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6 J.H. Lillvis et al.
this approach can be extended to other relationships such as cousin–cousin or
child–grandparent. The sib-pair approach is useful for studying late-age-at-
onset, lethal diseases like AAA for two important reasons (Fig. 3 ). First, sib-pair
linkage analyses require only two individuals from one generation of each
family to be alive and willing to contribute to the study, whereas conventional
family-based studies require affected individuals to be available for study from
at least three generations. Many individuals diagnosed with AAA have parents
who have died, but children too young to develop an aneurysm, therefore mak-
ing it difficult to find two, let alone three, generations living with aneurysms
(Fig. 3 ). Second, conventional linkage analyses rely on the ability to accurately
distinguish between affected and unaffected individuals. Since AAA is a late-
age-at-onset disease, its incidence increases with age, a phenomenon known
as age-dependent penetrance. However, by using a type of sib-pair analysis
known as affected sib-pair (ASP), problems of penetrance are avoided because
all subjects being studied have been diagnosed with the disease.
In the first published linkage analysis of AAA, Shibamura et al.
54
used an
affected relative pair (ARP) approach to carry out a genome-wide screen and
identified linked regions on chromosomes 4 and 19. This analysis was carried
out in two phases using 235 ARPs (213 ASPs) from 119 families with at least
2 individuals per family (mean = 3.2, range 2–7). In the first phase, genomic
DNA from 75 ARPs (62 ASPs) in 36 families was used in a whole-genome
scan of 405 microsatellite markers. From the initial scan, 12 regions were
identified as significant. In the second phase, additional microsatellite markers
in those 12 regions were genotyped using the original set of ARPs and 160
additional ARPs (151 ASPs). The combined data were then analyzed using
sex, number of affected individuals, and their interaction as covariates to allow
for genetic heterogeneity. This analysis showed two regions with significant
linkage, one on chromosome 19q13 with a logarithm of odds (LOD) score
of 4.64 and another on chromosome 4q31 with a LOD score of 3.73 (Fig. 4 ).
A second study by van Vlijmen-Van Keulen et al.
55
also demonstrated linkage
Fig. 3 Representative abdominal aortic aneurysm ( AAA ) families from our collection of 233 families. Slash across
symbol means death. Squares and circles with the upper right quadrant darkened indicate male and female mem-
bers, respectively, diagnosed with AAA.
https://t.me/med1917
Chapter 1 Genetics of Abdominal Aortic Aneurysms 7
to chromosome 19q13 (nonparametric, multipoint linkage score = 3.95) by a
different statistical method when analyzing 22 ASPs from three large Dutch
families. However, when 79 additional ASPs from 55 families were included
in the analysis, this method failed to demonstrate linkage, likely due to genetic
heterogeneity.
The chromosome 19 and 4 loci have been designated as the AAA1 and
AAA2 loci in the Online Mendelian Inheritance in Man (OMIM) database,
respectively.
56
Note that DNA linkage studies on thoracic aortic aneurysms
and dissections (TAAD) have identified completely different genetic regions
suggesting that the two aortic aneurysm subtypes are genetically different.
57
These findings are not too surprising considering the differences in the clini-
cal characteristics of patients who have TAAD versus those who have AAA;
TAAD patients are much younger and there is a much smaller gender difference
(1.7:1 male-to-female ratio). There are also differences in the pathobiology of
these aneurysms, TAADs being characterized by medial necrosis also known
as “Erdheim’s cystic medial necrosis,” mucoid infiltration, and cyst formation
with elastin degradation and vascular smooth muscle apoptosis.
58
Genetic Association Studies: From Candidate Gene Studies
to Genome-Wide Analyses
Genetic association studies, using methods similar to other forms of case-
control studies, compare two groups of individuals, those with a disease and
those without, with respect to frequency of a sequence variant from the gene
of interest.
53,
59
The comparisons are usually carried out using both genotype
and allele frequencies for the particular variant. If the frequency is statistically
higher or lower in the cases, it is suggestive of a causative or protective effect,
respectively. Testing for one genetic variant per gene is not usually sufficient
Fig. 4 Genetic-susceptibility locus on chromosome 19 for abdominal aortic aneurysm ( AAA ). This genetic region
was identified in a genome-wide DNA linkage study using affected relative pair approach. The bottom of the fig-
ure shows chromosome 19 with the linked region bracketed. The middle plot shows the logarithm of odds ( LOD )
score within the region. At the top of the figure, the locations of genes in the region have been indicated by arrows.
The locations of three candidate genes, low density lipoprotein receptor-related protein 3 ( LRP3 ), peptidase 3
( PEPD ), and hepsin ( HPN ), have been highlighted.
https://t.me/med1917
8 J.H. Lillvis et al.
unless a biologically important role for the tested variant has been established
in functional studies. Also, in many genetic association studies the exact
sequence variant responsible for the disease susceptibility has not been sought
for extensively. Rather, the genotyped markers are being used as surrogate
markers for variants with true functional effects. Identifying the causative
variants requires an extensive analysis of the surrounding genomic sequences,
which is often beyond the scope of the study.
Like other case-control studies, association studies have limitations.
Selection of controls for genetic association studies must be done carefully. If
independent samples of affected and unaffected individuals are used, factors
such as population stratification can lead to mathematical associations that
have no relationship to the underlying genetics of the disease being studied.
One possible approach is the use of spouses as controls since spouses tend
to be from the same ethnic and socioeconomic groups, and will have shared
many environmental factors. On the other hand, using “overmatched controls”
or “supercontrols” will lead to difficulty in estimating the population attribut-
able risk for the identified genetic variants. For this reason, some investiga-
tors are advocating the use of population-based controls representative of the
“ general” population in the geographic area where the study is performed. In
such studies, means for testing for potential stratification must be devised,
and the so-called “genomic control” method has been used widely. In this
approach, stratification can be tested by using genetic markers genotyped from
other chromosomal regions.
60
Alternatively, genetic markers known to show
population differences can be used to show that the case and control popula-
tions do not differ at those markers even when an association is detected in a
specific candidate gene being tested.
61
Another common limitation of genetic association studies is the sample
size. These studies require large sample sizes, and results obtained with fewer
than 250 cases and 250 controls must be viewed with skepticism. Even larger
sample sizes are needed if variants with low allele frequencies are being tested,
larger number of variants are being tested, or the study intends to investigate
gene–environment interactions. Yet another potential complication is multiple
testing since many research groups are interested in testing their study popula-
tion for multiple genes and genetic variants. It is possible to design these stud-
ies so that a preliminary screening of all variants is first carried out, followed
by testing only those variants which were found to be statistically significant
in the initial screening in a second independent set of samples. This approach
reduces the number of tests being done in the follow-up study. It must be
emphasized that replication of the findings in an independent sample set is
extremely important to eliminate false positives, although it is plausible that
population-specific disease-susceptibility alleles exist and that replication in
a different ethnic population will not support the initial findings. Detailed
recommendations on how to design, conduct, interpret, and report on genetic
association studies have been published recently.
62
Tables 3 and 4 summarize the genetic association studies for AAA reported
to date. These studies have examined many genes encoding for proteins known
to be important in AAA or vascular biology, such as matrix metallopeptidases
(MMPs), tissue inhibitors of metallopeptidases (TIMPs), angiotensin-convert-
ing enzyme ( ACE ), and nitric oxide synthase 3 ( NOS3 ). Table 4 summarizes
studies on a particularly intense area of study, human leukocyte antigen (HLA)
https://t.me/med1917
Table 3 Candidate genes for AAA tested in genetic association studies.
Number in the study Reported findings
Study Gene(s) studied
a
Ethnicity
Cases (subgroups) Controls (subgroups) Polymorphism (minor allele) Significance
b
Powell et al., 1990
63
HP UK 44 83 Variant a -chains a
1
/ a
2
p < 0.05
St. Jean et al., 1995
64
MMP9 USA 127 94 nt −131 to −90 CA repeat p = 0.36
Hamano et al., 1999
65
ACE Japan 125 153 Intron 16 287-bp I/D (D) p = 0.774
Wang et al., 1999
66
TIMP1 , TIMP2 Mixed Caucasian 84 51 TIMP1 , nt 434 C > T p = 0.5005 (males)
(64 male) (29 male) p = 0.0019 (females)
(20 female) (22 female) TIMP2 , nt 573 G > A p = 0.0156
Yoon et al., 1999
67
MMP3 , MMP9 ,
SERPINE1
Finnish 47 174 MMP3 nt −1621 5A/6A (5A) p = 0.1641
MMP9 , nt −131 to −90,
CA-repeat
p = 0.8171
SERPINE1 , nt −675 4G/5G (4G) p = 0.9421
Brunelli et al., 2000
68
MTHFR Italy 58 60 nt + 677 C > T NS
Kotani et al., 2000
69
NOS3 Japan 34 (surgical) 410 Intron 4 27-bp repeat p < 0.01 (surgical vs
control)
24 (nonsurgical) NS (nonsurgical vs
control)
p < 0.05 (surgical vs
nonsurgical)
NS (all AAA vs
control)
Rossaak et al., 2000
70
SERPINE1 New Zealand/
European
39 (FAAA)
151 (SAAA)
163 nt −675 4G/5G p = 0.024 (FAAA vs
SAAA)
p = 0.032 (FAAA vs
control)
Pola et al., 2001
71
ACE Italy 56 (NT)
68 (HT)
112 Intron 16 287 bp I/D OR = 5.31 (HT
AAA vs NT con-
trol)
OR = 3.63 (NT
AAA vs HT
AAA)
OR = 1.46 (HT
AAA vs NT con-
trol)
Schillinger et al., 2002
72
HMOX1 Austria 70 61 (controls) nt −258 to −195 GT repeat p = 0.04 (AAA vs
control)
(continued)
https://t.me/med1917
Number in the study Reported findings
Study Gene(s) studied
a
Ethnicity
Cases (subgroups) Controls (subgroups) Polymorphism (minor allele) Significance
b
70 (PVD)
70 (CAD)
p = 0.006; OR =
0.38 (AAA vs
CAD)
p = 0.01; OR = 0.35
(AAA vs PVD)
Unno et al., 2002
73
PLA2G7 Japan 131 106 nt + 994 G > T p = 0.015
Jones et al., 2003
74
MMP9 New Zealand 414 203 (controls)
172 (PVD)
nt −1562 C > T p = 0.013 (AAA vs
PVD)
p = 0.334, OR =
1.53 (AAA vs
control)
Bown et al., 2003
75
IL1B , IL6 , IL10 ,
TNF
UK 91 100 TNF nt −308 G > A p = 1.00
IL1B nt 3953 C > T p = 0.13
IL6 nt −174 G > C p = 0.92
IL10 nt −1082 G > A p = 0.03
IL10 nt −592 C > A p = 0.24
Ghilardi et al., 2004
76
CCR5 Italy 70 172 (controls) nt 794–825 deletion p = 0.064 (AAA vs
Control)
76 (PVD)
62 (carotid stenosis)
p = 0.012 (AAA vs
PVD and carotid
stenosis)
Massart et al., 2004
77
ELN , ESR1 ,
ESR2 , PGR ,
TGFB1
Italy 99 225 (male) ESR2 nt 1730 A > G p < 0.05, OR = 1.82
Five other polymorphisms NS
Fatini et al., 2005
78
ACE , AGTR1 Italy 250 250 Intron 16 287 bp I/D p < 0.0001
nt + 1166 A > C p = 0.4
Fatini et al., 2005
79
NOS3 Italy 250 250 nt + 894 G > T p < 0.0001
Intron 4 27 bp repeat p < 0.5
nt −786 T > C p = 0.6
Table 3 (continued)
https://t.me/med1917
Number in the study Reported findings
Study Gene(s) studied
a
Ethnicity
Cases (subgroups) Controls (subgroups) Polymorphism (minor allele) Significance
b
Ogata et al., 2005
80
MMP1 , MMP2 ,
MMP3 , MMP9 ,
MMP10 ,
MMP12 ,
MMP13 , TIMP1 ,
TIMP2 ,
TIMP3 ,
TGFB1 , ELN ,
COL3A1
Belgian, Canadian 387 425 TIMP1 nt 434 C > T p = 0.0047 (males
w/o family his-
tory)
TIMP1 rs2070584 T > G p = 0.015 (males
w/o family his-
tory)
TIMP1 TT haplotype p = 0.036
Schulz et al., 2005
81
ABCC6 Austria 133 910 total (controls) 31 mutations NS
54 (PXE)
Golledge et al., 2007
82
SPP1 Australia 689 3538 rs1126772, rs9138, rs4754,
rs1126616, rs11730582
All NS
Armani et al., 2007
83
MMP9 Italy 146 156 nt −1562 C > T NS
nt −131 to −90 CA repeat
Bown et al., 2007
84
IL10 UK 389 404 nt −1082 G > A p = 0.014 (A allele)
p = 0.004 (AA
genotype)
NS when adjusted
for covariates
Smallwood et al.,
2007
85
IL6 Australia 677 656 nt −174 G > C OR = 5.78 (−572
CC genotype in
recessive model)
nt −572 G > C OR = 1.55 (h.211
haplotype)
nt −597 G > A
Helgadottir et al.,
2008
86
ANRIL Several 2017 16,639 rs10757278-G p = 0.000003
(excluding known
CAD)
rs10811661-T NS
Jones et al., 2007
87
AGT , ACE ,
AGTR1 ,
BDKRB2
New Zealand,
Australia, UK
1226 1723 AGT rs699 p = 0.2
(continued)
https://t.me/med1917
Number in the study Reported findings
Study Gene(s) studied
a
Ethnicity
Cases (subgroups) Controls (subgroups) Polymorphism (minor allele) Significance
b
ACE rs4646994 p = 0.03 (heterozy-
gote), p = 0.05
(homozygote)
AGTR1 rs5186 p = 0.000012 (het-
erozygote)
p < 0.002 (homozy-
gote)
BDKRB2 rs6223 NS
Götting et al., 2008
88
XLYT1 Austria 129 129 nt 343 G > T p = 0.011
nt 1989 T > C p = 0.274
HP = haptoglobin; TIMP1/TIMP2/TIMP3 = tissue inhibitor of matrix metallopeptidases gene family; ACE = angiotensin-converting enzyme; MMP1/MMP2/MMP3/MMP9/MMP10/MMP12/
MMP13 = matrix metallopeptidase gene family; SERPINE1 = serpin peptidase inhibitor, clade E, member 1 (plasminogen activator inhibitor type 1); NOS3 = nitric oxide synthase 3 (endothelial cell);
MTHFR = 5,10-methylenetetrahydrofolate reductase; PLAG27 = phospolipase A2, group VII (platelet activating factor acetylhydrase, plasma); HMOX1 = heme oxygenase (decycling) 1; IL1B/IL6/
IL10 = interleukin gene family; TNF = tumor necrosis factor-alpha; CCR5 = chemokine receptor 5; ELN = elastin; ESR1/ESR2 = estrogen receptors 1 and 2; PGR = progesterone receptor; TGFB1 =
transforming growth factor-beta 1; AGTR1 = angiotensin II receptor, type 1; COL3A1 = collagen, type III, alpha 1; ABCC6 = ATP-binding cassette, subfamily C (CFTR/MRP), member 6; SPP1 =
secreted phosphoprotein 1 (osteopontin); PVD = peripheral vascular disease; NS = not significant; HT = hypertensive; NT = normotensive; FAAA = familial AAA; SAAA = sporadic AAA; PXE
= pseudoxanthoma elasticum
a
Gene symbols used are HGNC-approved symbols obtained from http://www.genenames.org/
b
p -values as reported in original studies. The statistical tests and actual comparisons vary. Some studies corrected for multiple testing, others did not
Ta ble 3 (continued)
https://t.me/med1917
Chapter 1 Genetics of Abdominal Aortic Aneurysms 13
Table 4 Genetic association studies for AAA using variants in the HLA genes.
Study
HLA loci (number
of alleles) Population studied Cases Controls
Findings
Allele/Haplotype Statistic
Norrgård et al., 1984
89
HLA-A (7) Sweden 48 368 NS NS
HLA-B (11)
Rasmussen et al., 1997
90
HLA-DR B1 (13) USA/White 37 90 HLA-DR B1
*
15 p = 0.035
HLA-DQ B1 (5) HLA-DR B1
*
0404 p = 0.032
HLA-DQ B1 alleles NS
Hirose et al., 1998
91
HLA-DR (12) Japan 46 50 HLA-DR2 (15) p < 0.005
Other HLA-DR alleles NS
Hirose and Tilson, 1999
92
HLA-DQ (4) Japan 36 39 HLA-DQ3 p = 0.014
Rasmussen et al., 2001
93
HLA-DR B1 (12) USA/White 112 (degenerative) 118 HLA-DR B1
*
02 (inflammatory) OR = 3.6
40 (inflammatory) HLA-DR B1
*
02 (degenerative) OR = 2.2
HLA-DR B1
*
04 (inflammatory) OR = 2.5
HLA-DR B1
*
04 (degenerative) OR = 2.0
Rasmussen et al., 2002
94
HLA-DR B1 USA/White 142 118 HLA-DR B1
*
02 OR = 2.5
HLA-DR B1
*
04 OR = 2.1
Ogata et al., 2006
95
HLA-DQA1 Belgian/Canadian 180 (Belgian) 269 (Belgian) HLA-DQA1
*
0102 (Belgians) p = 0.019
(Empirical)
HLA-DQB1 207 (Canadian) 157 (Canadian) HLA-DQA1-DRB1 (Begians) p = 0.0003
(Asymptotic)
HLA-DRB1, 3–5 All others p = 0.049
N S
Badger et al., 2007
96
HLA-A Northern Ireland 241 1000 All comparisons NS
HLA-B
HLA-DR
HLA human leukocyte antigen, NS not significant
https://t.me/med1917
14 J.H. Lillvis et al.
types. Note that many early association studies were small and did not have
adequate power to definitively exclude or prove an association. More recent
studies have overcome this problem by using larger groups of cases and
controls. Another important point is that for some polymorphisms, different
studies have published conflicting results. For example, in three studies on a
polymorphism in ACE , two studies reported an association
71,
78
whereas one
did not.
65
Similarly, a polymorphism in the gene SERPINE1 (also known as
plasminogen activator inhibitor 1) was found to have an association with AAA
in the New Zealand population
70
but not in the Finnish population.
67
Possible
explanations for these discrepancies are variation in genetic risk factors
between populations, and false positives or false negatives from small sample
sizes (type I and II errors).
Despite the problems with association studies on AAA mentioned above,
there is convincing evidence for polymorphisms in several genes contributing
to the development of AAA. Although there is conflicting evidence for a
polymorphism in ACE , the largest and most recent study with 250 Italian
cases and controls demonstrated a significant association.
78
A study by the
same group also showed a significant association for one polymorphism in
the NOS3 gene.
79
TIMP1 is another gene that shows possible association,
although one study reported association only in females,
66
whereas a second
larger study found association only in males without a positive family his-
tory.
80
Finally, in one of the two largest studies of HLA alleles, Ogata et al.
found association with HLA DQA*0102 in the Belgian population.
95
Early
studies on HLA DRB1 reported significant association, but both Ogata et al.
and a recent study by Badger et al.
96
with 241 cases and 1000 controls did
not confirm these results.
The next phase of genetic association studies will involve the entire
genome. These genome-wide association studies, commonly referred to as the
acronym GWAS, will require even larger samples sizes, but will give a better
understanding of the genetic architecture of AAA.
62
They are also more feasi-
ble than family-based studies for complex and deadly diseases such as AAA
since samples from extended families are not required.
Microarray-Based Global Gene Expression Profiling:
An Unbiased Approach to Identify Signaling Pathways
Altered in AAA
Expression profiling is the characterization of a tissue by which genes
are expressed in it and how highly they are expressed. Although several
methods have been developed to study gene expression profiles, the cur-
rent standard is whole-genome microarray platforms available from several
manufacturers. Microarray experiments compare gene expression patterns
between tissues or cells, such as between an aneurysmal aorta and a nonan-
eurysmal one. The changes in gene expression can then be used to identify
biological processes relevant to the disease being studied. This can be done
by categorizing differentially expressed genes by function, such as gene
ontology (GO) category
97
or Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathway,
98
and determining the categories or pathways that are
statistically enriched.
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Chapter 1 Genetics of Abdominal Aortic Aneurysms 15
Although microarrays have the potential to be a powerful tool for analysis,
they are also subject to some limitations. First, as with other types of gene
analysis, sample replicates are necessary to account for sample variability.
This can be problematic if the cost of the microarrays is prohibitive or if
obtaining multiple tissue samples is difficult. Another limitation is incomplete
annotation for gene function; GO categories and KEGG pathways are only
useful for genes with known function. Additionally, microarrays only test
for levels of mRNA and may not represent what is occurring on the protein
level. Therefore, it is important that microarray findings be followed-up using
different methods such as immunohistochemistry. Finally, there are several
statistical considerations for the analysis of microarray data such as how to
define differential expression or account for the large number of false positives
generated when looking at thousands of genes. For a detailed discussion of
microarray data analysis, the reader is referred to Allison et al.
99
There have been five array-based studies published on AAA.
100–
104
The
four earliest studies were performed using older, so-called macroarrays that
hybridized radiolabeled sample cDNA to gene-specific cDNA clones spot-
ted on a nylon membrane, between 275 and 1185 probes. The major draw-
back to these arrays was that they simply did not probe enough genes to
study which biological processes and pathways were altered. Additionally,
the measurement of expression relied upon densitometry, which is less
sensitive than current fluorescence-based methods. However, these stud-
ies were able to confirm some previously known expression changes (e.g.,
MMP9 , IL8) and provided limited expression information for future com-
parisons. Furthermore, three of the studies compared AAA to other disease
processes. Armstrong et al.
101
compared AAA with aortic occlusive disease
and showed some differences between the diseases, but the number of genes
( n = 265) studied was too small to make any generalizations. In a similar
study, Yamagishi et al.
103
compared expression of 375 cytokine genes
between AAA and carotid artery stenosis. Absi et al.
102
determined the
genes that are differentially expressed between thoracic aorta and thoracic
aortic aneurysms (TAA) and then compared these with the genes whose
expressions change in AAA. This analysis was able to show that AAA is
characterized by changes in genes related to inflammation, whereas TAA
is characterized by changes in cell survival, proliferation, and programmed
cell death.
In contrast to the three earlier papers, Lenk et al.
104
reported the only whole-
genome comparison of AAA and control aorta using two different microarray
platforms. From a reference list of 18,057 genes common to the two micro-
arrays, 3274 genes were found to be differentially expressed between AAA
( N = 7) and control tissue ( N = 7), even after correction for multiple testing
(
Fig. 5 ). These differentially expressed genes confirm previous work implicat-
ing genes such as MMPs, IL1B , and cathepsins in the pathogenesis of AAA,
as well as point to new genes for study such as RUNX3 and SOST . Analysis of
the differentially expressed genes using KEGG pathways showed several path-
ways related to immune function to be enriched in AAA, including “natural
killer cell-mediated cytotoxicity,” “leukocyte transendothelial migration,” and
“T cell-receptor signaling pathway.” This confirms previous histological work
showing immune activation to be important in the development of aneurysm
and suggests that this area merits further study.
https://t.me/med1917
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