Am. J. Hum. Genet. 77:389–407, 2005
Susceptibility Genes for Age-Related Maculopathy on Chromosome 10q26
Johanna Jakobsdottir,1 Yvette P. Conley,2,3 Daniel E. Weeks,1,2 Tammy S. Mah,4
Robert E. Ferrell,2 and Michael B. Gorin2,4
Departments of 1Biostatistics and 2Human Genetics, Graduate School of Public Health, 3Department of Health Promotion and Development,
School of Nursing, and 4UPMC Eye Center, Department of Ophthalmology, School of Medicine, University of Pittsburgh, Pittsburgh
On the basis of genomewide linkage studies of families affected with age-related maculopathy (ARM), we previously
identified a significant linkage peak on 10q26, which has been independently replicated by several groups. We
performed a focused SNP genotyping study of our families and an additional control cohort. We identified a strong
association signal overlying three genes, PLEKHA1, LOC387715, and PRSS11. All nonsynonymous SNPs in this
critical region were genotyped, yielding a highly significant association (P ! .00001) between PLEKHA1/LOC387715
and ARM. Although it is difficult to determine statistically which of these two genes is most important, SNPs in
PLEKHA1 are more likely to account for the linkage signal in this region than are SNPs in LOC387715; thus, this
gene and its alleles are implicated as an important risk factor for ARM. We also found weaker evidence supporting
the possible involvement of the GRK5/RGS10 locus in ARM. These associations appear to be independent of the
association of ARM with the Y402H allele of complement factor H, which has previously been reported as a major
susceptibility factor for ARM. The combination of our analyses strongly implicates PLEKHA1/LOC387715 as
primarily responsible for the evidence of linkage of ARM to the 10q26 locus and as a major contributor to ARM
susceptibility. The association of either a single or a double copy of the high-risk allele within the PLEKHA1/
LOC387715 locus accounts for an odds ratio of 5.0 (95% confidence interval 3.2–7.9) for ARM and a population
attributable risk as high as 57%.
Introduction
Age-related maculopathy (ARM), or age-related macular degeneration (ARMD-1 [MIM 603075]), is a leading
cause of central blindness in the elderly population, and
numerous studies support a strong underlying genetic
component to this complex disorder. Genomewide linkage scans performed using large pedigrees, affected sib
pairs, and, more recently, discordant sib pairs have identified a number of potential susceptibility loci (Klein et
al. 1998; Weeks et al. 2000; Majewski et al. 2003; Schick
et al. 2003; Seddon et al. 2003; Abecasis et al. 2004;
Iyengar et al. 2004; Kenealy et al. 2004; Schmidt et
al. 2004; Weeks et al. 2004; Santangelo et al. 2005).
Our genomewide linkage screen strongly implicated the
10q26 region as likely to contain an ARM gene (Weeks
et al. 2004); this region has also been supported by many
other studies and was the top-ranked region in a recent
meta-analysis (Fisher et al. 2005). Recently, three studies
(Edwards et al. 2005; Haines et al. 2005; Klein et al.
Received June 7, 2005; accepted for publication June 29, 2005;
electronically published July 26, 2005.
Address for correspondence and reprints: Dr. Michael B. Gorin,
Department of Ophthalmology, Eye and Ear Institute Building, 203
Lothrop Street, Room 1027, Pittsburgh, PA 15213. E-mail: gorinmb
@upmc.edu
䉷 2005 by The American Society of Human Genetics. All rights reserved.
0002-9297/2005/7703-0007$15.00
2005) identified an allelic variant in the complement
factor H gene (CFH [MIM 134370]) as responsible for
the linkage signal seen on chromosome 1 and as the
variant accounting for a significant attributable risk
(AR) of ARM in both familial and sporadic cases. We
and others have confirmed these findings (Conley et al.
2005; Hageman et al. 2005; Zareparsi et al. 2005a).
CFH has previously been suspected of playing a role in
ARM, as a result of the work of Hageman and Anderson
(Hageman and Mullins 1999; Johnson et al. 2000, 2001;
Mullins et al. 2000; Hageman et al. 2001), who have
shown that the subretinal deposits (drusen) that are observed in many patients with ARM contain complement
factors. However, until other genes that contribute to
ARM are identified, CFH remains an isolated piece of
the puzzle, implicating the alternative pathway and inflammation as part of the ARM pathogenesis but failing
to fully account for the unique pathology that is observed in the eye.
We have expanded our family linkage studies and
have also undertaken a case-control association study,
using a high-density SNP panel in two regions of linkage
on 1q31 and 10q26 that we had previously reported.
Our SNP linkage and association results for chromosome 1q31 yielded the same findings as others, confirming that the peak of linkage and the strongest associations with ARM were localized over the CFH gene. We
have analyzed both our family data and the case-control
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Am. J. Hum. Genet. 77:389–407, 2005
Table 1
Distribution of Subphenotypes in Patients with Advanced ARM
NO. OF PATIENTS FROM
CIDR FAMILIESa
NO. OF PATIENTS FROM
LOCAL FAMILIESa
NO. OF LOCAL
UNRELATED PATIENTS
SUBPHENOTYPE
With GA
Without GA
With GA
Without GA
With GA
Without GA
With CNV
Without CNV
220 (76)
108
187
62
130 (45)
57
106
28
71 (17)
40
59
26
NOTE.—The numbers in parentheses are the numbers of individuals with both CNV and GA who
were also included in GA group (see text for selection criteria) for OR and AR estimation and
association tests.
a
Counts are based on the set of unrelated cases generated by selecting one type A–affected person
from each family (see section “Part III: Interaction and OR Analysis: Unrelated cases”).
data on chromosome 10q26 to identify the next major
ARM susceptibility–related gene.
Material and Methods
Families and Case-Control Cohort
A total of 612 ARM-affected families and 184 unrelated controls were sent to the Center for Inherited
Disease Research (CIDR) for genotyping. Because of
possible population substructure, we restricted our analysis to the subset of data from white subjects; we were
not able to analyze the set of data from nonwhites separately, because it was too small. The white subset had
594 ARM-affected families, containing 1,443 genotyped
individuals, and 179 unrelated controls. The white families contained 430 genotyped affected sib pairs, 38 genotyped affected avuncular pairs, and 52 genotyped affected first-cousin pairs.
A total of 323 white families, 117 unrelated controls,
and 196 unrelated cases were also genotyped locally for
additional SNPs. The local subset contained 824 genotyped individuals, 298 genotyped affected sib pairs, 23
genotyped affected avuncular pairs, and 38 genotyped
affected first-cousin pairs. We used PedStats from the
Merlin package (Abecasis et al. 2000) to easily get summary counts on the family data.
large numbers of extramacular drusen were not coded
as unaffected.
In our efforts to examine specific ARM subphenotypes, we chose to look at only patients with end-stage
disease, either those with evidence of choroidal neovascular membrane (CNV) in either eye or those with geographic atrophy (GA) in either eye. There are a significant number of individuals who have been described as
having both GA and CNV, though this is problematic,
since, in these cases, it is often difficult to determine
whether the GA is secondary to the damage from the
CNV or is from the treatment given to limit the CNV
growth (i.e., laser, surgery, or photodynamic therapy).
Because it is often difficult to discern from photographs
or records whether a person had GA in an eye prior to
the development of CNV, we included the patients who
had both pathologies in the CNV group. However, we
allowed only a subset of this overlapping group to be
included in the GA group, specifically those who reportedly had GA in one eye that did not have evidence
of CNV. Table 1 shows the numbers of individuals in
each of our three sets. This approach may have excluded
a small proportion of individuals from the GA group
who had asymmetric GA prior to the development of
CNV in the same eye or who may have had bilateral
GA but developed CNV in both eyes.
Affection-Status Models
We have defined three classification models (types A,
B, and C) for the severity of ARM status (Weeks et al.
2004). For simplicity, we have restricted our attention
here to individuals affected with “type A” ARM, our
most stringent and conservative diagnosis. We used only
unrelated controls who were unaffected under all three
diagnostic models. Unaffected individuals were those for
whom eye-care records and/or fundus photographs indicated either no evidence of any macular changes (including drusen) or a small number (!10) of hard drusen
(⭐50 mm in diameter) without any other retinal pigment
epithelial (RPE) changes. Individuals with evidence of
Pedigree and Genotyping Errors and Data Handling
We used the program PedCheck (O’Connell and Weeks
1998) to check for Mendelian inconsistencies. Since it
can be extremely difficult to determine which genotypes
within small families are erroneous (Mukhopadhyay et
al. 2004), we set all genotypes at each problematic
marker to missing within each family containing a Mendelian inconsistency. Mega2 (Mukhopadhyay et al.
2005; see Division of Statistical Genetics Web site) was
used to set up files for linkage analysis and for allelefrequency estimation by gene counting.
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Jakobsdottir et al.: Chromosome 10q26 and Age-Related Maculopathy
Allele Frequencies and Hardy-Weinberg Equilibrium
Linkage Analysis
The allele frequencies used in the linkage analyses
were estimated, by direct counting, from the unrelated
and unaffected controls. All controls were unaffected
under all three affection status models. Genotyped
spouses who had no children or who had children who
were not yet part of the study were combined with the
controls for this study. The exact test of Hardy-Weinberg
equilibrium (HWE), implemented in Mega2 (Mukhopadhyay et al. 2005), was performed on our SNPs.
We also used Mendel (version 5) (Lange et al. 2001)
to estimate allele frequencies directly from the family
data, because Mendel properly accounts for relatedness
of the subjects while estimating the allele frequencies.
Since the majority of the genotyped family members
were affected, these estimates were quite close to estimates obtained using our unrelated affected cases.
Two-point analysis.—As in our previous study (Weeks
et al. 2004), we computed LOD scores under a single
simple dominant model (with disease-allele frequency of
0.0001 and penetrance vector of [0.01, 0.90, 0.90]). Because of the complexities and late onset of the ARM
phenotype, only two disease phenotypes were used: “affected under model A” (i.e., “type A–affected”) and “unknown.” Parametric LOD scores were computed under
heterogeneity (HLOD), whereas model-free LOD scores
were computed with the linear Sall statistic. Both scores
were computed using Allegro (Gudbjartsson et al. 2000).
Multipoint analysis ignoring LD.—Since intermarker
distances are often very small, LD between SNPs can be
high and thus violate the assumption of no LD made by
most linkage analysis programs. Multipoint analyses ignoring LD were performed using Allegro (Gudbjartsson
et al. 2000). Both HLOD scores and Sall statistics were
computed. Our main goal in estimating the multipoint
linkage curve without properly accounting for LD was
not to predict the position of ARM-associated loci but
to compare the results with those from analyses in which
LD was taken into account.
Multipoint analysis using htSNPs.—When only htSNPs
were used for LOD score calculation, the number of
SNPs decreased from 679 to 533 on chromosome 1 and
from 196 to 159 on chromosome 10. Multipoint linkage
analyses were done as described elsewhere (Weeks et al.
2004). The SNPs that were omitted fit well into the SNPSNP LD structure estimated by HaploView (Barrett et
al. 2005).
Genetic Map
We used linear interpolation on the Rutgers combined
linkage-physical map (version 2.0) (Kong et al. 2004) to
predict the genetic position of the SNPs that were not
already present in the Rutgers map. Since the distribution of our SNPs was very dense in the regions of interest,
the estimated recombination between several SNPs was
zero; for these, we set the recombination to 0.000001.
We obtained the physical positions for all our SNPs from
the National Center for Biotechnology Information
(NCBI) dbSNP database (human build 35).
LD Structure
Ignoring high linkage disequilibrium (LD) between
SNPs when performing linkage analysis can result in
false-positive findings (Schaid et al. 2002; Huang et al.
2004). Our efforts to take high SNP-SNP LD into account included the following measures. (1) We used the
H-clust method (Rinaldo et al. 2005), which is implemented in R (R Development Core Team 2004; see R
Project for Statistical Computing Web site), to determine
haplotype-tagging SNPs (htSNPs) for linkage analysis.
The method uses hierarchical clustering to cluster highly
correlated SNPs. After the clustering, the H-clust method
chooses a htSNP from each cluster; the htSNP chosen is
the SNP that is most correlated with all other SNPs in
the cluster. We chose to cluster the SNPs so that each
SNP had a correlation coefficient (r2) 10.5 with at least
one htSNP; we used HaploView (Barrett et al. 2005)
to get a graphical view of SNP-SNP LD along both
chromosomes, and we compared LD estimates of htSNPs with SNPs omitted by H-clust. (2) We performed
haplotype-based association analyses using two- and
three-SNP moving windows (see “Association Analysis”
section).
Association Analysis
To incorporate all cases from the families, we used
the new CCREL program (Browning et al. 2005), which
permits testing for association with the use of related
cases and unrelated controls simultaneously. CCREL
was used to analyze SNPs under the linkage peak on
chromosomes 1 and 10, to test for association. The
CCREL test accounts for biologically related subjects by
calculating the effective number of cases and controls.
For these analyses, type A–affected family members were
assigned the phenotype “affected,” unrelated controls
were assigned the phenotype “normal,” and family
members that were not affected with type A ARM were
assigned the phenotype “unknown.” (The CCREL approach has not yet been extended to permit the simultaneous use of both related cases and related controls.)
The effective number of controls for each SNP used for
association testing is therefore the number of controls
genotyped for that SNP. An allelic test, a haplotype test
with a two-SNP sliding window, a haplotype test with
a three-SNP sliding window, and a genotype test were
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Am. J. Hum. Genet. 77:389–407, 2005
Table 2
Summary of Statistical Analyses and Sample Sizes in Parts I–III
Part and Analysis
Set of SNPs, Method, and Sample Used
Results Shown in
I:
htSNP selection
SNP-SNP LD
Linkage
Allele frequencies
CCREL
GIST
CIDR SNPs, 179 controls
CIDR SNPs, 179 controls
CIDR SNPs and htSNPs, 594 ARM-affected families
Mendel v5 for 594 ARM-affected families; counting for 179 controls
CIDR SNPs, 594 ARM-affected families and 179 controls
594 ARM-affected families split into 734 typed nuclear families
…
Figs. 4 and 6
Figs. 3 and 5
Table 5
Table 5
Table 5
Allele frequencies
All SNPs (CIDR and local); Mendel v5 for 323 ARM-affected families;
counting for 117 controls
CIDR SNPs and local SNPs, 323 families and 117 controls
323 ARM-affected families split into 407 typed nuclear families
CIDR and local SNPs, 117 unrelated controls
Table 6
II:
CCREL
GIST
SNP-SNP LD
III:
Interaction by GIST
Logistic regression
OR and AR
OR and AR of subtypes:
CIDR SNPs
Local SNPs
Table 6
Table 6
Fig. 2
See GIST in I and II above
CIDR SNPs, 577 cases and 179 controls
CIDR SNPs, 577 cases and 179 controls; local SNPs, 517 cases (321
familial, 196 sporadic) and 117 controls
Tables 5 and 6
Table 7
Table 8
For CNV, 407 cases and 179 controls; for GA, 184 cases and 179 controls
For CNV, 366 cases and 117 controls; for GA, 159 and 117 controls
Table 9
Table 9
performed. We used the CCREL R package for analysis,
as provided by Browning et al. (2005).
GIST Analysis
To explore which allele/SNP contributes the most to
the linkage signal, we performed the genotype–identity
by descent (IBD) sharing test (GIST) using our locally
genotyped SNPs and significant SNPs from the CCREL
test that are located around the linkage peaks on chromosomes 1 and 10. GIST determines whether an allele,
or another allele in LD with it, accounts in part for the
observed linkage signal (Li et al. 2004). Weights were
computed for each affected sibship under three different
disease models (recessive, dominant, and additive); these
weights are unbiased under the null hypothesis of no
disease-marker association. The correlation between the
family weight variable and the nonparametric linkage
(NPL) score is the basis of the test statistic. Since the
GIST method is currently applicable only to affected sib
pair families, we split our families into their component
nuclear families before computing the NPL scores. Since
we do not know the underlying disease model, we performed tests using three different disease models (recessive, dominant, and additive) and then took the
maximum result, using a P value that was adjusted for
multiple testing over the three models.
Tripartite Analyses
Our analyses were performed in three sequential steps.
First, we analyzed the set of data that had been genotyped at CIDR. Second, after locally genotyping eight
additional SNPs in the PLEKHA1/LOC387715/PRSS11
region on chromosome 10, we then analyzed the locally
genotyped data set. Note that all of the known nonsynonymous SNPs in the region from PLEKHA1 (MIM
607772) through PRSS11 (MIM 602194) were investigated. Because these two data sets differ in size and
composition, it is most straightforward to analyze them
separately (table 2). Allele-frequency estimation, CCREL
association testing, and GIST analysis were performed
on both of these (overlapping) data sets, as described
above. Third, we tested for interaction between the chromosome 1 and chromosome 10 regions and examined
whether or not the risk differed as a function of the
presence of either GA or CNV.
Part I: Analysis of CIDR SNPs
To identify the responsible gene on chromosome
10q26, the CIDR performed high-density custom SNP
genotyping of 612 ARM-affected families and 184 unrelated controls with the use of 199 SNPs spanning 13.4
Mbp (26.7 cM), from rs7080289 through rs6597818
(nucleotide position: 115094788–128517320 bp), which
spans our region of interest. For our analysis, we used
196 SNPs; 3 were skipped because of a lack of polymorphism in the controls (when this was checked within
the family data, the less common allele was extremely
rare and was only present in heterozygotes). In addition,
684 SNPs spanning 45.7 Mbp (47.1 cM) on chromosome 1q31, from rs723858 through rs653734 (nucleotide position: 169749920–215409007 bp), were also
genotyped; 5 SNPs were skipped because of a lack of
polymorphism in the controls—the less common allele
was either not present or very rare and, in the family
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Jakobsdottir et al.: Chromosome 10q26 and Age-Related Maculopathy
data, was only present in heterozygotes. Table 3 shows
the correspondence between our allele labels and the
actual alleles, and, for nonsynonymous SNPs, the amino
acid change.
Table 3
Allele Labeling
The table is available in its entirety in the online
edition of The American Journal of Human Genetics.
Part II: Analysis of Locally Genotyped SNPs
We genotyped eight additional SNPs on chromosome
10 that overlie three susceptibility genes, PLEKHA1
(rs12258692, rs4405249, and rs1045216), LOC387715
(rs10490923, rs2736911, and rs10490924), and PRSS11
(rs11538141 and rs1803403). This genotyping effort
included all of the nonsynonymous SNPs that have
been reported for these genes in the NCBI databases
(see fig. 1). As part of another study (Conley et al.
2005), we genotyped two CFH variants (rs10922093
and rs1061170), which we have used here as well. Genotyping of additional SNPs under the GRK5/RGS10
(MIM 600870/MIM 602856) locus is in process. Genotype data for rs12258692, rs1803403, and the newly
characterized SNP rs4405249 (which is 1 base 3′ of
rs12258692) were collected by sequencing (Rexagen)
and were analyzed using Sequencher software (Gene
Codes). Genotype data for rs11538141, rs2736911,
rs10490923, and rs10490924 were collected using RFLP.
The primers, amplification conditions, and restriction
endonucleases, where appropriate, for SNPs that were
genotyped by sequencing or RFLP can be found in table
4. Genotype data for rs1045216 were collected using a
5′ exonuclease Assay-on-Demand TaqMan assay (Applied Biosystems). Amplification and genotype assignments were conducted using the ABI 7000 and SDS 2.0
software (Applied Biosystems). Two unrelated CEPH
samples were genotyped for each variant and were included on each gel and in each TaqMan tray, to assure
internal consistency in genotype calls. Additionally, double-masked genotyping assignments were made for each
variant and were compared, and each discrepancy was
addressed using raw data or regenotyping. Table 3 shows
the correspondence between our allele labels and the
actual alleles and, for nonsynonymous SNPs, the amino
acid change.
Part III: Interaction and OR Analysis
Unrelated cases.—No unrelated cases were genotyped
by CIDR, but 196 unrelated cases were genotyped locally for our additional SNPs. For computation of odds
ratios (ORs) and for interaction analyses (see below),
we chose to generate a set of unrelated cases by drawing
one type A–affected person from each family. A total of
321 locally genotyped families had at least one type A–
affected person. If a family had more than one type A–
affected person, we chose the person who had the most
complete genotyping at the Y402H variant (rs1061170)
and three CIDR SNPs representative of CFH, GRK5,
and PLEKHA1: rs800292 (CFH), rs1537576 (GRK5),
and rs4146894 (PLEKHA1; rs4146894 also represents
LOC387715, because of high LD with rs10490924) (see
fig. 2). If they could not be distinguished by the number
of genotyped SNPs, we chose the person who developed
the disease at the youngest age, or, if more than one
shared the earliest age at onset, we selected one type A–
affected individual at random from those with the most
SNPs genotyped and the earliest age at onset. A total of
577 CIDR families had at least one type A–affected person; 321 of these families were also genotyped locally,
and the type A–affected person was the same one chosen
for the local set. For the remaining 256 families, we
based our selection on the same criteria described above,
except that only rs800292 (CFH), rs1537576 (GRK5),
and rs4146894 (PLEKHA1) were used to identify the
person with the most complete genotyping.
Analysis of interaction with CFH.—We investigated
possible interaction between CFH on chromosome 1 and
the genes on chromosome 10 by using GIST to test
whether SNPs in CFH are associated with the linkage
signal on chromosome 10 and whether SNPs on chromosome 10 are associated with the linkage signal on
chromosome 1. We did this by using weights from SNPs
on one chromosome and family-based NPLs from the
other.
We also used logistic regression to evaluate different
interaction models and to test for interaction by use of
the approach described by North et al. (2005). In this
approach, many different possible models of the interactions, allowing simultaneously for additive and dominant effects at both of the loci, are fit, and relative
likelihoods of the different models are compared to
draw inferences about the most likely and parsimonious
model. As described elsewhere (North et al. 2005), the
fitted models include a MEAN model, in which only the
mean term is estimated; ADD1, ADD2, and ADD models, which assume an additive effect at one or both loci;
DOM1, DOM2, and DOM models, which additionally
incorporate dominance effects; and three further models, ADDINT, ADDDOM, and DOMINT, which allow
for interactive effects (for more details, see the work of
North et al. [2005]). Since some pairs of these models
are not nested, we compared them by using the Akaike
information criteria (AIC); in this approach, the model
with the lowest AIC is considered to be the best fitting
and the most parsimonious. For these analyses, we used
the program provided by North and colleagues (2005),
after some bugs that we discovered had been fixed; we
double-checked our results with our own R program.
To maximize the sample size, we chose CIDR SNPs in
Figure 1
Location of CIDR SNPs and locally genotyped SNPs with respect to candidate genes. Positions, distances, and nucleotide positions along chromosome 10 are derived from
NCBI Entrez Gene and dSNP databases.
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Jakobsdottir et al.: Chromosome 10q26 and Age-Related Maculopathy
Table 4
Primers, Annealing Conditions, and Restriction
Endonucleases Used for Genotype Data Collection
The table is available in its entirety in the online
edition of The American Journal of Human Genetics.
high LD with a highly significant nonsynonymous SNP
within each gene. The CIDR SNP rs800292 was chosen
to represent rs10611710 (the Y402H variant of CFH),
and the CIDR SNP rs4146894 represented rs1045216
in PLEKHA1. Similarly, we also selected a representative
CIDR SNP in GRK5, RGS10, and PRSS11.
Magnitude of association.—We calculated crude ORs
and estimated ARs for SNPs in each gene. The allele that
was least frequent in the controls was considered to be
the risk allele. AR was estimated using the formula
AR p 100 # P # (OR ⫺ 1)/[1 ⫹ P # (OR ⫺ 1)], where
OR is the OR and P is the frequency of the risk factor
(genotype) in the population, as estimated from the controls. We did this by comparing type A–affected subjects
with controls, comparing subjects who had CNV with
controls, and comparing subjects who had GA with controls. To have the maximum possible sample size, we
used different but overlapping samples for CIDR and
locally typed SNPs. A total of 577 cases selected from
the families and 179 unrelated cases were used for calculating OR and AR of CIDR SNPs, but 517 cases (of
those, 321 are within the 577 CIDR SNP cases) and 117
controls (all within the 179 CIDR SNP controls) were
used for calculating the OR and AR on the locally genotyped SNPs.
Multiple-testing issues.—Since we have very strong
evidence from previous studies that there is an ARMsusceptibility locus in the chromosome 10q26 region,
the analyses performed here were aimed at estimating
the location of the susceptibility gene, rather than testing
a hypothesis. Multiple-testing issues are most crucial and
relevant in the context of hypothesis testing. In estimation, we are simply interested in determining where
the signal is strongest. In any event, any correction for
multiple testing would not alter the rank order of the
results. A Bonferroni correction, which does not account
for any correlation between tests due to LD, for 196
tests at the 0.05 level would lead to a significance threshold of 0.05/196 p 0.00026; correlations due to LD
would lead to a larger threshold.
Results
Our analyses were performed in three sequential steps.
First, we analyzed the set of data that had been genotyped at CIDR. Second, after locally genotyping eight
additional SNPs in the PLEKHA1/LOC387715/PRSS11
region on chromosome 10, we then analyzed the locally genotyped data set. Allele-frequency estimation,
testing for HWE (table 3), CCREL association testing,
and GIST testing were performed on both of these (overlapping) data sets, as described above. Third, we tested
for interaction between the chromosome 1 and chromosome 10 regions and examined whether or not the
risk differed as a function of the presence of GA or CNV.
Part I: Analysis of CIDR SNPs
CIDR linkage results.—The narrow peak of our Sall
linkage curve obtained using the 159 htSNPs on chromosome 10 suggests that there might be an ARM gene
in the GRK5 region (marked “G” in fig. 3, right panel); rs1537576 in GRK5 had a two-point Sall of 1.87,
whereas the largest (across our whole region) two-point
Sall of 3.86 occurred at rs555938, 206 kb centromeric
of GRK5. Several elevated two-point nonparametric
Sall LOD scores and our highest HLOD score drew attention to the PLEKHA1/LOC387715/PRSS11 region
(marked “P” in fig. 3). In this region, SNP rs4146894
in PLEKHA1 had a two-point Sall of 3.34 and the highest
two-point HLOD of 2.66, whereas SNPs rs760336 and
rs763720 in PRSS11 had two-point Sall values of 2.69
and 2.23, respectively. However, the 1-unit support interval is large (10.06 cM) (fig. 3), and so localization
from the linkage analyses alone is rather imprecise.
We also explored the effect of failing to take SNPSNP LD into account, by comparing the multipoint
scores computed using all SNPs (fig. 3, left panel) with
those computed using only the htSNPs (fig. 3, right
panel). Two of the peaks found using all SNPs (referred
to as “false peaks”; marked “F” in fig. 3, left panel)
almost vanish completely when using only htSNPs; interestingly, these two peaks lie within haplotype blocks
(fig. 4A and 4B), whereas the LD around our highest
multi- and two-point LOD scores is low (fig. 4C), indicating the importance of taking LD into account when
performing linkage analysis.
Our linkage results on chromosome 1 gave three
peaks with Sall 1 2, and only one of those peaks was
observed when we restricted our analysis to htSNPs (fig.
5). This remaining peak overlies CFH and includes two
SNPs with very high two-point Sall and HLOD scores:
rs800292, a nonsynonymous SNP in CFH, had an Sall
of 1.53 and an HLOD of 2.11, whereas SNP rs1853883,
165 kb telomeric of CFH, had an Sall of 4.06 and an
HLOD of 3.49. These results strongly support earlier
findings of CFH’s involvement in ARM (Conley et al.
2005; Edwards et al. 2005; Hageman et al. 2005; Haines
et al. 2005; Klein et al. 2005; Zareparsi et al. 2005a).
The vanishing peaks (marked “F” in fig. 5, left panel)
that we saw when we used all of our SNPs in the linkage
analysis are located within strong haplotype blocks (fig.
6A and 6B), whereas the LD under the CFH peak is
relatively low (fig. 6C).
CIDR association results.—For finer localization than
Figure 2
A, LD patterns in GRK5 (Block 1), RGS10 (SNP 6), PLEKHA1 (Block 2), LOC387715 (Block 3), and PRSS11 (Block 4). B,
LD patterns in CFH (Block 1). Squares shaded pink or red indicate significant LD between SNP pairs (bright red indicates pairwise D′ p 1),
white squares indicate no evidence of significant LD, and blue squares indicate pairwise D′ p 1 without statistical significance. Significant SNPs
from the CCREL allele test are highlighted in green (see table 6). Three SNPs (rs6428352, rs12258692, and rs11538141) were not included,
because of very low heterozygosity, and one SNP (rs2736911) was not included, because it was uninformative. Note that the blocks were drawn
to show clearly the position of the genes and do not represent haplotype blocks.
397
Jakobsdottir et al.: Chromosome 10q26 and Age-Related Maculopathy
can be obtained by linkage, we turned to association
analyses (which were very successful in discovering CFH
on chromosome 1). Here, we performed association
analyses using the CCREL approach (Browning et al.
2005), which permitted the simultaneous use of our
unrelated controls and all of our related familial cases
by appropriately adjusting for the relatedness of the
cases. In the CIDR sample on chromosome 10, within
our linkage peak, we found a cluster of four adjacent
SNPs with very small P values (rs4146894, rs1882907,
rs760336, and rs763720) that overlies three genes:
PLEKHA1, LOC387715, and PRSS11. Our strongest
CCREL results on chromosome 10 were for SNP
rs4146894 in PLEKHA1 (table 5). The moving-window
haplotype analyses using three SNPs at a time resulted
in very small P values across the whole PLEKHA1 to
PRSS11 region (table 5). The association testing also
generated some moderately small P values in the GRK5
region, which is where our highest evidence of linkage
occurred.
We performed the CCREL on 56 SNPs spanning the
linkage peak on chromosome 1 and found two highly
significant SNPs (rs800292 and rs1853883) that overlie
CFH (table 5). The moving-window haplotype analyses,
performed using two and three SNPs at a time, resulted
in extremely low P values across the whole CFH gene
(table 5), which supports earlier findings of strong association between CFH and ARM.
CIDR GIST results.—When GIST was performed on
the CIDR data set, the two smallest P values in chromosome 10q26 (.006 and .004) occurred in the GRK5/
RGS10 region, whereas the third smallest P value (.008)
occurred in PLEKHA1 (table 5). All four SNPs in the
GRK5 gene have small GIST P values. The GIST results
suggest that both GRK5 and PLEKHA1 contribute significantly to the linkage signal on chromosome 10 and
that CFH contributes to the linkage signal on chromosome 1. Neither of the two SNPs in PRSS11 contributes
significantly to the linkage signal on chromosome 10.
There was no evidence that the genes on chromosome
10 were related to the linkage signal seen on chromosome 1.
PART II: Analysis of Locally Genotyped SNPs
Local association results.—After additional SNPs were
typed locally, the allele and genotype test generated
extremely small P values for each of the three genes
PLEKHA1, LOC387715, and PRSS11 (table 6). The
moving-window haplotype analyses with three SNPs resulted in very small P values across the entire PLEKHA1/
LOC387715/PRSS11 region (table 6). Thus, although
association implicates the PLEKHA1/LOC387715/
PRSS11 region, it does not distinguish between these
genes.
Local GIST results.—Of the three genes PLEKHA1,
LOC387715, and PRSS11, GIST most strongly implicated PLEKHA1 (table 6). It also generated a small P
value for rs10490924 in LOC387715, but this SNP is
in high LD with the PLEKHA1 SNPs (see fig. 2A). When
the locally typed data set was used, GIST did not generate any significant results for PRSS11, similar to the
nonsignificant results observed in the larger CIDR sample. This implies that PLEKHA1 (or a locus in strong
LD with it) is the most likely to be involved in ARM,
and therefore LOC387715 remains a possible candidate
locus.
For a fair assessment of which SNP accounts for the
linkage signal across the region, the NPLs were computed using only the locally genotyped families. This
permitted us to compare the PLEKHA1/LOC387715/
PRSS11 results (table 6) directly with the GRK5/RGS10
results. For the locally typed data set, the GIST results
for GRK5 are also interesting, with modest P values of
the same magnitude as the P values we got from applying
GIST to CFH (table 6). However, note that the P values
are not as small as those seen when the CIDR data set
was analyzed. Since all of the SNPs in the GRK5 region
are CIDR SNPs, this difference is solely a function of
sample size, because the locally typed data set is smaller
than the CIDR data set (see table 2).
Part III: Interaction and OR Analyses
GIST results.—We did not see any strong evidence of
an interaction between the chromosome 1 and chromosome 10 regions, by use of GIST. When the CIDR
data set was used to test whether SNPs on chromosome
10 contribute to the linkage signal on chromosome 1
(see GIST, NPL 1, in table 5), only rs763720 in PRSS1
gave a P value !.05; however, rs763720 does not contribute significantly to the linkage signal on chromosome
10, which makes this P value less convincing. When we
used the local data set, one GRK5 variant (rs1537576),
which was not significant in the larger CIDR data set,
gave a P value !.05. Similarly, we did not see evidence
that SNPs within CFH contribute to the linkage signal
on chromosome 10; only one SNP (rs955927) gave a P
value !.05—this SNP, however, is not in the CFH gene
and is not in strong LD (see fig. 2B) with any SNPs in
CFH.
Logistic regression results.—The logistic regression results (table 7) suggest that an additive model including
the variants from CFH and PLEKHA1 is the best model
for predicting case-control status; this indicates that both
genes are important to the ARM phenotype. The AIC
criteria also suggest that an additive model including
an additive interaction term is the next best model (table 7); however, the interaction term is not significant
(P p .71). We obtain similar results for interaction be-
399
Jakobsdottir et al.: Chromosome 10q26 and Age-Related Maculopathy
tween CFH and PRSS11, where the additive model including both variants appears to be the best model.
Within the GRK5/RGS10 region, a model with the CFH
SNP alone is the best-fitting model, which suggests that
the prediction of case-control status with CFH genotype
does not improve by the addition of either the GRK5
or RGS10 variant to the model.
OR and AR.—We estimated the magnitude of association by calculating OR and AR values; the significant
associations we saw (table 8) are, not surprisingly, consistent with the results from the CCREL tests in parts I
and II. Our two most significant SNPs in the PLEKHA1/
LOC387715 region are SNPs rs4146894 (PLEKHA1)
and rs10490924 (LOC387715); the two tests are highly
correlated because the LD between those SNPs is very
high (D ′ p 0.93) (see fig. 2A). The third most significant
SNP (rs1045216) in the chromosome 10 region is a nonsynonymous SNP in PLEKHA1 and in high LD with
both rs4146894 (D ′ p 0.97) and rs10490924 (D ′ p
0.91).
We obtained results and OR and AR values (table 8)
similar to those that others have reported for the CFH
gene. The three most significant SNPs were rs1061170
(Y402H variant), rs800292 (in CFH), and rs1853883
(in strong LD with rs1061170; D ′ p 0.91).
The magnitude of the association we saw within
PLEKHA1/LOC387715 is very similar to the level of
association seen between CFH and ARM; both loci result in extremely low P values (P ! .0001). The OR and
AR values were also similar—the dominant OR was
5.29 (95% CI 3.35–8.35) within CFH and 5.03 (95%
CI 3.2–7.91) within PLEKHA1/LOC387715, and the
dominant AR for CFH and PLEKHA1/LOC387715
was 68% and 57%, respectively.
Subphenotype analyses.—We estimated ORs and ARs
for patients with exudative disease versus controls and
for patients with GA versus controls (table 9). ORs and
corresponding P values yielded similar findings to those
of the allele test of CCREL (tables 5 and 6). We found
no major differences between the ORs for the presence
of either GA or CNV.
Discussion
Our linkage studies of families with ARM have consistently identified the chromosome 1q31 and chromosome
10q26 loci, in addition to several other loci. Multiple
linkage studies have replicated this finding; thus, we undertook a focused SNP analysis of both regions, using
Figure 3
Figure 4
LD patterns on chromosome 10 based on analysis of
196 CIDR SNPs and 179 unrelated controls. The legend is available
in its entirety in the online edition of The American Journal of Human
Genetics.
ARM-affected families as well as unrelated affected individuals and controls. We confirmed the strong association of chromosome 1q31 with CFH that has been
reported by others (see also Conley et al. [2005]), and
we have shown, for the first time, that SNPs in CFH
significantly account for the linkage signal. Interestingly,
our smallest GIST P value (!.001) was for rs1853883
(which has a high D′ of 0.91 with the Y402H variant)
and not for the presumed “disease-associated” Y402H
variant itself. This raises the possibility that we may still
have to consider other possible ARM-related variants
within the CFH gene and that these may be in high LD
with Y402H.
Our studies of chromosome 10q26 have implicated
two potential loci: (1) a very strongly implicated locus
that includes three tightly linked genes, PLEKHA1,
LOC387715, and PRSS11, and (2) a less strongly implicated locus comprising two genes, GRK5 and RGS10
(fig. 1). The GIST analysis does not support PRSS11 as
the ARM-related gene, but it does not completely exclude it as a potential candidate. PLEKHA1 has the
lowest GIST-derived P values, whereas LOC387715
harbors the SNP with the strongest association signal
and the highest ORs. With the high LD between SNPs
in LOC387715 and PLEKHA1, one cannot clearly distinguish between these genes by statistical analyses
alone. However, it is clear that the magnitude of the
impact of the PLEKHA1/LOC387715 locus on ARM
is comparable to that which has been observed for the
CFH locus. As in recent studies (Edwards et al. 2005;
Haines et al. 2005; Klein et al. 2005), we have found,
in our case-control population, that the CFH allele (either heterozygous or homozygous) accounts for an OR
of 5.3 (95% CI 3.4–8.4) and a significant population
AR of 68%. In the same fashion, the high-risk allele
within the PLEKHA1/LOC387715 locus accounts for
an OR of 5.0 (95% CI 3.2–7.9) and an AR of 57%
when both heterozygous and homozygous individuals
are considered. As noted by Klein et al. (2005), unless
Two-point (2pt) and multipoint (mpt) linkage results on chromosome 10. The panel on the left summarizes the results when
all SNPs were used for analysis. The panel on the right summarizes the results when only htSNPs were used. The peaks marked “F” represent
likely false peaks due to high SNP-SNP LD, whereas the peaks marked “G” and “P” correspond to the loci containing GRK5 and PLEKHA1,
respectively. The horizontal lines indicate the 1-unit support interval of multipoint Sall (i.e., maximum Sall ⫺ 1).
401
Jakobsdottir et al.: Chromosome 10q26 and Age-Related Maculopathy
the disease is very rare, the OR determined from a casecontrol study will usually overestimate the equivalent
relative risk. Estimates of AR based on ORs for common
genetic disorders can misrepresent the extent to which
a variant accounts for the population AR. However, if
this caution is kept in mind, it is still useful for us to
present AR values to allow for relative comparisons and
to allow the reader to appreciate that the potential impact of the CFH Y402H variant on ARM is comparable
to that of the variants observed in the PLEKHA1/
LOC387715 locus.
In the case of CFH on chromosome 1, the association
data were extremely compelling for a single gene, even
though CFH is within a region of related genes. In addition to the association data found by multiple independent groups, there is additional biological data to
implicate CFH, including localization of the protein
within drusen deposits of patients with ARM. Thus, we
also must consider the biological relevance of the potential ARM-susceptibility genes identified by our studies of chromosome 10q26.
As noted above, the GIST analysis most strongly implicated PLEKHA1, particularly when we included the
additional nonsynonymous SNPs that we genotyped locally. PLEKHA1 encodes the protein TAPP1, which is
a 404-aa protein with a putative phosphatidylinositol
3,4,5-trisphosphate-binding motif (PPBM), as well as
two plectstrin homology (PH) domains. The last three
C-terminal amino acids have been predicted to interact
with one or more of the 13 PDZ domains of MUPP1
(similar to the PDZ domain within PRSS11). Dowler
and colleagues (2000) have shown that the entire TAPP1
protein, as well as the C-terminal PH domain, interacts
specifically with phosphatidylinositol 3,4-bisphosphate
(PtdIns(3,4)P2) but not with any other phosphoinositides. TAPP1, which has 58% identity with the first
300 aa of TAPP2, shows a fivefold higher affinity for
PtdIns(3,4)P2 than does TAPP2, and this binding is
nearly eliminated by mutation of the conserved arginine
212 to leucine within the PPBM region (which is part
of the second PH domain). The most well-defined role
for TAPP1 (and its relatives, Bam32 and TAPP2) has
been as an activator of lymphocytes. PtdIns(3,4)P2 is
preferentially recruited to cell membranes when lipid
phosphatase (SHIP) is activated along with PI3K (phosphatidyl inositol 3-kinase). SHIP is responsible for the
dephosphorylation of PIP3 to PtdIns(3,4)P2. SHIP is
a negative regulator of lymphocyte activation, and
thus TAPP1 and TAPP2 may be crucial negative regu-
Figure 5
Figure 6
LD patterns on chromosome 1 based on analysis of
679 CIDR SNPs and 179 unrelated controls. The legend is available
in its entirety in the online edition of The American Journal of Human
Genetics.
lators of mitogenic signaling and of the PI3K signaling
pathway. Thus, one can envision a role in the eye for
PLEKHA1 and its protein, TAPP1, in modifying local
lymphocyte activation, consistent with the hypothesis
that ARM is closely linked to an inflammatory process.
However, we need to still consider the biological plausibility of the other two candidate genes within this
locus, LOC387715 and PRSS11. Little is known regarding the biology of LOC387715, except that its expression appears to be limited to the placenta. Our own
reverse transcription experiments with human retinal
RNA have confirmed the expression of PLEKHA1 and
PRSS11, but we have not detected LOC387715 transcripts in the retina under standard conditions, even
though we confirmed its expression with placental RNA
(data not shown). However, we cannot exclude the possibility that LOC387715 is expressed at very low levels
in the retina or retinal pigment epithelium or that its
expression in nonocular tissues, such as dendritic cells
or migrating macrophages, could be a factor in the pathogenesis of ARM.
PRSS11 is one of the genes of the mammalian high
temperature requirement A (HtrA) serine protease family, which has a highly conserved C-terminal PDZ domain (Oka et al. 2004). These secretory proteases were
initially identified because of their homologies to bacterial forms that are required for survival at high temperatures and molecular chaperone activity at low
temperatures. The ATP-independent serine protease activity is thought to degrade misfolded proteins at high
temperatures. The mammalian form, HtrA1, has been
shown to be selectively stimulated by type III collagen
alpha 1 C propeptide, in contrast to HtrA2 (Murwantoko et al. 2004). Type III collagen is a major constituent
(35%–39% of the total collagen) in Bruch membrane
and is also present in small amounts in the retinal microvascular basement membranes. Developmental studies have reported ubiquitous expression of HtrA1, but
with temporal and spatial specificities that coincide with
Multipoint linkage results for chromosome 1. The panel on the left summarizes results when all SNPs were used for analysis,
and the panel on the right summarizes results when only htSNPs were used. The peaks marked “F” represent likely false peaks due to high
SNP-SNP LD, whereas the peak marked “C” corresponds to the CFH gene. The horizontal lines indicate the 1-unit support interval of multipoint
Sall (i.e., maximum Sall over CFH ⫺ 1).
402
Am. J. Hum. Genet. 77:389–407, 2005
Table 5
CCREL, GIST, and Allele-Frequency Estimation for Families and Controls Typed at CIDR
P VALUE
FREQUENCY
SNP
rs6658788
rs1538687
rs1416962
rs946755
rs6428352
rs800292
rs70620
rs1853883
rs1360558
rs955927
rs4350226
rs4752266
rs915394
rs1268947
rs1537576
rs2039488
rs1467813
rs927427
rs4146894
rs1882907
rs760336
rs763720
GENE
CFH
CFH
GRK5
GRK5
GRK5
GRK5
RGS10
PLEKHA1
PRSS11
PRSS11
FOR
TEST
Moving-Window
Haplotype Test
IN
GIST
Families
(n p 594)
Controls
(n p 179)
Allele
Test
With 2 SNPs
With 3 SNPs
Genotype
Test
NPL 10
NPL 1
.460
.234
.321
.317
.001
.132
.147
.630
.425
.416
.055
.489
.307
.352
.344
.003
.232
.173
.489
.397
.391
.095
.37312
.00178
.16378
.20073
…
!.00001
.15602
!.00001
.34842
.36201
.00182
.01616
.00206
.39256
.20147
!.00001
!.00001
!.00001
!.00001
.60377
.00833
…
.00778
.00674
.4157
!.00001
!.00001
!.00001
!.00001
!.00001
.01118
…
…
.44415
.0054
.38009
.37434
…
!.00001
.33122
!.00001
.63012
.65613
.00183
.106
.781
.566
.513
…
.437
.893
.521
.183
.065
.171
.055
.129
.019
.012
…
.001
.333
!.001
.296
.145
.242
.220
.214
.112
.507
.078
.286
.223
.187
.117
.433
.115
.293
.84131
.15214
.97426
.01881
.01339
.63177
.23223
.19235
.00969
.01354
.07877
…
.28973
.00309
.01031
.03257
…
…
.03802
.35594
.97976
.0295
.05075
.71857
.088
.028
.052
.006
.004
.539
.475
.643
.345
.251
.609
.582
.514
.598
.127
.395
.295
.464
.466
.187
.480
.212
.00002
.00001
.00006
…
…
.05976
.198
.008
.169
.232
.198
.577
.802
.172
.581
.021
.06936
.00003
!.00001
!.00001
.00261
.00469
.00053
.00013
.00126
…
!.00001
.00521
.02036
.00290
NOTE.—The minor-allele frequency is reported for controls (estimated by counting) and families (estimated by Mendel, version
5). Allele frequencies that differed between controls and families by 10.1 are in bold italics. For the allele test, the two-SNP and
three-SNP moving-window haplotype tests, and the genotype test from the CCREL, P values ⭐.05 are in bold italics and P values
⭐.001 are underlined. The moving-window haplotype P values correspond to the SNPs in the same row as the P value and the next
one or two SNPs for the two- and three-SNP moving window, respectively. For GIST, with the use of NPL scores from chromosome
1 (NPL 1) and chromosome 10 (NPL 10), P values ⭐.05 are in bold italics and P values ⭐.001 are underlined. Blank spaces separate
the three chromosomal regions corresponding to SNPs in and around CFH, GRK5/RGS10, and PLEKHA1/LOC687715/PRSS11.
those regions in which Tgfb proteins play a regulatory
role (De Luca et al. 2004). Oka and colleagues (2004)
have shown that HtrA1 is capable of inhibiting the signaling of a number of Tgfb family proteins, including
Bmp4, Bmp2, and Tgfb1, presumably by preventing receptor activation with a requirement for protease activity of the HtrA1 molecule. One clue as to the potential importance of these relationships for ARM comes
from the studies of Hollborn et al. (2004), who found
that human RPE cells in vitro experienced reduced proliferation in the presence of Tgfb1 and Tgfb2 and an
increase in levels of collagen III and collagen IV transcripts. Normally, a rise in collagen III would activate HtrA1 and would lead to secondary inhibition of
the effects of Tgfb1. However, if the serine protease is
less effective (because of either reduced synthesis or a
nonfunctional mutation), then this regulatory pathway
would be disrupted, leading to an overall reduction in
the proliferation potential of the RPE cells, perhaps contributing to RPE atrophy or further changes that could
lead to the development of ARM. The gradual reduction
in solubility of type III collagen in Bruch membrane that
has been observed with aging could also, in part, account for a general reduction in HtrA1 activity as an
individual ages.
Both PRSS11 and PLEKHA1 are expressed in the
retina, and a SAGE analysis of central and peripheral
retina (Gene Expression Omnibus [GEO] expression
data) indicates higher levels of transcripts of both genes
in the central macula (more so for PLEKHA1 than for
PRSS11). Multiple studies (reported in GEO profiles)
have shown that PLEKHA1 expression is significantly
induced in a variety of cell types in response to exposure
to specific inflammatory cytokines. PRSS11 has also
been investigated as part of a microarray expression
analysis of dermal fibroblasts that have been oxidatively
challenged, in a comparison between normal individuals and patients with ARM. In that study, half of the
ARM samples (9 of 18) had lower Htra1 expression levels than any of the normal samples. The lower levels
403
Jakobsdottir et al.: Chromosome 10q26 and Age-Related Maculopathy
Table 6
CCREL, GIST, and Allele-Frequency Estimation for Locally Typed Families and Controls
P VALUE
FREQUENCY
SNP
rs6658788
rs1538687
rs1416962
rs946755
rs6428352
rs800292
rs1061170
rs10922093
rs70620
rs1853883
rs1360558
rs955927
rs4350226
rs4752266
rs915394
rs1268947
rs1537576
rs2039488
rs1467813
rs927427
rs4146894
rs12258692
rs4405249
rs1045216
rs1882907
rs10490923
rs2736911
rs10490924
rs11538141
rs760336
rs763720
rs1803403
GENE
CFH
CFH
CFH
CFH
GRK5
GRK5
GRK5
GRK5
RGS10
PLEKHA1
PLEKHA1
PLEKHA1
PLEKHA1
LOC387715
LOC387715
LOC387715
PRSS11
PRSS11
PRSS11
PRSS11
FOR
TEST
Moving-Window
Haplotype Test
IN
GIST
Families
(n p 323)
Controls
(n p 117)
Allele
Test
With 2 SNPs
With 3 SNPs
Genotype
Test
NPL 10
NPL 1
.563
.213
.299
.295
.001
.120
.609
.210
.148
.633
.437
.433
.050
.483
.342
.393
.380
.004
.269
.310
.295
.150
.432
.389
.385
.103
.02200
.00004
.00597
.01234
…
!.00001
!.00001
.00693
.91163
!.00001
.18014
.15343
.00312
.00052
.00043
.02623
.01243
!.00001
!.00001
!.00001
.00175
!.00001
!.00001
.43576
.01037
…
.00162
.00066
.02051
!.00001
!.00001
!.00001
!.00001
!.00001
!.00001
!.00001
.02079
…
…
.04920
.00014
.01819
.04531
…
!.00001
!.00001
.01723
.56770
!.00001
.37993
.36087
.00373
.319
.652
.442
.409
…
.315
.895
.360
.737
.776
.975
.017
.228
.244
.302
.041
.040
…
.014
.132
.327
.356
.011
.488
.585
.174
.223
.228
.117
.497
.083
.293
.226
.209
.115
.419
.115
.295
.81772
.34489
.81975
.02604
.11177
.86608
.27748
.83219
.02748
.02232
.42428
…
.64917
.05560
.02192
.05636
…
…
.08279
.62183
.78965
.06334
.42399
.85954
.107
.049
.049
.012
.025
.506
.453
.320
.689
.023
.358
.492
.506
.611
.008
.139
.289
.131
.089
.121
.475
.004
.373
.296
.118
.487
.474
.000
.158
.427
.184
.141
.119
.193
.005
.474
.226
.030
.56710
.00004
…
.39378
.00004
.01761
.02112
.71668
!.00001
…
.00527
.01645
.00009
.00056
.00012
.54750
.00026
.00036
.00140
.05024
!.00001
!.00001
.00726
.01386
.00016
…
.00083
.00053
.00018
.00280
.00001
.01099
!.00001
!.00001
!.00001
.01676
.00036
…
…
.42264
.00024
…
.33118
.00026
.04401
.03415
.64230
!.00001
…
.01396
.03899
.00022
.306
.006
…
.003
.068
.017
.086
.312
.018
…
.479
.305
.714
.625
.737
…
.345
.825
.372
.251
.968
.327
…
.683
.451
.778
NOTE.—The minor-allele frequency is reported for controls (estimated by counting) and families (estimated by Mendel, version 5).
Allele frequencies that differed between controls and families by 10.1 are in bold italics. For the allele test, the two-SNP and three-SNP
moving-window haplotype tests, and the genotype test from the CCREL, P values ⭐.05 are in bold italics and P values ⭐.001 are
underlined. The moving-window haplotype P values correspond to the SNPs in the same row as the P value and the next one or two
SNPs for the two- and three-SNP moving window, respectively. For GIST, with the use of NPL scores from chromosome 1 (NPL 1)
and chromosome 10 (NPL 10), P values ⭐.05 are in bold italics and P values ⭐.001 are underlined. Locally typed SNPs are in bold
italics. Blank spaces separate the three chromosomal regions corresponding to SNPs in and around CFH, GRK5/RGS10, and PLEKHA1/
LOC687715/PRSS11.
of Htra1 in nonocular tissues of patients with ARM
would suggest that this is an intrinsic difference in the
biology of these patients, compared with that of normal
individuals, and is not a consequence of degenerative
changes in the eye.
Several lines of evidence support the GRK5/RGS10
locus. The peak of our Sall multipoint curve is directly
over GRK5, and our largest two-point Sall p 3.86
(rs555938) is only 206 kb centromeric of GRK5. The
P values for the GIST analysis of the GRK5/RGS10
CIDR data were .004 and .006, which are even smaller
than the P value for the SNP within PLEKHA1 (P p
.008). By use of our locally genotyped sample, the GIST
P value for the GRK5 locus was .012, which is comparable to the P value that we found for the Y402H
variant in CFH (P p .011). However, the CCREL analyses were not very significant for the GRK5 SNPs, and
the ORs were mostly nonsignificant.
On the basis of biological evidence, GRK5 is a reasonable ARM candidate gene, given its role in modulating neutrophil responsiveness to chemoattractants
and its interactions with the Toll 4 receptor (Haribabu
404
Am. J. Hum. Genet. 77:389–407, 2005
value. However, we cannot completely exclude the possibility that there is a SNP within RGS10 that is in
strong LD with rs2039488.
RGS10 is one of a family of G protein–coupled receptors that has been implicated in chemokine-induced
lymphocyte migration (Moratz et al. 2004) and whose
expression in dendritic cells (which have been identified
in ARM-related drusen deposits) is modified by the Tolllike signaling pathway (Shi et al. 2004). RGS10 and
GRK5 expression in the same microarray study of oxidatively stressed dermal fibroblasts in patients with
ARM and control subjects showed minor fluctuations
among the samples but no clear differences between the
controls and affected individuals. This does not necessarily lower the potential for these genes being involved
in ARM, since the dermal fibroblasts lack the cell populations that would be expected to have modulation of
RGS10- and/or GRK5-related proteins.
Table 7
Results of Fitting Two-Locus Models by Logistic
Regression
The table is available in its entirety in the online
edition of The American Journal of Human Genetics.
and Snyderman 1993; Fan and Malik 2003), which has
also been implicated in ARM (Zareparsi et al. 2005b).
The retinal or RPE expression of GRK5 is not especially
relevant to the argument of causality, because it would
be the expression and function of GRK5 in migrating
lymphocytes and macrophages that would be crucial to
its role in the immune and/or inflammatory pathways
that may be pathogenic in ARM. The strongest GIST
results occur at rs2039488, which is located between
GRK5 and RGS10, 3′ of the ends of both genes. Several
other SNPs within GRK5 also have small GIST P values,
whereas the RGS10 SNP has a nonsignificant GIST P
Table 8
ORs, ARs, and Simulated P Values from x2 Test with 10,000 Replicates
DOMINANT
([RR⫹RN] VS. NN)
SNP (ALLELE)
rs6658788 (2)
rs1538687 (2)
rs1416962 (2)
rs946755 (2)
rs6428352 (2)
rs800292 (1)
rs1061170 (2)
rs10922093 (1)
rs70620 (1)
rs1853883 (2)
rs1360558 (1)
rs955927 (2)
rs4350226 (2)
rs4752266 (2)
rs915394 (2)
rs1268947 (2)
rs1537576 (2)
rs2039488 (2)
rs1467813 (1)
rs927427 (1)
rs4146894 (1)
rs12258692 (2)
rs4405249 (1)
rs1045216 (2)
rs1882907 (2)
rs10490923 (2)
rs2736911 (2)
rs10490924 (2)
rs11538141 (2)
rs760336 (2)
rs763720 (1)
rs1803403 (1)
GENE
CFH
CFH
CFH
CFH
GRK5
GRK5
GRK5
GRK5
RGS10
PLEKHA1
PLEKHA1
PLEKHA1
PLEKHA1
LOC387715
LOC387715
LOC387715
PRSS11
PRSS11
PRSS11
PRSS11
HETEROZYGOTES
(RR VS. NN)
(RR
RECESSIVE
[RN⫹NN])
HOMOZYGOTES
(RR VS. NN)
VS.
OR
95% CI
AR
P
OR
AR
OR
95% CI
AR
P
OR
AR
.83
.68
.84
.8
…
.43
5.29
.59
.83
2.67
1.16
1.13
.51
.57–1.22
.49–.95
.6–1.18
.57–1.13
…
.3–.62
3.35–8.35
.39–.88
.57–1.19
1.78–4.01
.82–1.65
.79–1.6
.32–.81
⫺14.04
⫺19.38
⫺10.02
⫺12.52
…
⫺30.01
68.2
⫺25.61
⫺5.64
54.41
9.12
7.5
⫺9.68
.3909
.023
.3418
.232
…
!.0001
!.0001
.0111
.3366
!.0001
.414
.5303
.0038
1.09
.5
.89
1
…
.48
2.66
.63
.85
1.65
1.1
1.28
.27
2.69
⫺11.74
⫺2.57
.04
…
⫺23.85
28.55
⫺19.65
⫺4.29
19.21
5.39
6.35
⫺4.76
1.01
.42
.82
.9
…
.15
4.57
.5
.67
2.08
1.25
1.31
.16
.68–1.5
.23–.78
.49–1.38
.53–1.52
…
.05–.45
2.48–8.42
.24–1.04
.27–1.68
1.43–3.02
.8–1.96
.83–2.08
.01–1.74
.21
⫺6.52
⫺2.31
⫺1.24
…
⫺4.98
30.06
⫺4.98
⫺1.3
22.06
3.94
4.53
⫺.95
1
.0068
.5002
.7816
…
.0001
!.0001
.0736
.4525
.0003
.3774
.2588
.142
.88
.38
.77
.81
…
.12
10.05
.41
.64
3.55
1.32
1.36
.14
⫺5.92
⫺12.42
⫺5.74
⫺4.34
…
⫺8.19
63.72
⫺10.14
⫺1.93
55.04
9.01
9.38
⫺1.16
.88
1.28
1.05
1.59
.7
.96
.62–1.23
.9–1.82
.7–1.57
1.11–2.29
.45–1.07
.69–1.35
⫺5.57
8.91
1.06
27.95
⫺6.5
⫺1.84
.4325
.1543
.841
.0109
.1067
.8645
3.27
1.35
1.24
.89
.23
1.01
10.68
2.73
1.82
⫺3.74
⫺11.98
.42
2.81
1.56
1.27
1.08
.19
.77
.98–8.04
.58–4.14
.35–4.55
.71–1.62
.04–.79
.42–1.38
3.89
1.53
.45
1.59
⫺2.33
⫺2.27
.0457
.3892
.7761
.7579
.0242
.4265
2.56
1.68
1.28
1.47
.18
.77
5.51
2.72
.58
15.14
⫺2.85
⫺3.76
1.09
2.22
…
.62
.48
.58
.53
.72
5.03
…
.64
1.69
2.98
.74–1.62
1.49–3.31
…
.33–1.15
.32–.74
.4–.84
.31–.9
.42–1.21
3.2–7.91
…
.44–.93
1.2–2.38
1.25–7.06
6.57
46.78
…
⫺12.96
⫺51.23
⫺16.73
⫺13.27
⫺6.92
57.11
…
⫺35.37
21.24
10.51
.6172
.0002
…
.1692
.0005
.0026
.0239
.2552
!.0001
…
.013
.0018
.0093
.94
1.77
…
.61
.49
.44
.34
1.47
2.72
…
.8
1.55
2.98
⫺4.66
33.08
…
⫺12.69
⫺18.27
⫺5.79
⫺9.01
1.99
22.76
…
⫺6.95
16.95
10.51
1.67
2.21
…
.87
.37
.31
.22
1.1
5.75
…
.69
2.63
…
1.09–2.56
1.49–3.29
…
.1–7.56
.21–.65
.1–.97
.04–1.09
.13–9.53
2.46–13.46
…
.46–1.03
1.1–6.25
…
10.73
20.46
…
⫺.23
⫺14.3
⫺2.37
⫺2.51
.1
21.2
…
⫺7.95
5.17
…
.0201
1.6
3.31
…
.77
.28
.27
.2
1.03
10.57
…
.55
3.16
…
19.91
49.88
…
⫺.57
⫺35.68
⫺3.65
⫺3.32
.04
42.71
…
⫺26.43
10.14
…
!.0001
…
1
.0003
.0438
.0809
1
!.0001
…
.0773
.0277
…
NOTE.—Type A–affected individuals are compared with controls. OR and AR values are underlined if corresponding P values are ⭐.001 and are in bold italics
if P values are ⭐.05. Allele denotes the risk allele (minor allele in controls). RR p homozygotes for the risk allele; RN p heterozygotes for the risk allele; NN p
homozygotes for the normal allele. Locally typed SNPs are in bold italics. Blank spaces separate the three chromosomal regions corresponding to SNPs in and
around CFH, GRK5/RGS10, and PLEKHA1/LOC687715/PRSS11.
405
Jakobsdottir et al.: Chromosome 10q26 and Age-Related Maculopathy
Table 9
ORs and ARs from Analysis of ARM Subtypes
The table is available in its entirety in the online
edition of The American Journal of Human Genetics.
We have attempted to look at potential interactions
between the high-risk alleles within the PLEKHA1/
LOC387715 and GRK5/RGS10 loci with respect to
CFH on chromosome 1. This is perhaps the first report
to use GIST to examine these interactions, and we found
no evidence that the NPL data on chromosome 1 could
be accounted for by the SNP data on chromosome 10.
Conversely, we found no such associations between the
NPL data on chromosome 10 and the SNP data from
the CFH alleles. Logistic regression analysis also failed
to identify an interaction, and it appears that a simple
additive risk model is the most parsimonious. We have
performed some initial logistic analyses that include
exposure to smoking. These analyses were initiated because of the previous suggestion of an interaction between smoking and the biology of complement factor H (Esparza-Gordillo et al. 2004) and because of
our prior studies, which found an interaction between
smoking and the locus on chromosome 10q26 (Weeks
et al. 2004). To date, we have found no strong interaction between smoking and either CFH or PLEKHA1/
LOC387715, but we are still exploring a possible interaction with the GRK5/RGS10 locus and different
modeling strategies.
We also examined the associations of ARM subphenotypes with the SNPs on chromosomes 1 and 10
(table 9). We found no major differences in the ORs for
the presence of either GA or CNV, which suggests that
these ARM loci contribute to a common pathogenic
pathway that can give rise to either end-stage form of
the disease. This does not exclude the possibility that
there are other as-yet-undescribed genetic loci that
may confer specific risk of GA or CNV development
separately.
In summary, these SNP-based linkage and association studies illustrate both the power and the limitation
of such methods to identify the causative alleles and
genes underlying ARM susceptibility. These genetic approaches allow us to consider genes and their variants
that may contribute to disease, whether or not there is
tissue-specific expression. Through high-density SNP
genotyping, we have narrowed the list of candidate
genes within the linkage peak found on chromosome
10q26, from hundreds of genes to primarily GRK5 and
PLEKHA1, but we cannot completely exclude possible
roles for RGS10 and/or PRSS11 and LOC387715. Additional genotyping of nonsynonymous 3′ SNPs within
the GRK5 gene may help to further discriminate between GRK5 and RGS10, but it may not establish a
definitive assignment of causality. Replication by other
studies (as done in the case of CFH) may allow the
attention to be focused on a single gene in future studies
of ARM pathology, but there is also the distinct possibility that we will be unable to achieve further resolution with association studies or to clearly establish
whether there are more than two genes responsible for
ARM susceptibility on chromosome 10q26. However,
it is now well within the capabilities of molecular biologists to investigate the potential role of each of these
candidate genes in mouse models of ARM and to address the issue of a causal role in disease pathogenesis.
Association studies are an incredibly powerful means of
testing hypotheses of genetic contributions to disease,
but, except in the most extreme cases, they cannot provide definitive answers, even when there are impressive
P values.
Acknowledgments
We gratefully acknowledge the many individuals and family
members who participated as research subjects, as well as their
eye care providers for this study. We also wish to express our
thanks to our current and past clinical research team: E. Adams, C. Baic, L. R. Barnes, F. Bivins, M. Blagodatny, A. Burchis, I. Cantillo, J. Green, B. Gur, K. Hendricks, C. Klump, S.
Pope, M. Shaffer-Gordon, H. Sheth, S. Ward, and B. Zhu, and
to our laboratory research staff: Y. Demirci (who tested for
retinal expression of the three candidate genes), H. Brockway,
S. Deslouches, and B. Rigatti. Illumina-based SNP genotyping
services were provided by the Center for Inherited Disease
Research, which is fully funded through federal contract
N01-HG-65403 from the National Institutes of Health to The
Johns Hopkins University. This study was supported by National Eye Institute grant R01EY009859 (to M.B.G.); The
Steinbach Foundation, New York (to M.B.G.); Research to
Prevent Blindness, New York (Senior Scientist Investigator
Award to M.B.G.); and the Eye and Ear Foundation of Pittsburgh (to M.B.G.).
Web Resources
The URLs for data presented herein are as follows:
Online Mendelian Inheritance in Man (OMIM), http://www
.ncbi.nlm.nih.gov/Omim/ (for ARMD-1, CFH, PLEKHA1,
PRSS11, GRK5, and RGS10)
Division of Statistical Genetics, http://watson.hgen.pitt.edu/
(for Mega2)
The R Project for Statistical Computing, http://www.r-project
.org/ (for R statistical software)
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