- Open Access
Effects on linkage analyses of different Affymetrix expression measures as quantitative trait phenotypes
© Peralta and Almasy; licensee BioMed Central Ltd. 2007
- Published: 18 December 2007
We compared results from linkage analyses of different phenotype measurements from the same gene expression traits and found that the strongest signals were detected by all expression measures that we considered. On average, that meant that the same quantitative trait loci (QTLs) were detected across methods, but the magnitude of the LOD score of each particular QTL and the false-positive ratio of QTL detection varied between the methods.
- Expression Measure
- Gene Expression Measure
- Expression Trait
- Robust Multiarray Average
- Gene Expression Trait
The Affymetrix GeneChip® is a very popular microarray system that is widely used by researchers in the biomedical field. A variety of statistical methods has been developed to obtain gene expression data from it. Robust multiarray average (RMA) , GeneChip RMA (GCRMA) , Microarray Suite Analysis (MAS5)  and dChip  are four commonly used gene expression measures that differ in their approach to background correction, normalization, and summarization of probe intensity.
As a consequence of such diversity, researchers have sought the best method for a given application, often driven by the comparison of their performance under specific conditions. Many studies have investigated the effects that different methods of obtaining expression values may have on the selection of differentially expressed genes or sequences from Affymetrix arrays. As noted by Irizarry et al. , conflicting results are often reported, making it difficult to identify the best expression measure for a given application. It would be logical to expect that regardless of the expression measure employed, the conclusions reached about the differential expression of the genes would be similar. Millenaar et al.  provided evidence showing that the reality is not quite so. They found less than 40% agreement in the selection of differentially expressed genes when comparing six different expression measures.
While Irrizry et al. and Millenaar et al. provide benchmarking and performance clues for several expression measures, both are focused on the use of expression values to identify differentially expressed genes under experimental conditions. The recent works of Cheung et al. [7, 8] and Morley et al.  have introduced a new application of gene expression arrays with the novel use of expression values as quantitative trait phenotypes. This raises concerns about the behavior and possible effects that the choice of a particular expression measure might have on downstream analyses such as heritability estimates and linkage. The data from Morley et al.  available in Problem 1 of Genetic Analysis Workshop 15 (GAW15) provide a convenient way to explore some of those concerns.
We were particularly interested in knowing whether the linkage signals detected by using phenotypes derived from a particular expression measure would still be present if other expression measures were to be used. This can be approached in several ways . In this work we took a results-oriented look at how well overall conclusions from linkage results derived from phenotypes calculated from one expression measure stand up when other expression measures are used. To that end, we compared the linkage results from an analysis of some of the phenotypes used by Morley et al., with the results derived from four commonly used gene expression measures.
The GAW15 Problem 1 (P1) data set was used by this study . Immortalized B cell gene expression data of 8793 probe sets (probes) from each of 276 GeneChip® Human Genome Focus Arrays was available for 193 individuals (56 founders) of 14 three-generation Centre d'Etude du Polymorphisme Humain families. Quantitative trait phenotypes derived from the 3554 probes with the most variable expression phenotypes identified by Morley et al. (P1QP) were also provided for 194 individuals.
RMA, GCRMA, MAS5, and two dChip gene expression values (DCHIPPM: only perfect-match probe data from each array was used for background correction; DCHIPMM: mismatch probe data was subtracted from perfect-match data during background correction) were estimated and log2-transformed with Bioconductor . The duplicate arrays that were available for some individuals (n = 82) were not used.
As described by McClintick et al. , three probes with very strong sex-specific expression were found: 214218_s_at (female), 205000_at and 206700_s_at (male). Their expression values were in disagreement with the specified genders of individuals 1421–8 (male) and 1421–14 (female). The arrays and P1QP of those two individuals were excluded from analyses.
Heritabilities of all P1QP traits were estimated with SOLAR , using the tdist adjustment, which allows for the robust estimation of the mean and variance from a trait when its distribution deviates from multivariate normality. The significance of each heritability estimate was then subjected to the family-wise type I error rate (FWER) adjustment of Sidak . All probes from P1QP with heritabilities ≥ 0.5 and a FWER p-value ≤ 0.05 were selected as the quantitative traits for linkage.
The expression values of the selected traits defined the RMAQP, GCRMAQP, MAS5QP, DCHIPPMQP, and DCHIPMMQP phenotype sets according to the expression measure from which they originated. The phenotypes of the selected traits from P1QP were used as a baseline for heritability and LOD score comparisons (REFQP). Note that REFQP and MAS5QP should be highly correlated because both were derived from MAS5 expression values.
In addition, a "false-positive set" of phenotypes was derived from the phenotype sets described above (the "real-linkage set"). In order to preserve the heritability structure of the real phenotype sets, and since Hinrichs et al.  found a high intraclass correlation between the sib phenotypes, phenotypes were randomly swapped between whole families by shuffling the family identifiers of each individual, keeping the same generational hierarchy intact.
A genetic map for linkage analyses was derived from the P1 physical map using the single-nucleotide polymorphism (SNP) Mapping web application at the University College of Dublin (UCD) Conway Institute of Biomolecular and Biomedical Research ; eight markers not mapped by it were linearly interpolated. Mendelian inheritance inconsistencies and double-recombinant genotypes were blanked from the P1 SNP genotypes according to mistyping probabilities from Simwalk . Multipoint IBD matrices for all 2882 autosomal and X-linked SNP markers were constructed with Merlin  and Merlin's minx.
Heritability and linkage analyses of the selected quantitative traits and phenotypes of all 14 families were performed with SOLAR, using the tdist adjustment and sex as the only covariable. LOD scores were calculated at 5-cM intervals along the 22 autosomes and the X chromosome, and at 1-cM intervals around signals ≥ 2.
We focused our observations on LOD scores equal to or above two different thresholds: three and five. In addition, because there is likely to be a correlation between the magnitude of closely located high LOD scores, we summarized them as a signal (a QTL), defined by the highest local maximum over the range of contiguous LOD scores that passed the threshold criteria. This gave us the highest peaks of the local LOD score curve, and its width (in centimorgans) at the threshold level. In this way, we expected to reduce the number of possibly redundant linkage signals in a region to a few. We arbitrarily defined cis linkages as those signals that included the location of the trait's probe (regardless of the length of the region). For trans linkages, the signals did not include the location of the trait's probe. Note that this is a different definition than the one used by Morley et al.
Distribution of the heritabilities
0.50 to 0.89
0.38 to 0.82
0.20 to 0.96
0.23 to 0.99
0.00 to 0.92
0.00 to 0.92
Summary of false-positive cis/trans linkage results (N, number of QTLs)
LOD ≥ 2
LOD ≥ 3
LOD ≥ 4
LOD ≥ 5
LOD ≥ 6
Mean LOD (max)
Summary of cis/trans real-linkage results (N, number of QTLs)
LOD ≥ 3
LOD ≥ 5
Mean LOD (max)
trans N (Traits)
trans N (Traits)
We performed linkage analyses with different phenotype measurements of the same group of expression traits and detected several QTLs. Because true QTLs are not known for any of the traits in the original data, we can not address certain issues that are of interest, such as how different expression measures affect the power to detect QTLs. However, we were able to gain some insight on other equally interesting effects that the differently pre-processed expression phenotypes had.
For instance, we examined the false-positive rate of QTL detection, based on two LOD score magnitude thresholds, and found a large amount of variation between expression measures. GCRMA was the method that had fewest false positives; MAS5 had the most. Close examination of MAS5QP false positives showed that they were the result of the fluctuation of the LOD score curve – around the threshold used – over the span of many centimorgans. This tended to break related signals as separate QTLs. Examination of three additional thresholds (at LOD ≥ 2, 4 and 6, Table 2) seems to indicate that this phenomenon is not a threshold-induced artifact and suggests that it might be a consequence of the higher magnitude of MAS5QP false-positive LOD scores. However, REFQP – also MAS5 derived phenotypes – did not perform as poorly as MAS5QP, although their phenotypes were not exactly the same. It is likely that differences in the procedure used to construct them – use or not of duplicate arrays – and other quality control steps are the cause of their different false-positive behavior. If that is the case, then it might mean that RMAQP, GCRMAQP, DCHIPMM, and DCHIPPM are more robust to the effects of random noise because they were subjected to exactly the same quality control and sampling as MAS5QP but gave considerably lower false positives.
Strong linkage signals appeared independently of the expression measure used to define the phenotype. That meant, on average, that the same QTLs tended to be detected by all measures (Figure 1). Yet, there were large – and apparently non-systematic – between-measure differences in the magnitude of the LOD scores observed for any particular QTL. We do not have an explanation of this phenomenon. However, from a practical point of view, the variation in LOD score magnitude is less of a concern for the strongest signals because the QTLs associated with them are likely to be detected, regardless of the expression measure used, with a reasonable threshold.
There was greater agreement across expression measures for cis QTLs than for trans QTLs. Because it is likely that cis QTLs are characterized by a stronger genetic effect, this is not a surprise . On the other hand, trans QTLs were much more in disagreement across measures. They showed a tendency to give weaker signals, possibly because of their smaller genetic effects or because they are more likely to be false positives. That mostly all false-positives were trans QTLs would seem to support this notion.
Our results suggest that, for QTLs of large effect sizes, the choosing of one gene expression measure as a quantitative phenotype is not likely to have profound repercussions in the conclusions drawn from linkage analyses of a particular expression trait, provided that false-positive ratios are taken into account, and reasonable QTL detection thresholds are set. However, large differences between the expression measures were observed in some cases, particularly for weaker linkage signals that may be due to QTLs with smaller genetic effects.
This work was supported in part by National Institutes of Health grants MH59490, MH078111, and HL45522.
This article has been published as part of BMC Proceedings Volume 1 Supplement 1, 2007: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci. The full contents of the supplement are available online at http://0-www.biomedcentral.com.brum.beds.ac.uk/1753-6561/1?issue=S1.
- Irizarry RA, Hobbs B, Collin F, Barclay YDB, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4: 249-264. 10.1093/biostatistics/4.2.249.View ArticlePubMedGoogle Scholar
- Wu Z, Irizarry RA, Gentleman R, Martinez Murillo F, Spencer F: A model-based background adjustment for oligonucleotide expression arrays. J Am Stat Assoc. 2004, 99: 909-917. 10.1198/016214504000000683.View ArticleGoogle Scholar
- Statistical algorithms description document. [http://www.affymetrix.com/support/technical/whitepapers/sadd_whitepaper.pdf]
- Li C, Wong WH: Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Nat Acad Sci USA. 2001, 98: 31-36. 10.1073/pnas.011404098.View ArticlePubMed CentralPubMedGoogle Scholar
- Irizarry RA, Wu Z, Jaffee HA: Comparison of Affymetrix GeneChip expression measures. Bioinformatics. 2006, 22: 789-794. 10.1093/bioinformatics/btk046.View ArticlePubMedGoogle Scholar
- Millenaar FF, Okyere J, May ST, van Zanten M, Voesenek LACJ, Peeters AJM: How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results. BMC Bioinformatics. 2006, 7: 137-10.1186/1471-2105-7-137.View ArticlePubMed CentralPubMedGoogle Scholar
- Cheung VG, Conlin LK, Weber TM, Arcaro M, Jen K, Morley M, Spielman RS: Natural variation in human gene expression assessed in lymphoblastoid cells. Nat Genet. 2003, 33: 422-425. 10.1038/ng1094.View ArticlePubMedGoogle Scholar
- Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT: Mapping determinants of human gene expression by regional and genome-wide association. Nature. 2005, 437: 1365-1369. 10.1038/nature04244.View ArticlePubMed CentralPubMedGoogle Scholar
- Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, Cheung VG: Genetic analysis of genome-wide variation in human gene expression. Nature. 2004, 430: 743-747. 10.1038/nature02797.View ArticlePubMed CentralPubMedGoogle Scholar
- Labbe A, McClintick J, Martinez M: Summary of Contributions to GAW Group 16: Processing/Normalization of Expression Traits. Genet Epidemiol. 2007, 31 (Suppl 1): S132-S138. 10.1002/gepi.20290.View ArticlePubMedGoogle Scholar
- Cheung VG, Spielman RS: Data for Genetic Analysis Workshop (GAW) 15, Problem 1: Genetics of Gene Expression Variation in Humans. BMC Proc. 2007, 1 (Suppl 1): S2-View ArticlePubMed CentralPubMedGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5: R80-10.1186/gb-2004-5-10-r80.View ArticlePubMed CentralPubMedGoogle Scholar
- McClintick JN, Liu Y, Edenberg HJ: Mapping of trans-acting regulatory factors from microarray data. BMC Proc. 2007, 1 (Suppl 1): S155-View ArticlePubMed CentralPubMedGoogle Scholar
- Almasy L, Blangero J: Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998, 62: 1198-1211. 10.1086/301844.View ArticlePubMed CentralPubMedGoogle Scholar
- Sidak Z: Rectangular confidence regions for the means of multivariate normal distributions. J Am Stat Assoc. 1967, 62: 626-633. 10.2307/2283989.Google Scholar
- Hinrichs AL, Culverhouse R, Jin CH, Suarez BK: Linkage and association analysis of principal components in expression data. BMC Proc. 2007, 1 (Suppl 1): S46-View ArticlePubMed CentralPubMedGoogle Scholar
- SNP Mapping Web Application. [http://actin.ucd.ie/software.html]
- Sobel E, Papp JC, Lange K: Detection and integration of genotyping errors in statistical genetics. Am J Hum Genet. 2002, 70: 496-508. 10.1086/338920.View ArticlePubMed CentralPubMedGoogle Scholar
- Abecasis GR, Cherny SS, Cookson W, Cardon LR: Merlin – rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002, 30: 97-101. 10.1038/ng786.View ArticlePubMedGoogle Scholar
- Kestler HA, Muler A, Gress TM, Buchholz M: Generalized Venn diagrams: a new method of visualizing complex genetic set relations. Bioinformatics. 2005, 21: 1592-1595. 10.1093/bioinformatics/bti169.View ArticlePubMedGoogle Scholar
This article is published under license to 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.