based on open MPI and pthreads, which
is a powerful association test that can accommodate
case-control and family data and account for population
stratification. As many GWAS datasets based on both
family and case-control designs are available, a flexible
and efficient tool such as CAPL will be very helpful to
combine the datasets to greatly increase statistical power
and finish the analysis in a reasonable time frame.
Moreover, population stratification is properly
accounted for in CAPL so that datasets from different
populations can be jointly analyzed.
Software Configuration
We compiled the CAPL code for the standalone version,
which can be executed on a single machine with multi-
ple cores, and the cluster version, which can be exe-
cuted in a cluster environment. We distribute binaries
for the standalone version (for Windows, Mac OS, and
Linux) and a binary for the cluster version (for Linux).
When executing the Windows binary, the pthreads
library (pthreadVC2.dll) needs to be in the same direc-
tory as the binary. For Linux and Mac OS users, we
strongly encourage the users to compile the code on
their machines, as the configurations of dynamic links
to libraries may vary on different machines.
In addition to binaries, we provide source code with
sets of makefiles for the standalone and cluster versions.
The standalone version can be compiled with GNU’s g+
+ 4.1.2 compiler or later versions. The cluster version
can be compiled with mpiCC. We also provide makefiles
based on Intel compilers for Intel hardware users. Based
on our experiments, CAPL has better performance on
the Intel hardware when it was compiled with the Intel
compiler. We provide examples of the submission
scripts for the cluster based on the commonly used LSF
and MOAB job schedulers. Users may need to work
with their cluster administrators to set up the para-
meters in the submission scripts properly. For example,
to achieve the optimal performance for CAPL, for each
computing node, the whole node needs to be reserved
for the job and the number of threads to run the job on
each node needs to be specified correctly in the submis-
sion script.
A control file is also required for the user to run
CAPL. The user needs to specify the input file format
and path. Currently the PLINK [12] binary file (bim,
Chung et al. BMC Bioinformatics 2011, 12:201
http://www.biomedcentral.com/1471-2105/12/201
Page 3 of 4
bed and fam files) or the text file (map and ped files)
formats are accepted by CAPL. Other important para-
meters in the control file are the number of threads,
number of nodes, number of subpopulations for the
clustering algorithm, and the name of the result file.
Note that the numbers of threads and nodes need to be
the same as the parameters specified in the submission
script for the cluster. More details about the configura-
tions of the CAPL software can be found in the software
documentation.
Availability and requirements
Project name: CAPL
Project home page: http://www.mihg.org/software_-
download/download_reg.php?software=CAPL
Operating system(s): Linux (both standalone and
cluster versions), Windows (standalone version), and
Mac OS (standalone version).
Programming language: C++
License: GNU GPL
Any restrictions to use by non-academics: None
Download statistics: 16 unique outside users since
the launch of the CAPL download site
Acknowledgements and Funding
We thank the Center for Computational Science (CCS) at University of Miami
for providing the computer cluster, which made our intensive simulations
possible. This work was supported by a University of Miami Stanley J. Glaser
Award and a grant from the Alzheimer Association (NIRG-09-132667).
Authors’ contributions
All co-authors contributed to writing the manuscript. RHC was the primary
author on the manuscript, developed the statistical methods and the design
for their implementation. MAS contributed to the implementation of the
software. ERM provided input to study design and interpretation of data. All
authors read and approved the final manuscript.
Received: 22 December 2010 Accepted: 25 May 2011
Published: 25 May 2011
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doi:10.1186/1471-2105-12-201
Cite this article as: Chung et al.: CAPL: an efficient association software
package using family and case-control data and accounting for
population stratification. BMC Bioinformatics 2011 12:201.
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Chung et al. BMC Bioinformatics 2011, 12:201
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Page 4 of 4


Chung, Ren-Hua

Schmidt, Michael A

Martin, Eden R

University of Miami: Scholarship Miami

Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson’s disease. Nat Genet

ER: CAPL: A Novel Association Test Using Case-Control and Family Data and Accounting for Population Stratification. Genetic Epidemiology

ER: The APL test: extension to general nuclear families and haplotypes and examination of its robustness. Hum Hered

et al: A genome-wide scan for common alleles affecting risk for autism. Hum Mol Genet

LA: Combined haplotype relative risk (CHRR): a general and simple genetic association test that combines trios and unrelated case-controls. Genet Epidemiol

Likelihood-based association analysis for nuclear families and unrelated subjects with missing genotype data. Hum Hered

MD: Generating Linkage Disequilibrium Patterns in Data Simulations using genomeSIMLA. Lect Notes Comput Sci

NL: Accounting for linkage in family-based tests of association with missing parental genotypes.

PC: PLINK: a tool set for wholegenome association and population-based linkage analyses.

RC: A unified association analysis approach for family and unrelated samples correcting for stratification.

Satten GA: Genetic association analysis using data from triads and unrelated subjects.

Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature

https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-12-201?site=bmcbioinformatics.biomedcentral.com

CAPL: an efficient association software package using family and case-control data and accounting for population stratification

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