M Morley

Richard S Spielman

TC Matise

VG Cheung

Vivian G Cheung

BMC Proceedings

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BioMed CentralBMC Proceedings
ssOpen AcceProceedings
Data for Genetic Analysis Workshop (GAW) 15, Problem 1: 
genetics of gene expression variation in humans
Vivian G Cheung1,2,3 and Richard S Spielman*2
Address: 1The Children's Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, Pennsylvania 19104, USA, 2Department of 
Genetics, University of Pennsylvania School of Medicine, 415 Curie Blvd, Philadelphia, Pennsylvania 19104-6145, USA and 3Department of 
Pediatrics, University of Pennsylvania School of Medicine, 3516 Civic Center Blvd, ARC 516, Philadelphia, Pennsylvania 19104, USA
Email: Vivian G Cheung - vcheung@mail.med.upenn.edu; Richard S Spielman* - spielman@mail.med.upenn.edu
* Corresponding author    
Abstract
Here we describe the data provided for Problem 1 of Genetic Analysis Workshop 15. The data
provided for Problem 1 were unusual in two ways. First, the phenotype was the level of gene
expression for each gene, not a conventional phenotype like height or disease, and second, there
were more than 3500 such phenotypes. Natural variation in gene expression was a new idea in
2004 when these data were collected and published. Because the phenotypes were measured in
members of 14 Centre d'Etude du Polymorphisme Humain (CEPH) families, there was an
opportunity for linkage mapping on a very large scale. For this purpose, 2882 single-nucleotide
polymorphism genotypes were also provided for each family member.
Background
There is extensive individual variation in the expression
level of many genes in organisms from yeast to humans.
In humans, the differences are smaller in monozygotic
twins than among individuals of other relationships, sug-
gesting a genetic contribution to the variation [1]. The
data for Problem 1 came from studies of the genetic basis
of variation in human gene expression [2,3].
Methods
Study subjects
The data provided to Genetic Analysis Workshop 15
(GAW15) were of several sorts. The basic collection was of
data from large families, specifically 14 three-generation
Centre d'Etude du Polymorphisme Humain (CEPH) Utah
families (approximately 8 offspring per sibship and 14
individuals per family). The CEPH Utah families are the
most uniform of the three-generation CEPH families (par-
ents and grandparents are available) and cells are availa-
ble for all four grandparents. The data provided were from
14 of these. In addition, gene expression data were pro-
vided from 30 "HapMap" trios: these are "grandparent-
parent" trios that are partly included among those in the
14 families, plus approximately 12 additional grandpar-
ent-parent trios of CEPH Utah individuals. The 30 trios
are also part of the International HapMap Project. The
data included pedigree files with information on the
structure of each family.
from Genetic Analysis Workshop 15
St. Pete Beach, Florida, USA. 11–15 November 2006
Published: 18 December 2007
BMC Proceedings 2007, 1(Suppl 1):S2
<supplement> <title> <p>Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci</p> </title> <editor>Heather J Cordell, Mariza de Andrade, Marie-Claude Babron, Christopher W Bartlett, Joseph Beyene, Heike Bickeböller, Robert Culverhouse, Adrienne Cupples, E Warwick Daw, Josée Dupuis, Catherine T Falk, Saurabh Ghosh, Katrina A Goddard, Ellen L Goode, Elizabeth R Hauser, Lisa J Martin, Maria Martinez, Kari E North, Nancy L Saccone, Silke Schmidt, William Tapper, Duncan Thomas, David Tritchler, Veronica J Vieland, Ellen M Wijsman, Marsha A Wilcox, John S Witte, Qio g Yang, Andreas Ziegler, Laura Almasy a d Jean W MacCluer</editor> <note>Proce dings</note> <url>http://www.biomedc ntral.com/content/pdf/1753-6561-1-S1-info.pdf</url> </supplement>
This article is available from: http://www.biomedcentral.com/1753-6561/1/S1/S2
© 2007 Cheung and Spielman; 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.Page 1 of 2
(page number not for citation purposes)
BMC Proceedings 2007, 1(Suppl 1):S2 http://www.biomedcentral.com/1753-6561/1/S1/S2Phenotypes
The expression level of genes expressed in lymphoblastoid
cells (EBV-transformed B-cells) were obtained for the
above subjects, using the Affymetrix Human Focus Arrays
that contain probes for 8500 transcripts. For approxi-
mately 85 of the study subjects, array hybridizations were
performed in duplicate. Data were provided for the 3554
genes for which we found greater variation among indi-
viduals than between replicates for the same individual.
The cel files (raw image files) were provided, as well as
slightly processed data (normalized data using the
Affymetrix MAS software) for all array hybridizations.
Genotypes
Genotypes of 2882 autosomal and X-linked SNPs for
members of the 14 CEPH Utah families described above
were provided. The genotypes were generated by The SNP
Consortium [[4]; also http://snpdata.cshl.edu/]. These
marker genotypes were used for the mapping of gene
expression phenotypes described in Morley et al. [2]. To
make it possible to compare linkage results obtained in
GAW15, the SNP genotype files that were used by Morley
et al. [2] to generate the linkage results were also provided.
These included the physical location of the SNP markers.
The genotypes for the HapMap subjects are freely availa-
ble from the HapMap website http://www.hapmap.org.
Because those data sets continue to be updated, they were
not provided separately for GAW15.
Discussion and possible analyses
The distinctive feature of these data is that genome scan
data are provided for more than 3500 quantitative traits at
one time. So instead of asking about properties of analysis
methods for one phenotype at a time, once can ask about
"operating characteristics" over a whole range of traits.
Furthermore, because the phenotypes are in some sense
all of one type (level of gene expression), it is natural to
look for relationships among them, in a way that would
not normally be appropriate for a collection of dissimilar
traits, such as (for example) blood pressure, IQ, height,
and serum glucose. The data offer an opportunity to look
for genes whose expression appears to be co-regulated,
and try to find where the determinants of these expression
phenotypes map.
In addition, one might
1. Test methods for transforming expression data in a con-
text where some answers are essentially "known."
2. Explore problems of multiple testing.
3. Explore replication by subsetting data, and/or by cross-
validation and other post-hoc methods. Develop and use
permutation tests appropriate for such a collection of
data.
4. Look for interactions of chromosomal regions, or par-
ticular genes, etc.
Conclusion
The data provided for Problem 1 offer a unique opportu-
nity for analysis of genetics of variation in expression lev-
els of a large number of genes. The data lend themselves
to a wide variety of analyses, including mapping of deter-
minants for individual expression phenotypes, testing for
effects of multiple determinants, and technical aspects of
microarray interpretation. The participants in GAW15 car-
ried out a remarkable variety of approaches, with ingen-
ious new ideas for learning from the data.
Competing interests
The author(s) declare that they have no competing inter-
ests.
Acknowledgements
This work was supported by U.S. National Institutes of Health grants (to 
RSS and VGC) and by the W.W. Smith Chair (VGC).
This article has been published as part of BMC Proceedings Volume 1 Sup-
plement 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://www.biomedcentral.com/
1753-6561/1?issue=S1.
References
1. Cheung VG, Conlin LK, Weber TM, Arcaro M, Jen KY, Morley M,
Spielman RS: Natural variation in human gene expression
assessed in lymphoblastoid cells.  Nat Genet 2003, 33:422-425.
2. Morley M, Molony CM, Weber T, Devlin JL, Ewens KG, Spielman RS,
Cheung VG: Genetic analysis of genome-wide variation in
human gene expression.  Nature 2004, 430:743-747.
3. 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.
4. Matise TC, Sachidanandam R, Clark AG, Kruglyak L, Wijsman E,
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(page number not for citation purposes)


Data for Genetic Analysis Workshop (GAW) 15, Problem 1: genetics of gene expression variation in humans

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Data for Genetic Analysis Workshop (GAW) 15, Problem 1: genetics of gene expression variation in humans

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