argyle: an R package for analysis of Illuminagenotyping arraysAndrew P Morgan∗,1

∗Department of Genetics, University of North Carolina, Chapel Hill, NC

ABSTRACT Genotyping microarrays are an important and widely-used tool in genetics. I present argyle, anR package for analysis of genotyping array data tailored to Illumina arrays. The goal of the argyle package isto provide simple, expressive tools for non-expert users to perform quality checks and exploratory analyses ofgenotyping data. To these ends, the package consists of a suite of quality-control functions, normalizationprocedures, and utilities for visually and statistically summarizing such data. Format-conversion tools allowinteroperability with popular software packages for analysis of genetic data including PLINK, R/qtl and DOQTL.Detailed vignettes demonstrating common use cases are included as supporting information. argyle bridgesthe gap between the low-level tasks of quality control and high-level tasks of genetic analysis. It is freelyavailable at https://github.com/andrewparkermorgan/argyle.

KEYWORDS

SNP microarraysgenotypingsoftware

INTRODUCTION

High-throughput genotyping of tens of thousands of SNPs usingmicroarrays is common practice in both laboratory and populationgenetics. Genotypes at a dense panel of biallelic markers with a lowrate of missing data are a valuable resource for breeding, marker-assisted selection, genetic mapping and analyses of populationstructure. The Illumina Infinium system is one popular and cost-effective (∼ $100/sample) platform. Custom Illumina arrays areavailable for many organisms of research, agricultural or ecologicalinterest including mouse (Collaborative Cross Consortium 2012;Morgan and Welsh 2015), dog, cat (Willet and Haase 2014), chicken,cow, pig, horse, sheep (Kijas et al. 2009), salmon (Johnston et al.2013) and cotton (Hulse-Kemp et al. 2015).

Infinium arrays consist of many thousands of short invari-ant oligonucleotide probes conjugated to silica beads. SampleDNA is hybridized to the probes and a single-base, hybridization-dependent extension reaction is performed at the target SNP. Al-ternate alleles (herein denoted A and B) are labelled with differentfluorophores (Steemers et al. 2006). Raw fluorescence intensityfrom the two color channels is processed into a discrete genotypecall at each SNP, and both the total intensity from both channelsand the relative intensity in one channel versus the other are infor-mative for copy number.

Copyright © 2015 Andrew P Morgan et al.Manuscript compiled: Friday 16th October, 2015%1Please address correspondence to apm@email.unc.edu

Many tools, both open-source and proprietary, already exist forpost-processing of raw hybridization intensity data. R packagesinclude beadarray (Dunning et al. 2007), lumi (Du et al. 2008) andcrlmm (Ritchie et al. 2009) among others. Illumina’s proprietaryBeadStudio software is widely-used by commercial laboratoriesand core facilities. BeadStudio applies a six-step “affine normaliza-tion” (Peiffer 2006) which pools data across many probes and manyarrays. Intensities from the two color channels (herein denoted xand y) are transformed to lie in the standard coordinate plane, withhomozygous genotypes near the x and y axes, heterozygous geno-types approximately on the x = y diagonal, and R = x + y ≈ 1.Biallelic genotypes are then called by clustering in this space.

Fewer tools exist for downstream quality control, exploratoryanalysis and interpretation of genotype calls jointly with underly-ing hybridization intensity data. To fill this gap I present argyle, apackage for the R statistical computing environment. The purposeof argyle is to provide simple and flexible tools for programmaticaccess to data from SNP arrays, with an emphasis on visualization.Although some functionality is tailored to Illumina arrays, manyof the features are general enough to accommodate any datasetwhich can be expressed as a matrix of genotypes at biallelic mark-ers. The main text of this paper outlines the key features of argyle;detailed code vignettes are provided as supplementary material.

DESIGN

The design of argyle is inspired by the PLINK software (https://www.cog-genomics.org/plink2/; Purcell et al. (2007)). A PLINK fileset

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MULTIPARENTAL POPULATIONS

has three parts: a genotype matrix, a marker map and a “pedigree”(sample and family metadata) file. Likewise the central data struc-ture in argyle (the genotypes object) stores a matrix of genotypecalls, and hybridization-intensity data when available, in parallelwith a marker map and sample metadata. A genotypes object istherefore a self-contained and largely self-describing representa-tion of a genotyping dataset. The genotypes object is described infurther detail in File S1.

This package explicitly favors simplicity and readability of codeover raw efficiency. It is appropriate for the “medium-sized” data –tens of thousands of markers and hundreds of individuals – reg-ularly encountered in experimental contexts. Users with largerdatasets such as those routinely collected in human genetics – mil-lions of markers and thousands of individuals – which do notfit comfortably in memory should explore more sophisticated Rpackages (such as the GenABEL suite (http://www.genabel.org/)).

QUALITY CONTROL

Removal of poorly-performing markers and poor-quality samplesis an important precursor to genetic analysis. Failed arrays arecharacterized by aberrant intensity distributions, excess of miss-ing and heterozygous calls, or both. A summary plot (Figure 1)facilitates the identification of low-quality samples. Concordancebetween biological sex and sex inferred from calls on the sex chro-mosomes is also useful for identifying contaminated or swappedsamples. Failed arrays can be flagged and removed using global orsubgroup-specific thresholds. See File S2 for a worked example.

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In addition to global summaries, argyle provides easy accessto hybridization intensity data from individual probes. Inspectionof “cluster plots” for individual probes is useful for confirming

the accuracy of genotype calls and diagnosing poorly-performingmarkers (Figure 2). A dotplot (Figure 3) permits direct inspectionof genotype calls at multiple markers over small genomic regions.

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Figure 2 Cluster plots for individual markers. Each point repre-sents a single sample; points are colored according to genotypecall, expressed as number of copies of the non-reference allele.The marker on the left performs as expected: the three canonicalclusters are present in the expected locations. The marker on theright may be genotyped incorrectly: there is not homozygous ref-erence cluster, and the nominally heterozygous samples fall intotwo clusters. This marker merits further inspection. For example,one nominally heterozygous cluster may correspond to homozy-gosity for the reference allele or the marker may be detectingparalogous variation at other loci.

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Figure 3 Dotplot representation of genotypes among 9 wild-caught mice on proximal chromosome 19 (from Yang et al.(2011)). Genotype calls are coded as counts of the reference al-lele, and points are colored according to genotype call. Blankspaces indicate missing calls. Markers are plotted with constantspacing in the main panel; grey lines indicate physical positionalong the chromosome in megabases (Mbp).

ARRAY NORMALIZATION

Illumina BeadStudio uses an “affine normalization” algorithmto perform within- and between-array adjustments to x- and y-hybridization intensities before calling genotypes. However, fur-ther normalization is helpful for analyses of sample contaminationand copy number. Two standard metrics are the log2 R/R0 ratio(LRR), which captures total hybridization intensity (R) relative to areference level (R0); and B-allele frequency (BAF), which capturesthe relative signal from the A and B alleles (Peiffer 2006). For anuncomtaminated euploid sample, the expected value of LRR is 0and the expected value of BAF is 0.5 at heterozygous markers.

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Figure 4 Joint plot of B-allele frequency (BAF, upper panel) and log2 intensity ratio (LRR, lower panel) for an outbred male sample.The autosomes are almost entirely heterozygous, while the X chromosome is hemizygous: no points appear near BAF = 0.5 on the Xchromosome, and its LRR is decreased relative to the autosomes. Red traces are a local smoothing of underlying points.

The argyle package implements the thresholded quantile nor-malization (tQN) approach described in Staaf et al. (2008) andDidion et al. (2014). Brielfy, tQN peforms within-array quantilenormalization of the x channel against the y channel to accountfor dye biases specific to the Infinium chemistry, but places anupper bound on the difference between normalized and unnor-malized intensity values. LRR and BAF are then computed usingknown cluster positions computed from a set of reference samples.The tQN procedure may optionally be preceded by preliminarybetween-array quantile normalization using routines implementedin the preprocessCore package (Bolstad et al. 2003). A joint plot ofBAF and LRR (Figure 4) is valuable for assessing heterozygosity,ploidy, sample purity and sex-chromosome karyotype.

Copy-number inference from Illumina arrays is a well-studiedproblem for which good solutions already exist – for instance, thestandalone software PennCNV (Wang et al. 2007) or the R packagegenoCN (Sun et al. 2009). Most of these packages take BAF and LRRvalues as input and so are easily integrated downstream of argyle.

Systematic batch effects on intensity distributions are possiblewhen analyzing samples processed which were not processedconcurrently. The reliability of discrete genotype calls may beunchanged between batches, but downstream analyses that makeuse of hybridization intensities (eg. copy-number analyses, orhidden Markov models for haplotype inference in multiparentalpopulations (Fu et al. 2012; Gatti et al. 2014)) may benefit from afurther batch correction. One possibility, given k non-overlappingbatches, is quantile normalization of batches 1, . . . , k − 1 againstthe kth batch. Although between-batch normalization is not yetimplemented in argyle, it is slated for inclusion in future releases.

GENETICS TOOLS

Utilities are provided for efficient calculation of allele frequencies,heterozygosity and missingness by sample and by marker. Whengenotypes of both parents and offspring are available, pedigreerelationships can be confirmed via checks for Mendelian inconsis-tencies. Separate datasets can be concatenated or merged using

functions that ensure consistency of allele encoding and detectstrand swaps (eg. an [A/G] versus a [T/C] SNP).

To facilitate analysis of genotypes from experimental crosses,argyle provides functions for recoding alleles with respect toparental lines. A general-purpose hidden Markov model (HMM)allows for reconstruction of haplotype mosaics, given a panel ofreference samples and a genetic map – although users are cau-tioned that more sophisticated implementations are available forsome special cases (Broman et al. 2003; Fu et al. 2012; Gatti et al.2014). Mature tools for genetic mapping in the R environment al-ready exist (eg. R/qtl (Broman et al. 2003)). Genotypes processedwith argyle can be readily converted to R/qtl format to create aunified pipeline for quantitative-trait locus (QTL) mapping. One-and two-locus “mosaic plots” allow joint visualization of allele fre-quencies and phenotype at candidate QTL (Figure 5). A workedexample is provided in File S3.

Genome-wide patterns of relatedness can be explored usingbuilt-in functions for efficient kinship estimation (Figure 6) andprincipal components analysis (Figure 7). See File S4 for moredetailed demonstration of functions useful for population-geneticanalysis.

DATA EXPORT

The argyle package provides functions to convert genotypes ob-jects to other formats either within the R session (for R/qtl andDOQTL) or on disk. Currently argyle supports export to eitherPLINK binary format (*.fam/*.bim/*.bed) or Stanford HGDP for-mat. PLINK provides command-line utilities to convert its fileformat to many others, including VCF, LINKAGE (*.map/*.ped),Haploview, STRUCTURE and fastPHASE. In addition, sincegenotypes objects are regular R matrices, users can adapt them tobespoke input formats required by other tools for genetic analysis.

PERFORMANCE

argyle and its dependencies are compatible with R (≥ 2.14)on Windows or Mac OS X. The performance of argyle benefits

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Figure 6 Heatmap representation of matrix of pairwise geneticdistances between 28 wild-caught mice from three different sub-species using data from the Mouse Diversity Array (Yang et al.2011). Genetic distance is defined here as the proportion of alle-les shared identical by state between two individuals. The matrixis hierarchically clustered to that more closely-related samplesare adjacent to each other. The heatmap is useful for visualizingpopulation structure; here it reveals obvious genetic differentia-tion between mouse subspecies.

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4 | Andrew P Morgan et al.

from optimized code in several existing R packages includingdata.table and Rcpp (Eddelbuettel 2013). Reading a dataset of re-alistic size – 96 samples × 77,808 markers (164 Mb ZIP-compressedon disk) – from Illumina BeadStudio output into an R session takesabout 30 seconds. The full dataset, including hybridization intensi-ties and sample and marker metadata, occupies 202.4 Mb; withouthybridization intensities, the size drops to 77.9 Mb. Memory usagescales approximately linearly with either the number of samplesor the number of markers (Figure 8A). The most computationally-intensitive component of argyle is the tQN procedure and is im-plemented in C++. Its running time is compared to the quantilenormalization routine from the preprocessCore package in Fig-ure 8B. These resource requirements are well within the range of atypical laptop or desktop computer.

R’s internal limit of 231 − 1 entries for any matrix or vectorplaces an upper bound on the dimensions of a genotypes object.For arrays with between 10,000 and 150,000 markers, this translatesto a limit of between 14,000 and 21,000 samples.

Tests were performed in R 3.1.2 (64-bit) on a MacBook Air witha single 1.7Ghz Intel Core i7 processor and 8 Gb RAM.

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ACKNOWLEDGMENTS

The author thanks John Didion for advice on design and quality-control methods; John Didion, Dan Gatti, Marty Ferris and SofiaGrize for valuable feedback on early versions of this software;and Robert Corty, Will Valdar and Fernando Pardo-Manuel deVillena for helpful comments on this manuscript. This work wassupported in part by F30MH103925 (APM), U19AI100625 (FPMV),

U42OD010924 (Terry Magnuson) and the UNC Bioinformatics andComputational Biology Training Grant (T32GM067553).

SUPPORTING INFORMATION

A series of detailed vignettes demonstrate the key features ofargyle.File S0. Installation.File S1. Data import.File S2. Quality control.File S3. Analysis of an experimental cross.FIle S4. Analysis of genotypes from natural populations.

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