Replication of a Glaucoma Candidate Gene on 5q22.1 for Intraocular Pressure in Mongolian Populations:

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Replication of a Glaucoma Candidate Gene on 5q22.1 for Intraocular Pressure in Mongolian Populations:

The GENDISCAN Project

Mi Kyeong Lee,

^1,2

Se Joon Woo,

^2,3

Jong-il Kim,

⁴

Sung-il Cho,

¹

Ho Kim,

⁵

*Joohon Sung,**

^,1

*Jeong-Sun Seo,**

^,4

*and Dong Myung Kim**

^,6

PURPOSE.Glaucoma is the second most frequent cause of visual impairment worldwide. Elevated intraocular pressure (IOP) causes glaucomatous optic nerve damage, especially in the primary open-angle glaucoma (POAG) subtype. As most previous studies on IOP genetics were analyses of glaucomatous families, a study of general pedigrees will provide additional information on genetic etiology.

METHODS.This work was part of the GENDISCAN study (Gene Discovery for Complex Traits in Isolated Large Families of Asians of the Northeast), which recruited families from population isolates in Mongolia. IOP (obtained by a noncontact method), epidemiologic, and clinical information were collected from 1451 healthy individuals of 142 families. From these individuals, 390 genome-wide short tandem repeat markers were genotyped. Variance component-based linkage analysis was applied to pursue candidate loci explaining IOP variation.

R^ESULTS.The mean IOP was 13.6 mm Hg in the men and 13.7 mm Hg in the women, inversely associated with aging (␤ ⫽

⫺0.05;Pⱕ0.0001). The heritability of IOP was 0.48. Sugges- tive linkage evidence was found on the 5q22.1 region (LOD

score, 2.4), which harbors WDR36, a candidate gene for POAG. In addition, possible linkage evidence was found on 2q37.1, 7p15.3, 17q25.3, and 20p13.

C^ONCLUSIONS. The findings support evidence that IOP regulation is associated with the 5q22.1 region, along with four other candidate regions. The present results further indicate that genetic factors regulating IOP in the general Mongolian population are linked to regions harboring POAG genes, suggesting that common genetic factors influence both normal IOP variation and POAG occurrence. In addition, the replication of previous findings concerning POAG regions from the white and African populations implies that the mutations regulating IOP levels did not occur recently. (Invest Ophthalmol Vis Sci.

2010;51:1335–1340) DOI:10.1167/iovs.09-3979

G

laucoma is the second most common cause of blindness worldwide, with a racially variable prevalence rate of 1%

to 3%.^1,2In 2010, glaucoma is predicted to affect 60.5 million people, with this number predicted to increase to 79.6 million by 2020.³The prevalence of primary glaucoma in the Mongo- lian population seems to be different from that in white populations. Primary angle-closure glaucoma (PACG) is more common (1.4%), and primary open-angle glaucoma (POAG) less common (0.5%) in the Mongolian population than in white populations.^4,5For example, The Baltimore Eye Survey found a POAG prevalence of 1.4% in the examined white population.⁵ Previous studies of subjects with elevated intraocular pressure (IOP) have shown a higher prevalence of glaucoma^6,7; moreover, eyes with higher IOP showed more severe glaucomatous optic nerve damage and visual impairment. Although IOP is not a necessary condition for glaucoma, it is a major risk factor for the disease. Therefore, the investigation of genes that influence IOP may help to elucidate the genetic mechanisms of IOP regulation as well as the genetic background of glaucoma.

Three genes have been identified in families of patients with glaucoma: myocilin (1q24.3-q25.2, in a family affected with an autosomal dominant form of juvenile open-angle glaucoma⁸), optineurin (10p15-p14, in a large British family with a classic form of normal-tension open-angle glaucoma⁹), and WD40- repeat 36 (WDR36; 5q21.3-q22.1, in families with adult-onset POAG¹⁰).

The environmental risk factors of IOP are not well under- stood, although previous studies^11,12support genetic contributions. Heritabilities of IOP were reported to be 0.35 in the Erasmus Rucphen Family study,¹³0.36 in the Beaver Dam Eye Study,¹¹ and 0.29 in the Salisbury Eye Evaluation Study.¹² Several studies have reported quantitative trait loci (QTLs) that influence IOP levels. However, in general, QTLs for IOP have not been proven either by replication or identifying the causative variant. Duggal et al.¹⁴reported two regions on chromosomes 6 and 13 linked to IOP in a middle-aged normal population in the United States. Charlesworth et al.¹⁵demonstrated From the¹Complex Disease Genetic Epidemiology Branch, De-

partment of Epidemiology, Graduate School of Public Health and In- stitute of Environment and Health, and the⁵Department of Epidemi- ology and Biostatistics, School of Public Health, Seoul National University, Seoul, Republic of Korea; the³Department of Ophthalmol- ogy, Seoul National University Hospital College of Medicine, Seoul National University Bundang Hospital, Seoul, Republic of Korea; the

4Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea; and the

6Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.

2Contributed equally to the work and therefore should be consid- ered equivalent authors.

Supported by Grant M10305030005-08N0503-00110 from the Ko- rean Ministry of Education, Science, and Technology and the second stage of the Brain Korea 21 Project.

Submitted for publication May 13, 2009; revised August 19, 2009;

accepted August 20, 2009.

Disclosure:M.K. Lee, None;S.J. Woo, None;J. Kim, None;S.

Cho, None;H. Kim, None;J. Sung, None;J.-S. Seo, None;D.M. Kim, None

*Each of the following is a corresponding author: Joohon Sung, Department of Epidemiology, Graduate School of Public Health and Insti- tute of Environment and Health, Seoul National University, 28 Yeongon- Dong, Chongro-gu, Seoul, Republic of Korea; [email protected].

Jeong-Sun Seo, Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, 28 Yeongon-Dong, Chongro-gu, Seoul, Republic of Korea; [email protected].

Dong Myung Kim, Department of Ophthalmology, Seoul National Univer- sity College of Medicine, Seoul National University Hospital, 28 Yeongon- Dong, Chongro-gu, Seoul, Republic of Korea; [email protected].

Investigative Ophthalmology & Visual Science, March 2010, Vol. 51, No. 3

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that the 10q22 region was linked to maximum IOP in one large POAG Australian pedigree. Rotimi et al.¹⁶reported 5q and 14q QTLs in families with type II diabetes in an African population.

Finally, Duggal et al.¹⁷reported 19p as a novel candidate region controlling IOP in a white population.

The lack of replication among QTLs of IOP may be partly attributable to the differences in ascertainment strategy (glaucomatous families or general population) or in the age distribution of participants. Other explanations of the discrepancy include ethnic differences in the etiology represented by the difference in glaucoma epidemiology, as well as genetic heterogeneity across populations. The phenotypic variability, including intraindividual variation and measurement errors and the complex mechanisms of optic nerve damage, may be other reasons for the lack of replication.

Since most studies regarding the genetics of glaucoma or IOP have been performed in non-Asian populations, studies from Asians, with different glaucoma epidemiology, will augment existing evidence. There are several reasons for this. First, the ratio of POAG to angle-closure glaucoma (ACG) is substantially different between Asian and white populations.⁵Second, the age-related patterns of change in IOP differ between these populations, displaying negative age association in East Asian populations^{18 –21}and positive age associations in West Asian^22,23and Caucasian^24,25pop- ulations. In addition, mean IOP levels in Mongolian, Chi- nese, and Japanese populations are lower than those in white^26,27populations. These ethnic differences in clinical and epidemiologic features of IOP suggest roles of both genetics and environment.

Studies of healthy participants from population isolates in Asia will not only reduce variations from environments and genetic heterogeneity, but will provide insight concerning epidemiologic differences. The GENDISCAN (GENe DIScovery for Complex traits in isolated large families of Asians of the Northeast) study is uniquely powerful in this regard. It is one of the rare studies of Northeast Asians tailored to gene discovery.

Using homogeneous population isolates, using families not selected according to their health status, and recruiting large and extended families are several ways to increase the power of detecting genes that regulating IOP.

M

ATERIALS AND

M

ETHODS

This study was performed as part of the GENDISCAN study designed to research the genetic backgrounds of several complex traits of the Asian population. In 2004, 142 large and complex pedigrees composed of 1451 family members were collected in 2004 in Orhongol, Selengae Province, Mongolia. The pedigree structure was complicated, with compound generations and numerous full and half-siblings, cousins, and spouses. Pedigree relationship information was determined by personnel interviews and confirmed by genotype data. The study adhered to the tenets of the Declaration of Helsinki. Informed consent was obtained from all subjects, and each study protocol was approved by the institutional review board of Seoul National University (approval number H-0307-105-002).

One trained examiner measured IOP in both eyes by noncontact tonometry (NCT; model TM800; Kowa, Torrance, CA). The average of two successive measurements was regarded as the IOP for each eye. In cases in which the difference between the two consecutive IOP measurements was⬎3 mm Hg, one more measurement was performed, and the average of all three measurements was used. A total of 219 individuals⬍13 years-of-age without reliable IOP measurements and one individual with a self-reported history of diabetes were excluded.

We collected additional epidemiologic data and clinical information, including basic demographics; anthropometries such as height, weight, waist circumference, and systolic/diastolic blood pressure;

clinical tests such as fasting plasma glucose level and blood lipids; and personal and family histories of disease.

Genotyping was conducted with linkage mapping (Prism Linkage Mapping Set, ver. 2.5; Applied Bioscience, Inc. [ABI], Foster City, CA) containing 400 short tandem repeat markers. An additional 80 Marsh- field microsatellite markers were adopted to make up intermarker gaps that resulted from genotype errors (markers with an error rate⬎1%) or insufficient information (a heterozygote index⬍0.4). Extensive quality checks were performed to verify consistency of marker genotyping and self-reported pedigree relationships. Genetic distances were based on the public Marshfield sex-averaged genetic map. Pedigree errors were found and corrected by using PREST.²⁸Paternity errors were detected and removed with Simwalk2 software (http://www.genetics.

ucla.edu/software/simwalk/ provided in the public domain by the Department of Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA).²⁹Lists of original and additional markers are provided in

TABLE1. Basic Characteristics of the Participants

Characteristic Men Women Total

Study subjects,n* 346 (40.2) 514 (59.8) 860

Age, y† 33.5 (16.5) 34.6 (16.5) 34.1 (16.5)

IOP, mm Hg by age† 13.6 (13.2–14.0) 13.7 (13.3–14.1) 13.7 (13.4–14.0) 13–19 y (n⫽238) 14.6 (14.2–15.0) 15.8 (15.4–16.2) 15.3 (14.9–15.7) 20–29 y (n⫽134) 14.6 (14.1–15.1) 14.3 (13.7–14.9) 14.4 (13.8–15.0) 30–39 y (n⫽185) 14.0 (13.6–14.4) 13.7 (13.3–14.1) 13.8 (13.4–14.2) 40–49 y (n⫽147) 14.0 (13.4–14.6) 13.2 (12.7–13.7) 13.6 (13.1–14.1) 50–59 y (n⫽76) 13.7 (13.0–14.4) 13.8 (13.1–14.5) 13.7 (13.0–14.4)

⬎60 y (n⫽80) 12.3 (11.7–12.9) 12.4 (11.7–13.1) 12.4 (11.7–13.1)

Range (median) 7–24 (13) 7–26 (13) 7–26 (13)

25%–75% 12–16 12–17 12–16

5%–95% 9–19 10–20 10–20

SBP, mm Hg† 121.5 (119.1–23.9) 120.3 (118.1–22.5) 120.8 (119.2–22.4)

DBP, mm Hg† 81.5 (80.2–82.8) 82.3 (81.2–83.8) 82.0 (81.0–83.0)

Fasting plasma glucose† 89.9 (88.4–91.4) 87.0 (85.7–88.3) 88.2 (87.2–89.2) Total serum cholesterol† 149.2 (145.6–52.8) 153.5 (150.6–56.4) 151.8 (149.5–54.1) Triglyceride† 72.6 (68.3–76.9) 68.1 (65.2–71.0) 69.9 (67.1–72.1) Body mass index† 22.6 (22.2–23.0) 23.6 (23.2–24.0) 23.2 (22.9–23.5)

SBP, systolic blood pressure; DBP, diastolic blood pressure.

* Count (%).

† Mean (95% confidence interval).

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Supplementary Table S1, http://www.iovs.org/cgi/content/full/51/3/

1335/DC1.

The mean IOP measurement of the left eye was used in the analysis;

the correlation of IOP between the left and right eyes was 0.86. The IOP levels were not normally distributed but instead were right skewed.Z-transformation was used for heritability and linkage analysis.

Basic statistical analyses were performed (SAS ver. 9; SAS Institute, Cary, NC), and Familial Correlation³⁰from SAGE (Statistical Analysis of Genetic Epidemiology, ver. 4.3; http://darwin.cwru.edu/sage/ provided in the public domain by the Department of Biostatistics and Epidemiology, Case Western Reserve University, Cleveland, OH) was used to estimate familial correlations and the asymptotic standard errors. In addition, heritability was estimated with the Variance Com- ponent algorithm in SOLAR (Sequential Oligogenic Linkage Analysis Routines), version 2.1.4³¹(http://www.vipbg.vcu.edu/software_docs/

solar/doc/00.contents.html). Multipoint linkage analysis was performed with SOLAR, to localize the QTLs that influence IOP. Expected LOD scores and empiric locus-specific P-values were calculated by using a 10,000-permutation simulation.^32,33 From the results, LOD scores of 1.9 to 3.3 were taken as evidence suggestive of linkage.³⁴All the analyses outlined were adjusted for age, age², sex, and interactions between each age term and sex.

R

ESULTS

The pedigree data used for this linkage study were obtained from 1451 individuals from 142 families with 1720 parent–

offsprings, 660 siblings, 946 grandparents, 795 avunculars, 548 cousins, and 452 spousal pairs. With the largest pedigree having a bit rate of 207, the average pedigree size was 10.2. Table 1 shows a detailed description of the study population and the distribution of the trait IOP. Approximately 40% of the subjects were male. The mean age of the men was 33.5 years and that of the women, 34.5 years. The mean (95% confidence interval) IOP was 13.7 (13.4 –14.0) mm Hg ranging from 7–26 mm Hg for all individuals, and 13.6 (13.2–14.0) mm Hg for the men and 13.7 (13.3–14.1) mm Hg for the women. As shown in Table 1, a decreasing tendency of IOP with age was evident in both sexes. The mean IOP in the 13- to 19-year and⬎60-year age

groups was 15.3 mm Hg and 12.4 mm Hg, respectively. The decreasing trend of IOP with age was significant (⫺0.25 by 5-year withP⬍0.0001.)

Table 2 shows the age- and sex-adjusted familial correlations of IOP for each relationship. The intraclass correlation between sibling pairs was the largest. Notably, spousal pairs showed a correlation of 0.02, indicating that environment influences, if any, would only marginally contribute to IOP variation. The higher correlations evident in closer familial relationship pairs were strongly suggestive of a role of genetic factors in controlling IOP levels.

The heritability (SE) for IOP was 0.48 (0.06;P⬍ 0.0001), which was compatible with the findings in familial correlation analyses that indicated the importance of genetic factors. The multivariate normality assumption was met for models under analysis (residual kurtosis, 0.21) when we adjusted for age, age², sex, and interactions between each age term and sex.

On genome-wide linkage scanning, we found several linkage regions with empiric P⬍ 0.001. The highest multipoint LOD score of IOP was 2.4, with an empiricP⫽ 0.0002, on 5q22.1 with the nearest markerD5S2027; the strongest signal met the Lander-Kruglyak criterion³⁴for suggestive linkage results. The one drop from maximum LOD score region spanned roughly 26 cM, from 126 to 152 cM. Other candidate regions were located on chromosome 2 with an LOD score of 1.6 (empiricP⫽0.0032), chromosome 7 with an LOD score of 1.4 (P⫽0.0051), chromosome 17 with an LOD score of 1.5 (P⫽ 0.0043), and chromosome 20 with an LOD score of 1.5 (P⫽ 0.0043), having the nearest marker (cytogenetic region) of D2S260(2q37.1),D7S493(7p15.3),D17S784(17q25.3), and D20S117(20p13), respectively. Table 3 shows detailed chro- mosomal information for these regions, including LOD score and the empiric P from simulation analyses. Figure 1A is a graphic display of the multipoint linkage analysis across 22 chromosomes. Figure 1B is a more detailed description of chromosome 5.

D

ISCUSSION

To our knowledge, this is the first report on QTLs that influence IOP levels in an Asian population. In our study, suggestive linkage evidence was observed on 5q22.1, with an LOD score of 2.4. Previously, 5q22 was reported as a suggestive linkage region for IOP in a study of a West African population.¹⁶The 5q22.1 region has also been linked to POAG in several other populations. GLC1G andWDR36, containing glaucoma-caus- ing mutations, were identified in the 5q22.1 region.¹⁰Several variants ofWDR36have been associated with POAG.^35,36In a recent study, alterations inWDR36in Japanese patients with POAG were not associated with normotensive POAG, whereas one variant ofWDR36was significantly associated with high- tension POAG.³⁷Our findings not only replicate those of previous studies but further suggest the possibility that normal variation in IOP, elevated IOP, and POAG may be regulated by common genetic factors. The LOD scores in this study did not TABLE2. Age- and Sex-Adjusted Intraclass Correlations between

Family Pairs of IOP Levels Relationship

Pair Count Correlation 95% CI

Parent–offspring 690 0.23 (0.19 to 0.27)

Sibling 374 0.37 (0.31 to 0.43)

Half-sibling 102 ⫺0.01 (⫺0.13 to 0.11)

Grandparent 161 ⫺0.02 (⫺0.11 to 0.07)

Avuncular 362 0.24 (0.17 to 0.31)

Half-avuncular 161 0.10 (⫺0.1 to 0.21)

Cousin 214 0.03 (⫺0.06 to 0.12)

Half-cousin 91 0.04 (⫺0.10 to⫺0.18)

Spouse 140 0.02 (⫺0.06 to 0.10)

Data are for genotyped individuals only.

TABLE3. Suggestive Regions from Genome-Wide Linkage Scan

Chromosome (Location)

Empirical P

Maximum LOD Score

1-LOD Unit Support Interval

Locus- Specific Heritability

Nearest Marker

Cytogenetic Region

5 (133) 0.0004 2.4 125–145 0.39 D5S2027 5q22.1

2 (254) 0.0032 1.6 240–266 0.39 D2S206 2q37.1

7 (35) 0.0051 1.4 17–52 0.36 D7S493 7p15.3

17 (130) 0.0043 1.5 118–139 0.35 D17S784 17q25.3

20 (4) 0.0043 1.5 2–16 0.32 D20S117 20p13

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reach the level of significance proposed by Lander and Krug- lyak.³⁴However, recent studies on age-related macular degeneration and the association ofTCF7L2and type 2 diabetes^38,39 demonstrated that replication with suggestive or possible evi-

dence is more convincing than unreplicated findings with strong LOD scores.

In addition, we found potential linkage evidence on chromosomes 2, 7, 17, and 20, with LOD scores higher than and or

0 50 100 150 200 250 300

Chromosome Position (cM) 0

1 2 3 4

LOD

Chromosome 1

D1S468D1S450D1S2667D1S1597D1S2697D1S199D1S234ATA79C10D1S2797D1S2890D1S230D1S2841D1S207D1S2868D1S206D1S2726D1S534D1S1653D1S484D1S2878D1S196D1S218D1S238D1S413D1S249D1S2141D1S213D1S2800D1S2785D1S2842

0 50 100 150 200 250

1 2 3 4

LOD

Chromosome 2

D2S319D2S2211D2S162D2S168D2S305D2S165D2S367D2S2259D2S391D2S337D2S2368D2S286D2S2333D2S2216D2S160D2S347D2S112D2S151D2S142D2S2330D2S335D2S364D2S117D2S2944D2S2382D2S434D2S126D2S1363D2S206D2S338D2S125

0 50 100 150 200 250

1 2 3 4

LOD

Chromosome 3

D3S1297 D3S1304D3S1263D3S2403 D3S1266D3S1277D3S1289D3S1300D3S1285D3S1566D3S3681D3S1271D3S3045 D3S1267D3S1292D3S1569D3S1279D3S1614D3S1565 D3S1262D3S1580D3S2398D3S1601D3S1311

0 50 100 150 200

1 2 3 4

LOD

Chromosome 4

D4S412D4S2935D4S403D4S419D4S391 D4S405 D4S1592D4S392D4S2964D4S1534 D4S1572D4S406D4S2394D4S1575 D4S1625 D4S1629D4S2368D4S1597D4S2431D4S415 D4S1535D4S426

0 50 100 150 200

1 2 3 4

LOD

Chromosome 5

D5S1981D5S406 D5S2845D5S419 D5S426D5S418D5S407D5S647D5S424D5S641D5S428D5S644D5S433D5S2027 D5S471D5S2115 D5S820D5S422D5S1471D5S400D5S211ATA52D02D5S408

0 50 100 150 200

1 2 3 4

LOD

Chromosome 6

D6S1574D6S309D6S470D6S289D6S422 D6S1051D6S1610D6S1017D6S2410 D6S460D6S462D6S1056 D6S287 D6S308GATA184A08D6S2436D6S1581D6S305D6S264D6S1027D6S446D6S281

0 50 100 150

1 2 3 4

LOD

Chromosome 7

D7S531D7S3056 GATA119B03 D7S493D7S516 D7S484D7S510D7S519 D7S502 D7S669D7S630D7S657D7S515 D7S3061D7S530 D7S684GATA104D7S636

0 50 100 150

1 2 3 4

LOD

Chromosome 8

D8S264D8S277 D8S550 D8S549 D8S258D8S1771D8S1048 D8S285D8S260D8S1136 D8S2324 D8S270 D8S1784 D8S514 D8S284 D8S272

0 50 100 150

1 2 3 4

LOD

Chromosome 9

D9S288D9S286GATA187D09D9S285D9S157D9S171D9S1121D9S161D9S1817D9S273D9S175 D9S167 D9S283D9S287D9S910D9S1690 D9S1677D9S1776 D9S1682D9S290D9S164 D9S1826D9S1838

0 50 100 150 200

1 2 3 4

LOD

Chromosome 10

D10S249D10S1435D10S591D10S189 D10S1653D10S548D10S197D10S208D10S196 D10S1652 D10S537 D10S2470D10S185 D10S597ATA103C06D10S1693D10S1230 D10S217D10S1248D10S1651D10S212

0 50 100 150

1 2 3 4

LOD

Chromosome 11

D11S4046 D11S1338D11S902 D11S904 D11S935D11S1993D11S4191D11S987D11S1314D11S937D11S901 D11S898D11S2000D11S1986D11S908 D11S4151 D11S1304D11S968

0 50 100 150

1 2 3 4

LOD

Chromosome 12

D12S352 D12S99D12S336 D12S364 D12S1617 D12S345D12S85D12S368 D12S83 D12S326 D12S351D12S346D12S78 D12S79D12S86 D12S324D12S1659D12S1045D12S1723

0 50 100

1 2 3 4

LOD

Chromosome 13

D13S175 D13S217 D13S171 D13S218D13S263 D13S153 D13S156 D13S170D13S265 D13S779 D13S173D13S1265 D13S285

0 50 100 150

1 2 3 4

LOD

Chromosome 14

D14S261D14S283 D14S1280D14S275 D14S70D14S306D14S288 D14S63D14S258 D14S74 GATA193A07 D14S280 D14S65 D14S985 D14S292

0 50 100

1 2 3 4

LOD

Chromosome 15

D15S128 D15S1002D15S1019D15S165D15S1007 GATA50C03 D15S978D15S117 D15S1507D15S153 D15S205D15S655 D15S652 D15S120

0 50 100

1 2 3 4

LOD

Chromosome 16

D16S423D16S404D16S3075 D16S3103 D16S3046 D16S3068 D16S3136D16S415 D16S503D16S2624 D16S516 D16S3091 D16S520

0 50 100

1 2 3 4

LOD

Chromosome 17

D17S831 D17S938 D17S1852 D17S799 D17S1857 D17S1294D17S798 D17S1868 D17S787D17S1290D17S944 D17S949 D17S785 D17S784 D17S928

0 50 100

1 2 3 4

LOD

Chromosome 18

D18S59 D18S63 D18S452 D18S464 D18S53 D18S478 D18S1102 D18S474 D18S64 D18S1364D18S61 D18S1161D18S462

0 50 100

1 2 3 4

LOD

Chromosome 19

D19S591 D19S216D19S1034D19S884 D19S586 D19S226 D19S433D19S414 D19S220D19S420 D19S902 D19S589D19S418 D19S210

0 50 100

1 2 3 4

LOD

Chromosome 20

D20S117 D20S889 D20S115 D20S186 D20S112 D20S195D20S107 D20S119 D20S100 D20S171D20S173

0 10 20 30 40 50 60

1 2 3 4

LOD

Chromosome 21

D21S1914 D21S1252 D21S266 D21S1446

0 10 20 30 40 50

1 2 3 4

LOD

Chromosome 22

D22S420 D22S539 D22S315 D22S280 D22S423 D22S274

A

0 50 100 150 200

Chromosome Position (cM)

0 1 2 3 4

LOD

Chromosome 5

D5S1981 D5S406 D5S2845 D5S419 D5S426 D5S418 D5S407 D5S647 D5S424 D5S641 D5S428 D5S644 D5S433 D5S2027 D5S471 D5S2115 D5S820 D5S422 D5S1471 D5S400 D5S211 ATA52D02 D5S408

B

WDR36 gene

FIGURE1. (A) Genome-wide multipoint linkage analysis results for IOP. The regions in chromosomes 2, 7, 5, 17, and 20 showed LOD scores greater than or⬃1.5. (B) Multipoint linkage results for chromosome 5. The highest LOD score was 2.4, with an empiricP⫽0.0002. The location of theWDR36gene and the highest peak from our analysis were very close.

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equal to 1.5. The locus on 2q37.1,MYP12, is related to high myopia⁴⁰and is also linked to common myopia.⁴¹This region should be investigated further, as myopia is one of the risk factors of elevated IOP⁴²and glaucoma.⁴³In 2006, a region on chromosome 7 (7p15.3) was reported to be related to congen- ital cataract.⁴⁴As 17q25 is one of the loci previously reported to be responsible for POAG,⁴⁵it would be worthwhile to focus on this region for candidate genes as well, although what we found in the present work is only less than suggestive linkage evidence. Moreover, 17q25.3 has been linked to systolic blood pressure,⁴⁶ which focuses attention on the relationship between systolic blood pressure and IOP.^12,24,47We used systolic blood pressure as a covariate in linkage analysis; however, adjusting for it did not materially alter the LOD score for this region. Chromosome 20 also showed a potential linkage in this study; however, 20p13 has not been linked to IOP or glaucoma to date.

The heritability of IOP was 0.48, which is higher than that in other studies.^11,12,48 The higher heritability is most likely due, at least in part, to the lesser variation in other environments and more genetic heterogeneity underlying IOP levels.

Given the higher frequencies of PACG in Mongolia,⁴if the same population were analyzed for genes influencing glaucoma risk, the results would have explained the risk of PACG rather than POAG. Our findings alone cannot exclude or include the possibility that those who have elevated IOP will develop higher risk of POAG in the Mongolian population.

However, considering our findings on IOP genes and previous reports on POAG genes, together with the pathogenesis underlying POAG, it is logical to suggest that there is a common genetic variation in the 5q region that influences both IOP and POAG risk. Furthermore, the possibility of a common genetic variant among the Caucasian, African, and Asian populations suggests that the genetic variation regulating IOP levels is ancient and is not selected by the evolutionary process.

In this study, we obtained IOP data by using NCT. Although NCT readings tend to show slightly higher values than Gold- mann applanation tonometry,²⁰ good correlations between NCT and Goldmann readings have been reported previously.^{49 –51}Thus, NCT is a reasonable substitute for the gold standard method, especially in large-scale epidemiologic studies.

Our study was a large family-based examination of an isolated Asian population. Population admixture is a critical limi- tation in many genetic studies and may lead to biased results.⁵² Collecting related individuals from a genetically homogeneous population not only decreases subpopulation effects but has greater statistical power.⁵³

There are several limitations to our study. First, we did not check central corneal thickness in our subjects. As NCT is more sensitive to the level of central corneal thickness than Goldmann tonometry,⁵⁴we would have obtained more accu- rate IOP data if we had adjusted IOP according to central corneal thickness. Second, the subjects did not undergo thor- ough ophthalmic examinations. More detailed examinations, such as slit lamp examination, gonioscopic angle assessment, optic disc examination, and visual field testing, which were all unavailable in our survey setting, would have yielded additional information regarding related types of glaucoma. The IOP levels used in this study were of cross-sectional measurements that include intraindividual variation as well as measurement errors. However, it is unlikely that the variation in IOP measurement is associated with genetic predisposition and the resultant biasing of the results. The intraindividual variation and errors in measurements partly account for the weaker linkage evidence in this study.

In conclusion, by primary genome-wide linkage analysis in a general population in Mongolia, we replicated the genomic region 5q22.1 containing theWDR36gene. Region 5q22.1 was

previously reported to be linked to IOP in a West African glaucomatous pedigree, and WDR36 is a causative gene in POAG. In addition, we discovered four new candidate loci of IOP regulation, each of which has been reported to be associated with IOP or IOP-related traits.

References

1. Quigley HA, Vitale S. Models of open-angle glaucoma prevalence and incidence in the United States.Invest Ophthalmol Vis Sci.

1997;38:83–91.

2. Quigley HA. Number of people with glaucoma worldwide. Br J Ophthalmol.1996;80:389 –393.

3. Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020.Br J Ophthalmol.2006;90:262–

267.

4. Foster PJ, Baasanhu J, Alsbirk PH, Munkhbayar D, Uranchimeg D, Johnson GJ. Glaucoma in Mongolia: a population-based survey in Hovsgol province, northern Mongolia.Arch Ophthalmol. 1996;

114:1235–1241.

5. Tielsch JM, Sommer A, Katz J, Royall RM, Quigley HA, Javitt J.

Racial variations in the prevalence of primary open-angle glaucoma. The Baltimore Eye Survey.JAMA.1991;266:369 –374.

6. Hollows FC, Graham PA. Intra-ocular pressure, glaucoma, and glaucoma suspects in a defined population. Br J Ophthalmol.

1966;50:570 –586.

7. Kahn HA, Leibowitz HM, Ganley JP, et al. The Framingham Eye Study, I: outline and major prevalence findings.Am J Epidemiol.

1977;106:17–32.

8. Sheffield VC, Stone EM, Alward WL, et al. Genetic linkage of familial open angle glaucoma to chromosome 1q21– q31. Nat Genet.1993;4:47–50.

9. Sarfarazi M, Child A, Stoilova D, et al. Localization of the fourth locus (GLC1E) for adult-onset primary open-angle glaucoma to the 10p15–p14 region.Am J Hum Genet.1998;62:641– 652.

10. Monemi S, Spaeth G, DaSilva A, et al. Identification of a novel adult-onset primary open-angle glaucoma (POAG) gene on 5q22.1.

Hum Mol Genet.2005;14:725–733.

11. Klein BE, Klein R, Lee KE. Heritability of risk factors for primary open-angle glaucoma: the Beaver Dam Eye Study.Invest Ophthal- mol Vis Sci.2004;45:59 – 62.

12. Chang TC, Congdon NG, Wojciechowski R, et al. Determinants and heritability of intraocular pressure and cup-to-disc ratio in a defined older population. Ophthalmology. 2005;112:1186 – 1191.

13. van Koolwijk LM, Despriet DD, van Duijn CM, et al. Genetic contributions to glaucoma: heritability of intraocular pressure, retinal nerve fiber layer thickness, and optic disc morphology.

Invest Ophthalmol Vis Sci.2007;48:3669 –3676.

14. Duggal P, Klein AP, Lee KE, et al. A genetic contribution to intraocular pressure: the Beaver Dam Eye Study.Invest Ophthal- mol Vis Sci.2005;46:555–560.

15. Charlesworth JC, Dyer TD, Stankovich JM, et al. Linkage to 10q22 for maximum intraocular pressure and 1p32 for maximum cup-to- disc ratio in an extended primary open-angle glaucoma pedigree.

Invest Ophthalmol Vis Sci.2005;46:3723–3729.

16. Rotimi CN, Chen G, Adeyemo AA, et al. Genomewide scan and fine mapping of quantitative trait loci for intraocular pressure on 5q and 14q in West Africans. Invest Ophthalmol Vis Sci.2006;47:

3262–3267.

17. Duggal P, Klein AP, Lee KE, Klein R, Klein BE, Bailey-Wilson JE.

Identification of novel genetic loci for intraocular pressure: a genomewide scan of the Beaver Dam Eye Study.Arch Ophthalmol.

2007;125:74 –79.

18. Lin HY, Hsu WM, Chou P, et al. Intraocular pressure measured with a noncontact tonometer in an elderly Chinese population: the Shihpai Eye Study.Arch Ophthalmol.2005;123:381–386.

19. Mori K, Ando F, Nomura H, Sato Y, Shimokata H. Relationship between intraocular pressure and obesity in Japan. Int J Epide- miol.2000;29:661– 666.

20. Lee JS, Lee SH, Oum BS, Chung JS, Cho BM, Hong JW. Relationship between intraocular pressure and systemic health parameters in a Korean population.Clin Exp Ophthalmol.2002;30:237–241.

(6)

21. Foster PJ, Baasanhu J, Alsbirk PH, Munkhbayar D, Uranchimeg D, Johnson GJ. Central corneal thickness and intraocular pressure in a Mongolian population.Ophthalmology.1998;105:969 –973.

22. Hennis A, Wu SY, Nemesure B, Leske MC, Barbados Eye Studies G.

Hypertension, diabetes, and longitudinal changes in intraocular pressure.Ophthalmology.2003;110:908 –914.

23. Hashemi H, Kashi AH, Fotouhi A, Mohammad K. Distribution of intraocular pressure in healthy Iranian individuals: the Tehran Eye Study (see comment).Br J Ophthalmol.2005;89:652– 657.

24. Rochtchina E, Mitchell P, Wang JJ. Relationship between age and intraocular pressure: The Blue Mountains Eye Study. Clin Exp Ophthalmol.2002;30:173–175.

25. Klein BE, Klein R, Linton KL. Intraocular pressure in an American community: The Beaver Dam Eye Study.Invest Ophthalmol Vis Sci.1992;33:2224 –2228.

26. Hu Z, Zhao ZL, Dong FT. An epidemiological investigation of glaucoma in Beijing and Shun-hi county (in Chinese). Chin J Ophthalmol.1989;25:115–118..

27. Shiose Y, Kitazawa Y, Tsukahara S, et al. Epidemiology of glaucoma in Japan: a nationwide glaucoma survey.Jpn J Ophthalmol.

1991;35:133–155.

28. McPeek MS, Sun L. Statistical tests for detection of misspecified relationships by use of genome-screen data. Am J Hum Genet.

2000;66:1076 –1094.

29. Weeks DE, Sobel E, O’Connell JR, Lange K. Computer programs for multilocus haplotyping of general pedigrees.Am J Hum Genet.

1995;56:1506 –1507.

30. Elston RC, George VT, Severtson F. The Elston-Stewart algorithm for continuous genotypes and environmental factors.Hum Hered- ity.1992;42:16 –27.

31. Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees.Am J Hum Genet.1998;62:1198 –1211.

32. Blangero J, Williams JT, Almasy L. Robust LOD scores for variance component-based linkage analysis. Genet Epidemiol. 2000;

19(suppl 1):S8 –S14.

33. Blangero J, Williams JT, Almasy L. Variance component methods for detecting complex trait loci.Adv Genet.2001;42:151–181.

34. Lander E, Kruglyak L. Genetic dissection of complex traits: guide- lines for interpreting and reporting linkage results. Nat Genet.

1995;11:241–247.

35. Kramer PL, Samples JR, Monemi S, Sykes R, Sarfarazi M, Wirtz MK.

The role of the WDR36 gene on chromosome 5q22.1 in a large family with primary open-angle glaucoma mapped to this region.

Arch Ophthalmol.2006;124:1328 –1331.

36. Pasutto F, Mardin CY, Michels-Rautenstrauss K. Profiling of WDR36 missense variants in German patients with glaucoma.

Invest Ophthalmol Vis Sci.2008;49:270 –274.

37. Miyazawa A, Fuse N, Mengkegale M, et al. Association between primary open-angle glaucoma and WDR36 DNA sequence variants in Japanese.Mol Vis.2007;13:1912–1919.

38. Grant SF, Thorleifsson G, Reynisdottir I, et al. Variant of transcrip- tion factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes.

Nat Genet.2006;38:320 –323.

39. Klein RJ, Zeiss C, Chew EY, et al. Complement factor H polymor- phism in age-related macular degeneration. Science. 2005;308:

385–389.

40. Paluru PC, Nallasamy S, Devoto M, Rappaport EF, Young TL.

Identification of a novel locus on 2q for autosomal dominant high-grade myopia. Invest Ophthalmol Vis Sci. 2005;46:2300 – 2307.

41. Chen CY, Stankovich J, Scurrah KJ, et al. Linkage replication of the MYP12 locus in common myopia. Invest Ophthalmol Vis Sci.

2007;48:4433– 4439.

42. Weih LM, Mukesh BN, McCarty CA, Taylor HR. Association of demographic, familial, medical, and ocular factors with intraocular pressure.Arch Ophthalmol.2001;119:875– 880.

43. Mitchell P, Hourihan F, Sandbach J, Wang JJ. The relationship between glaucoma and myopia: the Blue Mountains Eye Study.

Ophthalmology.1999;106:2010 –2015.

44. Zara F, Biancheri R, Bruno C, et al. Deficiency of hyccin, a newly identified membrane protein, causes hypomyelination and congen- ital cataract.Nat Genet.2006;38:1111–1113.

45. Wiggs JL, Allingham RR, Hossain A, et al. Genome-wide scan for adult onset primary open angle glaucoma.Hum Mol Genet.2000;

9:1109 –1117.

46. Franceschini N, MacCluer JW, Goring HH, et al. A quantitative trait loci-specific gene-by-sex interaction on systolic blood pressure among American Indians: The Strong Heart Family Study.Hyper- tension.2006;48:266 –270.

47. Dielemans I, Vingerling JR, Algra D, et al. Primary open-angle glaucoma, intraocular pressure, and systemic blood pressure in the general elderly population: The Rotterdam Study (see comment).

Ophthalmology.1995;102:54 – 60.

48. Forsman E, Cantor RM, Lu A, et al. Exfoliation syndrome: prevalence and inheritance in a subisolate of the Finnish population.

Acta Ophthalmol Scand.2007;85:500 –507.

49. Van de Velde T, Zeyen T. Reliability of the Nidek NT-1000 non contact tonometer.Bull Soc Belge Ophtalmol.1995;255:19 –22.

50. Gupta V, Sony P, Agarwal HC, Sihota R, Sharma A. Inter-instrument agreement and influence of central corneal thickness on measurements with Goldmann, pneumotonometer and noncontact tonometer in glaucomatous eyes.Indian J Ophthalmol. 2006;54:261–

265.

51. Ogbuehi KC. Assessment of the accuracy and reliability of the Topcon CT80 non-contact tonometer.Clin Exp Optometry.2006;

89:310 –314.

52. Rao DC, Gu C. False positives and false negatives in genome scans.

Adv Genet.2001;42:487– 498.

53. Varilo T, Peltonen L. Isolates and their potential use in complex gene mapping efforts.Curr Opin Genet Dev.2004;14:316 –323.

54. Tonnu PA, Ho T, Newson T, et al. The influence of central corneal thickness and age on intraocular pressure measured by pneumotonometry, non-contact tonometry, the Tono-Pen XL, and Goldmann applanation tonometry.Br J Ophthalmol.2005;

89:851– 854.