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Osteoporosis Research Center (H.-W.D., F.-H.X., T.C., X.-T.D., J.-L.L., K.M.D., H.D., M.J., R.R.R.) and Department of Biomedical Sciences (H.-W.D., F.-H.X., X.-T.D., J.-L.L.), Creighton University, Omaha, Nebraska 68131; and Laboratory of Molecular and Statistical Genetics (H.-W.D.), Hunan Normal University, ChangSha, Hunan 410081, P. R. China
Address all correspondence and requests for reprints to: Hong-Wen Deng, Ph.D., Osteoporosis Research Center, Creighton University, 601 North 30th Street Suite 6787, Omaha, Nebraska 68131. E-mail: deng{at}creighton.edu
Abstract
Our purpose is to test linkage of human chromosome 11q12–13 to BMD
variation. Chromosome 11q12–13 has been linked to three BMD-related
phenotypes that are inherited as Mendelian traits in human pedigrees:
an autosomal dominant high bone mass trait, autosomal recessive
osteoporosis pseudoglioma, and autosomal recessive osteopetrosis. A
sibling pair study with 374 sibships showed significant linkage
of D11S987 to normal BMD variation, with a maximum logarithm of
odds score of 3.5. However, a subsequent linkage study with a
total of 595 sibling pairs demonstrated reduced significance for
linkage of D11S987 to bone mineral density variation, with a logarithm
of odds score less than 2.2. We genotyped five markers in a genomic
region of 
27 cM centering on D11S987 and measured bone
mineral density and other traits (weight, etc.) for 635
individuals from 53 human pedigrees. Each of these pedigrees was
ascertained through a proband with bone mineral density Z-scores less
than -1.28 at the hip or spine. Adjusting for age, sex, and weight as
covariates, we performed two-point and multipoint linkage analyses
using the variance component linkage analysis method implemented in
Sequential Oligogenic Linkage Analysis Routines. We found little
evidence of linkage of these five markers to bone mineral density at
the spine, hip, wrist and total body bone mineral content. The
maximum logarithm of odds score at these five markers was 0.25, and the
maximum logarithm of odds score at D11S987 was 0.15. Therefore,
although we cannot exclude the linkage of D11S987 region to bone
mineral density variation, there is no evidence for linkage of the
marker D11S987 on human chromosome 11q12–13 to bone mineral density
variation in our study population.
LOW BONE MINERAL density (BMD), which can be measured by methods such as dual-energy x-ray absorptiometry, is an important risk factor for fracture, and osteoporosis is mainly characterized by low BMD (1, 2, 3). Osteoporosis results in more than 1.3 million osteoporotic fractures a year, with an estimated direct cost of 13.8 billion dollars (4) in the United States alone. Extensive data have established that BMD variation is under strong genetic control with heritability (h2) estimates ranging from 0.5–0.9 (Refs. 5, 6, 7, 8, 9, 10 ; Deng, H. W., J. Li, M. C. Mahaney, et al., submitted for publication).
Molecular genetic studies (11, 12, 13, 14, 15, 16, 17, 18) have been launched to search for genes underlying BMD variation. Results so far from extensive population association studies have largely been inconsistent (14, 19, 20, 21). A few studies using alternative approaches such as quantitative trait loci (QTL) mapping in model organisms (22) and linkage analyses in humans (12, 16, 23, 24, 25, 26, 27, 28) have appeared. From the few linkage studies (12, 16, 23, 24, 25, 26, 27, 28), both consistent and inconsistent findings (e.g. Refs. 23, 25, 27), have been reported, and the results (12, 16, 24, 26, 28) concerning the marker D11S987 in chromosome 11q12–13 are particularly intriguing. In human pedigrees, significant linkage to chromosome 11q12–13 has been reported for three distinct Mendelian-inherited traits that are BMD-related. The first trait is the autosomal recessive syndrome, osteoporosis-pseudoglioma, which is characterized by severe juvenile-onset osteoporosis and congenital or juvenile onset blindness. It was linked to chromosome 11q12–13 with a maximum logarithm of odds (LOD) score of 5.99 achieved at marker D11S987 (24). The second is the autosomal recessive osteopetrosis that is characterized by abnormally dense bones, as well as acrocephaly, anemia, and progressive deafness and blindness that are due to a failure of osteoclast-mediated bone resorption. It is linked to chromosome 11q12–13 with a maximum LOD score of 5.9 achieved (28) in two consanguineous kindreds. Mutations for the gene responsible for sub sets of autosomal recessive osteopetrosis have been identified (48, 49). The third is an autosomal dominant trait characterized by high bone mass (HBM) that is not accompanied by other clinical manifestations. Our group reported it to be linked to chromosome 11q12–13 with a maximum LOD score of 5.74 achieved at the marker D11S987 (12).
Stimulated by these results, chromosome 11q12–13 was subsequently investigated for its linkage to normal BMD variation (16, 26). A sibling (Sib) pair study with 374 sibships reported that for seven microsatellite markers typed in chromosome 11q12–13, a maximum LOD score of 3.50 was achieved near D11S987 for linkage to femoral neck BMD variation (16). However, a subsequent linkage study (26) by the same group with an expanded sample size of a total of 595 sib pairs demonstrated much reduced significance for linkage of chromosome 11q12–13 to BMD variation, with a LOD score achieved at D11S987 less than 2.2 (indeterminate for linkage). LOD scores greater than 3.6 and 2.2 are, respectively, considered as indicative of significant and suggestive linkage for sib pair whole genome scan to identify genes for complex traits (44). The P value corresponding to a LOD score of 2.2 is 0.0015, and the P value corresponding to a LOD score of 3.5 is 0.000060 (44). Regular sib pair linkage studies have limited power in searching for QTL (29, 30, 31, 39).
The limited theoretical power of the sib pair linkage study design and the few but somewhat inconsistent empirical linkage findings of D11S987 to BMD variation (16, 26) warrant that an independent linkage analysis with an alternative linkage approach be conducted in a different population. Hence, we will test linkage of the genomic region near the marker D11S987 in chromosome 11q12–13 to BMD variation in a Caucasian population from the Midwestern United States at the hip, spine, wrist, and total body BMC (TBBMC), respectively. We analyzed multiple bone mass traits because there is substantial heterogeneity of BMD at different bone sites (32) and the genetic determination of BMD at different bone sites may not be all the same (9).
Materials and Methods
Subjects
The study was approved by the Creighton University Institutional Review Board. All the study subjects signed informed consent documents before entering the project. The study subjects came from an expanding database being created for a whole genome linkage study aimed at searching for genes underlying BMD variation and osteoporotic fracture risk that is underway in the Osteoporosis Research Center of Creighton University. Only healthy people (defined by the exclusion criteria detailed below) were included in the analysis. All the study subjects were Caucasians of European origin. Fifty-three pedigrees with 635 subjects (252 males and 383 females) from two to four generations were analyzed. The pedigrees varied in size from 3–99 individuals, with a mean of 11.7 (±SE = 2.4). Each pedigree was identified through a proband having BMD Z-scores -1.28 or lower at the hip or spine so that the probands were selected from the bottom 10th percentile of the population BMD variation with the purpose to achieve higher statistical power than random sampling (40). BMD values are expressed as Z-scores that adjust for age, gender, and ethnic difference in general referent healthy populations. The exclusion criteria for the study subjects were a history of 1) serious residuals from cerebral vascular disease:, 2) diabetes mellitus, except for easily controlled, noninsulin dependent diabetes mellitus; 3) chronic renal disease manifest by serum creatinine greater than 1.9 mg/dl; 4) chronic liver disease or alcoholism; 5) significant chronic lung disease; 6) corticosteroid therapy at pharmacologic levels for more than 6 months duration; 7) treatment with anticonvulsant therapy for more than 6 months duration; 8) evidence of other metabolic or inherited bone disease such as hyper- or hypoparathyroidism, Paget’s disease, osteomalacia, osteogenisis imperfecta, or others; 9) rheumatoid arthritis or collagen disease; 10) recent major gastrointestinal disease (within the past year) such as peptic ulcer, malabsorption, chronic ulcerative colitis, regional enteritis, or any significant chronic diarrhea state; 11) significant disease of any endocrine organ that would affect bone mass; 12) hyperthyroidism; 13) any neurological or musculoskeletal condition that would be a nongenetic cause of low bone mass; and 14) any disease, treatment, or condition that would be a nongenetic cause for low bone mass. The exclusion criteria were assessed by nurse- administered questionnaires and/or medical records.
Genotyping
For each subject, blood (20 cc) was drawn into lavender cap
(EDTA-containing) tubes by certified phlebotomists and stored chilled
(-4 C) until DNA extraction that was normally completed within the
next five calendar days. DNA was extracted by using a kit (Puregene DNA
Isolation Kit; catalog no. D-5000; Gentra Systems, Inc.,
Minneapolis, MN), following the procedures detailed in the kit. DNA was
genotyped using fluorescently labeled markers as we did before (Li,
J. L., D. B. Lai, H. Y. Deng, R. R. Recker, and
H. W. Deng, submitted for publication). The five markers used
(D11S905, D11S4191, D11S987, D11S1314, and D11S901) are commercially
available through PE Applied Biosystems and have
population heterozygosity of 0.75, 0.87, 0.82, 0.78, and 0.82,
respectively (ABI PRISM Linkage Mapping Sets, version 2; Norwalk, CT).
These markers span a genomic region of 27 cM and center
around the marker D11S987. Genotyping was performed using an Applied
Biosystems automated DNA sequencing system (model 377;
Perkin-Elmer Corp.-ABI, Foster City, CA) running the
GENESCAN and GENOTYPER software for allele identification and sizing. A
genetic database management system with efficient algorithms developed
by us (Li, J. L., D. B. Lai, H. Y. Deng, R. R.
Recker, and H. W. Deng, submitted for publication) was used to
manage the phenotype and genotype data for linkage analyses. The
genetic database management system was also used for allele bining
(including setting up allele bining criteria and converting allele
sizes to distinct allele numbers) and data formatting for PedCheck
(38) and linkage analyses by Sequential Oligogenic Linkage
Analysis Routines (SOLAR). PedCheck (available at
http://watson.hgen.pitt.edu/register/soft_doc.html) was used for
verifying Mendelian inheritance of all the marker alleles. The
genotyping error rate, determined by sample replication in experiments
and data analyses by PedCheck, was about 0.3%.
Measurement
BMDs of spine, hip, wrist, and TBBMC were measured by a Hologic 1000, 2000+, or 4500 scanner (Hologic, Inc., Waltham MA). All machines are calibrated daily, and long-term precision is monitored with external spine and hip phantoms. Hip, spine, and wrist were chosen because they are the most common osteoporotic fracture sites (1). Short-term precision in humans is 0.7% for spine BMD, 1.0% for hip BMD, 1.2% for wrist BMD, and 1.1% for TBBMC. We maintain constant quality assurance procedures that track potential confounding events such as x-ray tube replacement, arm realignments, collimator changes, and software version updates. Technicians maintain scan-by-scan surveillance for quality control. We have chosen BMD rather than bone mineral content as our bone mass phenotype, because BMD is the measure most closely correlated with fracture risk (43). For the spine, our quantitative phenotype was combined BMD of L1–4. For the hip, it was combined BMD of the femoral neck, trochanter, and intertrochanteric region. For the wrist, it was ultra distal BMD. All dual-energy x-ray absorptiometry machines report BMD in g/cm2 and BMC in grams. Weight was measured at the same visit when the BMD measurements were taken. Data obtained from different machines are transformed to a compatible measurement by an algorithm developed by us (Recker, R. R., and K. M. Davies, unpublished data), and members of the same pedigree are usually measured on the same type of machine.
Statistical analyses
The variance component linkage analysis (33, 34, 35) for quantitative traits was performed. The analysis is based on specifying the expected genetic covariances between arbitrary relatives as a function of the identity by descent at a given marker locus. The analysis considers the phenotypic and genetic information from all pedigree members simultaneously. The analysis assumed joint multivariate normality of phenotypic values, additive genetic effects, and no interaction between genes and the residual. The common familial environmental effects were assumed to be negligible, which is reasonable and supported by previous studies (7, 8, 9, 36). The program used was SOLAR (35), which is available on the internet (http://www.sfbr.org/sfbr/public/software/solar/solar.html). The ascertainment scheme of pedigrees based on the low BMD values of probands was accounted for in the analyses by identifying to the program the probands for each pedigree.
In linkage analysis, age, sex, and weight were adjusted as covariates
to adjust for raw BMD and BMC values (not the Z-scores), as these
generally affect BMD or BMC variation significantly (10).
Analyses were also performed without adjusting for some of these
covariates. Adjustment for significant covariates in genetic analyses
can generally increase the genetic signal to noise ratio
(i.e. h2 estimates) by
decreasing the proportion of the residual phenotypic variation
attributable to random environmental factors (9, 10).
This can improve statistical power in our linkage analyses. The BMD
data were tested by graphical methods (37) and found not
to deviate from normal distributions. Marker allele frequencies were
obtained by maximum likelihood estimation in SOLAR. Hypothesis testing
for linkage was conducted by the maximum likelihood method by
investigating the relationship of genetic covariances and the identical
by descent between arbitrary relatives. The method compares the maximum
likelihoods obtained in the full model (with linkage so that the locus
is a QTL and accounts for some additive genetic variance) and the
nested null model (without linkage and the locus is not a QTL). The
difference between the two log10 likelihoods
yields a LOD score. Twice the difference of the
loge of the likelihoods of these two models is
asymptotically distributed as a 1/2:1/2 mixture of a
2 variable and a point mass at zero
(35) with 1 degree of freedom. Using SOLAR, two-point and
multipoint linkage analyses were performed, respectively, for BMD at
hip, spine, wrist, and TBBMC. LOD scores may be converted to
approximate P values commonly used in statistical testing
through a 2 distribution
(44).
Results
The basic characteristics of the study subjects stratified by age
and sex are summarized in Table 1
.
Our pedigree study includes men and women (including both pre- and
postmenopausal women). Thus, our study may differ from some earlier
studies that use only premenopausal women (where peak bone mass would
be the phenotype) or postmenopausal women (where a combination of
peak bone mass and bone loss is the study phenotype). However,
statistical adjustment for significant effects of sex, age, and/or
menopausal status should minimize the difference of subjects used for
testing important genomic regions. In Table 2, the results of the two-point linkage
analyses are given. The results of the multipoint linkage analyses are
summarized in Fig. 1.
We did not detect a LOD score greater than 0.25 for BMD at spine, hip,
wrist, or TBBMC, either in two-point or multipoint linkage analyses
(Table 2, Fig. 1). The maximum LOD score at these five markers was 0.25
(achieved by the marker D11S901 for hip BMD in the two-point analyses).
The maximum LOD score at D11S987 was 0.15 (achieved for spine BMD in
the two-point analyses). The LOD scores of 0.25 and 0.15 correspond,
respectively, to P values of 0.14 and 0.20. Therefore, we
did not find evidence for linkage of the marker D11S987 on human
chromosome 11q12–13 to BMD variation in our study population. The
analyses that did not adjust for other covariates such as height and
weight showed the same and nonsignificant results.
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We genotyped five closely linked markers in a genomic region of
27 cM centering on D11S987 and measured BMD and other
traits (weight, etc.) for 635 individuals from 53 human
pedigrees each ascertained through a proband with BMD Z-scores
less than -1.28. Ascertainment of pedigrees or sib pairs through
individuals with extreme values may greatly increase the power of
linkage analyses (40), and this strategy has been adopted
in a recent linkage study of BMD variation (27). We did
not find evidence of linkage of the genomic region studied with BMD.
Therefore, we did not find evidence for linkage of the marker D11S987
in human chromosome 11q12–13 to BMD variation in our study population.
Our finding concerning linkage of this genomic region is consistent
with those linkage results concerning the same region in other
populations (23, 25). Bone mass is analyzed here, as it is
the phenotype that is used in the previous linkage studies (12, 16, 23, 24, 25, 26, 28) that prompted this study and it is also the
phenotype most commonly used for osteoporosis genetic research at
present. Other bone phenotypes, such as bone size (Deng, H. W.,
X. T. Deng, T. Conway, et al., submitted for
publication), osteoporotic fractures per se
(3), cortical thickness, and medullary diameter, may also
be used in genetic analyses for osteoporosis and could be pursued in
the future.
In addition to the ascertainment scheme through probands with extreme
phenotypic values that may greatly increase the power to detect a
potential true linkage, the sample size used in this study is among the
largest for linkage analyses published so far to search for QTL in
humans. The variance component linkage approach used here is also a
powerful one (33, 34, 35) in that it can use the information
from all members in a pedigree simultaneously. Such information may be
extremely rich in terms of the number of the used relative pairs such
as sib pairs and grandparent-grandchildren pairs, etc. In
our sample of 635 individuals genotyped and phenotyped, there is a
total of 7846 relationships (including 1380 parent-offspring
relationships that are not informative in the variation of identical by
descent; Table 3) among which there are
1249 sib pairs. Using the same linkage approach even without
ascertainment through probands with extreme values, Comuzzie et
al. (42) successfully identified a major QTL for the
variation of serum leptin levels and fat mass in 458 individuals from
10 pedigrees with a total of 5667 relationships (including 445
uninformative parent-offspring relationship) used. Therefore, the lack
of evidence of linkage in the current study does not seem to be due to
the smaller statistical power of our approach and our sample when
compared with those of Koller et al. (16).
Simulations similar to those performed in (49) suggest
that our sample may detect a QTL with a heritability of 0.20 with more
than 90% power.
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Although we did not find evidence that the genomic region surrounding the marker D11S987 harbors a QTL for BMD variation, our results do not exclude the existence of such a QTL with a small effect in this genomic region. Confirmation of previous linkage results for a genomic region to a complex trait is usually difficult (46). This is largely due to the limited power with relatively small sample sizes in most linkage studies in searching for genes of relatively small effects. The principle can be intuitively demonstrated by a hypothetical example as follows. Assume that there are 10 QTLs underlying the variation of a trait with each QTL having a heritability of 0.08, so that the overall heritability of the trait is 80%. We also assume that with a sample size n and a specific study design, for each of the QTL, we have 10% power to detect its linkage with the trait. Then, in the first attempt of a whole genome scan, we will have approximately 66% power to detect linkage of at least one of the 10 QTLs to the trait. This is simply because there are 10 QTLs and each QTL can be detected with 10% power, and, thus, the power to detect any one of them (at least one of them) is obtained by 1-(1–0.1)10. In another or a follow-up whole genome scan study with the same sample size and the same study design but for a different sample, there is again 80% power to detect linkage of one of the 10 QTLs to the trait. However, the QTL detected in the second study is unlikely to be the one reported in the first study. This is because for the specific QTL detected in the first study, the power to detect it in the second study is only 10%. For the same reason, to confirm specifically linkage of a previously found QTL, the power is low (only 10%) even if the same study design and sample size (different samples) are used. The problem of a QTL with a small effect that is detected by a study of low power due to the existence of multiple QTLs and the high chance of false linkage results in a genome wide scan, together contribute to the failure of confirmation of many linkage studies. Therefore, the lack of evidence of linkage of the current study either suggests that, in the genomic region studied in the current population, there is no QTL or that the effect of the QTL (if existent) is small so that the power for repeated experiments to confirm it is small.
It should be noted that the current study is a replication study for a previously found putative significant linkage result. The significance level required for a replication study to confirm previous putative significant linkage results is much less stringent than it is for whole genome scans that establish the significant linkage results (44). This is because replication studies are carried out to test specific hypotheses established concerning specific genomic intervals and not concerning the null hypothesis about the whole genome. Depending on the number (k) of markers genotyped in specific genomic intervals to be tested, the significance level required in a linkage replication study is generally 0.05/k, so that the interval-wide significance level is maintained at 0.05 level. If multiple tests involving multiple traits are conducted, as is the case in this study, the significance level may need to be further adjusted to account for the number of traits analyzed. Therefore, the minimum P value (0.14) achieved in this study indicates that we cannot replicate significant linkage results to BMD variation (16, 26) concerning the genomic region surrounding the marker D11S987 on chromosome 11q12–13 in our population of healthy Caucasians.
Acknowledgments
We thank Dr. Tim Keith for helpful suggestions that helped to improve the manuscript.
Footnotes
This study was supported by grants from the NIH, Health Future Foundation, U.S. Department of Energy, State of Nebraska (Department of Health and Human Services Cancer and Smoking Related Disease Research Program, Grant LB595), U.S. Department of Energy, and Creighton University.
Abbreviations: BMC, Bone mineral content; BMD, bone mineral density; HBM, high bone mass; LOD, logarithm of odds; QTL, quantitative trait loci; sib, sibling; SOLAR, Sequential Oligogenic Linkage Analysis Routines; TBBMC, total body BMC.
Received December 19, 2000.
Accepted April 25, 2001.
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