Download PDF - Arteriosclerosis, Thrombosis, and Vascular Biology

Editorial
Association Studies of Vascular Phenotypes
How and Why?
L. Almasy, J.W. MacCluer
T
Downloaded from http://atvb.ahajournals.org/ by guest on June 17, 2017
the disease or trait in question and the other group, controls,
does not. Association exists when the allele frequencies differ
between cases and controls. To avoid spurious associations, it
is important that the case and control groups be matched as
closely as possible for potentially confounding factors that
may be correlated with the phenotype, such as ethnicity or
cigarette smoking.
To eliminate the need to match case and control populations, another method was developed which derives control
alleles from the chromosomes carried by parents of cases.2,3
In the absence of association, there is an equal probability that
either of a parent’s two alleles will be transmitted to his or her
offspring. The transmission disequilibrium test, or TDT, tests
whether a given allele was transmitted from a heterozygous
parent to affected offspring more often than it was not
transmitted. This requires that cases and both of their parents
be genotyped. The genotyping of parents or other additional
individuals is becoming increasingly inexpensive and helps
guard against some types of spurious association. Particularly
for late-onset diseases, parents of affected individuals may
not be available. Extensions to the TDT have been developed
which use only a single parent, siblings of affected individuals, or both parents and siblings.4,5
The most basic type of association analysis for quantitative
traits involves testing whether the trait mean varies among
individuals with different genotypes. This is the approach
taken by Brousseau et al.1 For a rare variant, the analysis may
be done by grouping subjects into two categories, those who
carry the allele and those who do not. For more common
polymorphisms, where all genotypes appear at appreciable
frequencies, an additive model may be used. In this case, the
trait mean for heterozygotes is constrained to be exactly
halfway between that of the two homozygotes. This implies
that each “dose” of the variant allele has the same incremental
effect on phenotypic values. As with discrete traits, these tests
are susceptible to hidden stratification in the sample. Although the statistical methodology is more straightforward
with unrelated individuals, various methods can be used to
perform this type of test in a sample of related individuals
taking into account their familial relationships.6 – 8
Markers with more than two alleles complicate all of the
above tests. It is common for investigators to try to reduce a
multiallelic marker to a two-allele system by combining the
alleles into two groups. There are many possible ways to
construct these combinations, and if these permutations are
not accounted for in the statistical analyses, they can greatly
increase the rate of false-positive associations. Alternatively,
some statistical tests have been designed specifically for use
with multiallelic markers.
echnological and methodological advances in the last
decade have rendered genetic studies of complex
traits, influenced by multiple genes and their interactions with each other and with the environment, increasingly
feasible. Cardiovascular disease and its risk factors have been
the subject of numerous genetic studies seeking to identify
the specific DNA variants that influence these traits. One
such study appears in this issue of Arteriosclerosis, Thrombosis, and Vascular Biology. Brousseau and colleagues1
investigate associations of a variant in the cholesteryl ester
transfer protein (CETP) gene with lipid and lipoprotein
concentrations, particle sizes, and coronary heart disease
endpoints. Given the increasing frequency of genetic association studies in the pages of the Journal, we take this
opportunity to explain the types of methodology used in these
studies, how the results of these studies may be interpreted,
and how association techniques fit into the larger arsenal of
genetic epidemiological methods.
See page 1148
How Do You Do an Association Study?
Genetic association studies essentially look for correlations
between phenotype and genotype. The phenotype may be
presence or absence of disease, such as atherosclerosis, or it
may be a quantitative measure such as systolic blood pressure
or HDL cholesterol concentration. Slightly different analytical techniques, explained below, are used for discrete and
quantitative phenotypes. The genotype is generally obtained
from some type of polymorphic marker. This may be a short
tandem repeat or microsatellite in which the number of copies
of a 2-, 3-, or 4-base pair DNA motif varies in the population
or it may be a single nucleotide polymorphism (SNP) in
which a particular DNA base pair varies. A marker is said to
be polymorphic and may be referred to as a polymorphism, if
the frequency of the most common variant is less than 99%.
Microsatellites generally have 4 to 12 variants, or alleles,
whereas SNPs generally have 2. Each individual carries two
alleles, one obtained from each parent.
For a discrete trait, the simplest sort of association study
counts the frequency of each allele at a polymorphic marker
in two groups of unrelated individuals. One group, cases, has
From the Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Tex.
Address correspondence to Laura Almasy, Department of Genetics,
Southwest Foundation for Biomedical Research, PO Box 760549, San
Antonio, TX 78245-0549. E-mail [email protected]
(Arterioscler Thromb Vasc Biol. 2002;22:1055-1057.)
© 2002 American Heart Association, Inc.
Arterioscler Thromb Vasc Biol. is available at http://www.atvbaha.org
DOI: 10.1161/01.ATV.00000024686.49995.41
1055
1056
Arterioscler Thromb Vasc Biol.
July 2002
Why Might You Observe an Association?
Downloaded from http://atvb.ahajournals.org/ by guest on June 17, 2017
There are three reasons why one might observe an association, or correlation, between a marker and a phenotype. It is
possible that the relationship is a causal one and the genotyped marker is itself functional. This implies that the
different alleles at the marker change the transcription of the
DNA into RNA, affect the processing or stability of the RNA
or the protein, or change the structure of the protein. A second
option is that the genotyped marker is not itself functional,
but is in linkage disequilibrium with other polymorphisms
that are functional. Linkage disequilibrium, explained in
greater detail below, is a function of both physical proximity
between the marker and the functional polymorphism on the
same chromosome, and their shared history in the population.
Finally, it is possible that the association is due to population
stratification. Population stratification refers to the case in
which a correlation between a marker and a phenotype is due
to each being correlated with a third, nongenetic factor. The
classical example of such a factor is ethnicity. Allele frequencies differ in different ethnic groups, sometimes appreciably.
If the frequency of disease also differs among these groups,
an association may be observed between disease and a marker
simply because the ethnic make-up of the affected and
unaffected samples differs. In this case, any marker with
discordant allele frequencies in the underlying populations
would show an association with disease, regardless of its own
functionality or its proximity to other functional
polymorphisms.
Given these potential sources of association, we must be
cautious in the conclusions we draw from association studies.
If care has been taken to avoid potential sources of stratification and to correct for multiple testing, the most likely
explanation for a positive result is that one or more functional
sites are in disequilibrium with the genotyped marker. If the
different alleles at the genotyped marker result in changes in
the amino acid structure of a protein or if previous in vitro
studies have identified differences in, for example, stability,
localization, binding, or transcription rate of the products of
the different alleles, then we may have a stronger case for
arguing that the genotyped marker is functional and directly
influences the phenotype.
What Is Linkage Disequilibrium?
Linkage disequilibrium occurs when an allele at one genetic
locus (for example, a rare mutation in a functional gene) is
situated on the same chromosome with a specific allele at
another locus (for example, the most common allele at a
polymorphic marker locus) more often than would be expected by chance. Linkage disequilibrium between a mutation
and surrounding polymorphisms is an artifact of the history of
the mutation. A given mutation originates in a single individual. At that point, the new allele occurs only on that one
chromosomal background, and there is complete disequilibrium between the new mutation and the surrounding markers.
When this first individual reproduces, recombinations occur
between the new allele and the surrounding markers. In the
eggs or sperm of the original individual, the mutation now
appears in connection with more than one of the alleles at the
surrounding markers, though still more often on the original
chromosomal background, and we have incomplete disequilibrium. The probability of recombination is proportional to
the distance between the new mutation and the other marker.
So markers that are closer to the new mutation are likely to be
in stronger disequilibrium with it. Generations pass, more
recombinations occur, and disequilibrium between the mutation and surrounding markers continually decreases. Eventually, the mutation reaches equilibrium with the surrounding
markers. At equilibrium, the probability of finding a particular combination of alleles occurring together is simply the
product of their individual allele frequencies. In addition to
recombination, recurrence of the same mutation also decreases disequilibrium. A particular mutation may have arisen
multiple times in different individuals on different chromosomal backgrounds. In this case, disequilibrium with surrounding markers may only exist in population subgroups
that may not be easily identified.
In association studies that seek to localize genes influencing human phenotypes, the difficulty with exploiting disequilibrium is that it is generally impossible to guess where in this
process a mutation is. If a mutation influencing HDL cholesterol levels occurred only recently, then disequilibrium between it and other markers is likely to be strong. Only a small
sample size will be required to detect association, and few
markers will need to be genotyped because disequilibrium
will extend over a broad chromosomal region. If the mutation
is somewhat older, a larger sample size will be required and
more markers will need to be genotyped. In some cases, when
equilibrium has been reached, no disequilibrium will be
present and association will not be detected unless we are
lucky enough to pick the mutation itself as the marker to be
genotyped for our study. Even if we are lucky enough to pick
a functional site as the marker to be genotyped, we may still
run into trouble if there are multiple mutations that produce
similar phenotypic effects. In this case, only a few of the
individuals with high cholesterol levels may carry a particular
deleterious allele.
Another difficulty is that the relationship between disequilibrium and distance erodes quickly. Contrary to expectation,
two polymorphisms at adjacent base pairs may be in equilibrium with each other while each is in strong disequilibrium
with sites tens or hundreds of base pairs away. This makes it
difficult to select a subset of markers within a region that will
capture the relevant genetic variation. Or, conversely, it is
difficult to predict to what extent a particular set of markers
represents the genetic variation within a gene or region. Tiret
et al9 surveyed disequilibrium between markers in 50 candidate genes related to cardiovascular disease and found considerable variation between genes in the patterns of intragenic
disequilibrium. Given this variation, the finding of negative
association with a set of polymorphisms cannot exclude a
particular gene, as it is possible that there are functional sites
within the gene that are not in disequilibrium with the
genotyped markers. Only when all polymorphic sites within a
gene have been tested and rejected can we safely conclude
that the gene has no effect on a phenotype.
Almasy and MacCluer
How Do Association Tests Fit Into the
Big Picture?
Downloaded from http://atvb.ahajournals.org/ by guest on June 17, 2017
With the recent progress in the human genome project and the
increasing availability of sequence data and SNPs, it has been
suggested that association studies will replace linkage studies
as the method of choice for localizing genes influencing
complex phenotypes. Linkage differs from association in that
it is based on the joint transmission of a marker and a
functional site from parent to offspring (ie, co-segregation),
rather than on correlation. Thus, linkage studies do not
require disequilibrium and are not susceptible to population
stratification. However, cosegregation can only be detected
by observing the passage of chromosomes between generations, and thus linkage studies require family data. On the
positive side, in a linkage study, a given marker does
represent a whole chromosomal region, and generalizations
about a gene or a region can be made from negative results;
if linkage is formally excluded in a particular chromosomal
region, then we can conclude that the region does not contain
genes that have a large effect on the phenotype. On the
negative side, linkage exists over longer distances than
disequilibrium does, and a positive result implicates an entire
chromosomal region rather than a specific gene.
Although the discussion of the relative merits of linkage
and association methods is often framed in terms of a
competition between the two, they are in fact complementary.10,11 The unpredictable nature of disequilibrium across the
genome and the fact that single markers can represent entire
regions in linkage studies imply that linkage methods are
likely to be more efficient for initial gene localization.12 The
limited extent of disequilibrium and the fact that linkage
extends over large regions suggest that association methods
are likely to be more useful for narrowing in on specific
genes. To put it another way, linkage methods are generally
good for finding new genes, and association methods are
typically good for testing known ones. To screen the genome
with association methods would require the genotyping of
hundreds of thousands of markers, a formidable task given
current technology. To screen the genome with linkage
methods takes only a few hundred markers. However, linkage
with markers in a candidate gene implies only that there are
functional variants in that general chromosomal region,
whereas association with markers in a candidate gene implies
that there are functional variants very nearby. Given the
family-based association methods, linkage and association
analyses may be carried out in the same sample as a study
progresses from initial screening to following up promising
signals. Sophisticated joint tests of linkage and association
can then be used to test whether polymorphisms showing
Association Studies of Vascular Phenotypes
1057
association can account for a previously observed linkage
signal.13
Advances in sequencing technology will soon permit
investigators to carry out comprehensive surveys of the full
range of polymorphisms within a candidate gene in large
samples of individuals, and advances in statistical genetic
methodology will provide ever more powerful techniques for
extracting information from these data. Further, the current
rapid development of new molecular and statistical genetic
methods that can complement existing approaches will undoubtedly inspire new study designs that will increase the
pace of gene discovery in the study of complex diseases.
Acknowledgments
The authors wish to acknowledge grants from the National Heart,
Lung and Blood Institute (HL45522, HL65520, HL64244), the
National Institute of Mental Health (MH59490), and the National
Institute of General Medical Sciences (GM31575).
References
1. Brousseau ME, O’Connor JJ Jr, Ordovas JM, Collins D, Otvos JD,
Massov T, McNamara JR, Rubins HB, Robins SJ, Schaefer EJ. The CETP
B2B2 genotype is associated with higher HDL cholesterol levels and
lower risk for coronary heart disease end points in men with HDL
deficiency. Arterioscler Thromb Vasc Biol. 2002;22:1148 –1154.
2. Falk CT, Rubinstein P. Haplotype relative risks: an easy way to construct
a proper control sample for risk calculations. Ann Hum Genet. 1987;51:
227–233.
3. Spielman RS, McGinnis RE, Ewens W. Transmission test for linkage
disequilibrium: the insulin gene region and insulin-dependent diabetes
mellitus (IDDM). Am J Hum Genet. 1993;52:506 –516.
4. Horvath S, Laird NM. A discordant-sibship test for disequilibrium and
linkage: no need for parental data. Am J Hum Genet. 1998;63:1886 –1897.
5. Sun F, Flanders WD, Yang Q, Khoury MJ. Transmission disequilibrium
test (TDT) when only one parent is available: the 1-TDT. Am J Epidemiol. 1999;150:97–104.
6. Hopper JL, Matthews JD. Extensions to multivariate normal models for
pedigree analysis. Ann Hum Genet. 1982;46:373–383.
7. Boerwinkle E, Chakraborty R, Sing CF. The use of measured genotype
information in the analysis of quantitative phenotypes in man. Ann Hum
Genet. 1986;50:181–194.
8. Zhu X, Elston RC. Transmission/disequilibrium tests for quantitative
traits. Genet Epidemiol. 2001;20:57–74.
9. Tiret L, Poirier O, Nicaud V, Barbaux S, Herrmann SM, Perret C, Raoux
S, Francomme C, Lebard G, Tregouet D, Cambien F. Heterogeneity of
linkage disequilibrium in human genes has implications for association
studies of common diseases. Hum Mol Genet. 2002;11:419 – 429.
10. Almasy L, Williams JT, Dyer TD, Blangero J. Quantitative trait locus
detection using combined linkage/disequilibrium analysis. Genet Epidemiol. 1999;17(Suppl 1):S31–S36.
11. Terwilliger JD, Göring HHH. Gene mapping in the 20th and 21st centuries: statistical methods, data analysis, and experimental design. Hum
Biol. 2000;72:63–132.
12. Terwilliger JD, Weiss KM. Linkage disequilibrium mapping of complex
disease: fantasy or reality? Curr Opin Biotechnol. 1998;9:578 –594.
13. Soria JM, Almasy L, Souto JC, Tirado I, Borrell M, Mateo J, Slifer S,
Stone W, Blangero J, Fontcuberta J. Linkage analysis demonstrates that
the prothrombin G20210A mutation jointly influences plasma prothrombin levels and risk of thrombosis. Blood. 2000;95:2780 –2785.
Downloaded from http://atvb.ahajournals.org/ by guest on June 17, 2017
Association Studies of Vascular Phenotypes: How and Why?
L. Almasy and J.W. MacCluer
Arterioscler Thromb Vasc Biol. 2002;22:1055-1057
doi: 10.1161/01.ATV.0000024686.49995.41
Arteriosclerosis, Thrombosis, and Vascular Biology is published by the American Heart Association, 7272
Greenville Avenue, Dallas, TX 75231
Copyright © 2002 American Heart Association, Inc. All rights reserved.
Print ISSN: 1079-5642. Online ISSN: 1524-4636
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://atvb.ahajournals.org/content/22/7/1055
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published
in Arteriosclerosis, Thrombosis, and Vascular Biology can be obtained via RightsLink, a service of the
Copyright Clearance Center, not the Editorial Office. Once the online version of the published article for which
permission is being requested is located, click Request Permissions in the middle column of the Web page
under Services. Further information about this process is available in thePermissions and Rights Question and
Answer document.
Reprints: Information about reprints can be found online at:
http://www.lww.com/reprints
Subscriptions: Information about subscribing to Arteriosclerosis, Thrombosis, and Vascular Biology is online
at:
http://atvb.ahajournals.org//subscriptions/