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A Genome Wide Linkage Scan of Metacarpal Size and Geometry in the Framingham Study
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Bone geometry is a significant component of bone strength, and has a clinical utility in predicting fractures and quantifying bone loss. Bone geometry is known to have a substantial genetic component. We performed linkage analysis to identify chromosomal regions governing metacarpal bone geometry. A genome-wide scan (with a set of 615 markers with spacing of approximately 5.7 cM) was performed on 1,702 individuals from 330 extended families of the Framingham Study. Midshaft width was measured and several indices calculated, namely Metacarpal Cortical Thickness (MCT), Cortical Index (MCI), and Section Modulus (MZ), using digitized X-rays of 1,380 participants (men, n = 666, mean age 55.2 yr, women, n = 714, 55.5 yr). Metacarpals 2, 3, and 4 were averaged. Heritability was significant for all indices, ranging from 0.51 to 0.72. Linkage analysis of indices adjusted for age, age(2), and estrogen status in women, identified chromosomal regions 6p21, 9p21, 11q21-q22, and Xq26-Xq27, with LOD scores >2.0. Additional adjustment for smoking, height, and BMI, generally reduced the LOD scores. Finally, bivariate linkage analysis confirmed that a QTL on chr. 6 (51 cM) was shared by midshaft width and MZ (LOD = 2.40, adjusted for all covariates). Neither MCT nor MCI shared linkage loci with width or MZ. In conclusion, we have identified chromosomal regions potentially linked to bone geometry. Genes in these regions may regulate bone geometry via effects on body size. Identification and subsequent characterization of loci for bone geometry can further elucidate the genetic contributions to bone's resistance to stress.
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... The results of this study are consistent with the notion that the genes directly involved in bone formation, metabolic and inherited bone diseases and osteoporosis contribute little, if anything, to SF pathogenesis. In support of this notion, analysis of the chromosomal location of the sequence variants found in the present study as putatively associated with SF pathogenesis (n = 146) showed that none co-localized with any of the SNPs or genes previously associated with metacarpal bone geometry (Karasik et al., 2008), trabecular and cortical volumetric bone mineral densities (Paternoster et al., 2010(Paternoster et al., , 2013, osteoporosis-related phenotype (Karasik et al., 2012), bone mass, hip geometry and fractures (Boudin et al., 2013;Garcia-Ibarbia et al., 2013), bone mineral density, cortical bone thickness and bone strength , bone mineral density loci and osteoporotic fracture (Duncan et al., 2011;Estrada et al., 2012), and femoral neck bone geometry (Zhao et al., 2010) (Supplemental Table 6), including the ones that were reviewed by Ralston & Uitterlinden (2010) (Supplemental Table 7). If the results of the present study are validated in a larger study, of ethnically diverse individuals with SF, the data can be used for several clinical applications: identifying pre-symptomatic individuals at high risk for developing SF and offering them a modified training regimen, and possibly using the up-regulated gene products as surrogate markers for diagnosing SF without using radiation-emitting modalities. ...
Summary While genetic factors in all likelihood contribute to stress fracture (SF) pathogenesis, a few studies focusing on candidate genes have previously been reported. The objective of this study is to gain better understanding on the genetic basis of SF in a gene-naive manner. Exome sequence capture followed by massive parallel sequencing of two pooled DNA samples from Israeli combat soldiers was employed: cases with high grade SF and ethnically matched healthy controls. The resulting sequence variants were individually verified using the Sequenom™ platform and the contribution of the genetic alterations was validated in a second cohort of cases and controls. In the discovery set that included DNA pool of cases (n = 34) and controls (n = 60), a total of 1174 variants with >600 reads/variant/DNA pool were identified, and 146 (in 127 genes) of these exhibited statistically significant (P < 0·05) different rates between SF cases and controls after multiple comparisons correction. Subsequent validation of these 146 sequence variants individually in a total of 136 SF cases and 127 controls using the Sequenom™ platform validated 20/146 variants. Of these, three missense mutations (rs7426114, rs4073918, rs3752135 in the NEB, SLC6A18 and SIGLEC12 genes, respectively) and three synonymous mutations (rs2071856, rs2515941, rs716745 in the ELFN2, GRK4, LRRC55 genes) displayed significant different rates in SF cases compared with controls. Exome sequencing seemingly unravelled novel candidate genes as involved in SF pathogenesis and predisposition.
... Pubertal timing and bone strength are each under genetic influence [60][61][62]. Studies of genetic underpinnings of skeletal traits frequently show heritabilities of 50-65% [37,63,64], with the other proportion of trait variance due to the environment. ...
Elucidating the somatic and maturational influences on the biomechanical properties of bone in children is crucial for a proper understanding of bone strength and quality in childhood and later life, and has significant potential for predicting adult fracture and osteoporosis risks. The ability of a long bone to resist bending and torsion is primarily a function of its cross-sectional geometric properties, and is negatively impacted by smaller external bone diameter. In pubescent girls, elevated levels of estrogen impede subperiosteal bone growth and increase endosteal bone deposition, resulting in bones averaging a smaller external and internal diameter relative to boys. In addition, given a well-documented secular trend for an earlier menarche, the age at which the rate of subperiosteal bone deposition decreases may also be younger in more recent cohorts of girls. In this study we examined the relationship between pubertal timing and subsequent bone strength in girls. Specifically, we investigated the effects of age at menarche on bone strength indicators (polar moment of inertia and section modulus) determined from cross-sectional geometry of the second metacarpal (MC2) using data derived from serial hand-wrist radiographs of female participants (N=223) in the Fels Longitudinal Study, with repeated measures of MC2 between the ages of 7 and 35 years. Using multivariate regression models, we evaluated the effects of age at menarche on associations between measures of bone strength in early adulthood and the same measures at a prepubertal age. Results indicate that later age at menarche is associated with stronger adult bone (in torsion and bending) when controlling for prepubertal bone strength (R(2) ranged between 0.54 and 0.70, p<0.001). Since cross-sectional properties of bone in childhood may have long lasting implications, they should be considered along with pubertal timing in assessing risk for future fracture and in clinical recommendations.
... As BMD measures alone are insufficient to predict future fracture in many patients, much effort has focused on identifying other clinically measurable phenotypes that either alone or in concert with BMD can be used to better predict fracture risk, such as long bone geometry. Measures of bone geometry can be used to predict risk of osteoporotic fractures [3][4][5][6] and these phenotypes are also heritable [7][8][9]. Therefore, identification of genes that regulate BMD and bone geometry will enhance our understanding of osteoporosis etiology. The assumption has been that finding the genetic causes of this disease would lead to better fracture risk prediction and new and better treatment options. ...
Trps1 has been proposed as a candidate gene for a mouse bone mineral density (BMD) QTL on Chromosome (Chr) 15, but it remained unclear if this gene was associated with BMD in humans. We used newly available data and advanced bioinformatics techniques to confirm that Trps1 is the most likely candidate gene for the mouse QTL. In short, by combining the raw genetic mapping data from two F2 generation crosses of inbred strains of mice, we narrowed the 95% confidence interval of this QTL down to the Chr 15 region spanning from 6 to 24cM. This region contains 131 annotated genes. Using block haplotyping, all other genes except Trps1 were eliminated as candidates for this QTL. We then examined associations of 208 SNPs within 10kb of TRPS1 with BMD and hip geometry, using human genome-wide association study (GWAS) data from the GEFOS consortium. After correction for multiple testing, six TRPS1 SNPs were significantly associated with femoral neck BMD (P=0.0015-0.0019; adjusted P=0.038-0.048). We also found that three SNPs were highly associated with femoral neck width in women (rs10505257, P=8.6×10(-5), adjusted P=2.15×10(-3); rs7002384, P=5.5×10(-4), adjusted P=01.38×10(-2)). In conclusion, we demonstrated that combining association studies in humans with murine models provides an efficient strategy to identify new candidate genes for bone phenotypes.
... The algorithm implemented in the software then determined the coordinates of outlined bone, generated a mass profile, and converted the data into the cortical macrostructure. For our analyses we focused on lengths of the second through fourth metacarpals and averaged them as previously described [41]. Finally, considering height to be a bone-related phenotype, it was measured to the nearest one-fourth inch using a stadiometer [35, 37]. ...
There is increasing interest in identifying new pathways and candidate genes that confer susceptibility to osteoporosis. There is evidence that adipogenesis and osteogenesis may be related, including a common bone marrow progenitor cell for both adipocytes and osteoblasts. Perilipin 1 (PLIN1) and Perilipin 4 (PLIN4) are members of the PATS family of genes and are involved in lipolysis of intracellular lipid deposits. A previous study reported gender-specific associations between one polymorphism of PLIN1 and bone mineral density (BMD) in a Japanese population. We hypothesized that polymorphisms in PLIN1 and PLIN4 would be associated with bone measures in adult Caucasian participants of the Framingham Osteoporosis Study (FOS). We genotyped 1,206 male and 1,445 female participants of the FOS for four single-nucleotide polymorphism (SNPs) in PLIN1 and seven SNPs in PLIN4 and tested for associations with measures of BMD, bone ultrasound, hip geometry, and height. We found several gender-specific significant associations with the measured traits. The association of PLIN4 SNP rs8887, G>A with height in females trended toward significance after simulation testing (adjusted P = 0.07) and remained significant after simulation testing in the combined-sex model (adjusted P = 0.033). In a large study sample of men and women, we found a significant association between one SNP in PLIN4 and height but not with bone traits, suggesting that PATS family genes are not important in the regulation of bone. Identification of genes that influence human height may lead to a better understanding of the processes involved in growth and development.
... Therefore, risk factors for osteoporotic fracture (proxy phenotypes), such as bone mineral density (BMD), bone quantitative ultrasound (QUS), and bone geometry are traditionally investigated, since these quantitative traits predict risk of osteoporotic fractures (2)(3)(4)(5) and may be reliably measured at any age. Studies over the last decades have documented the major contribution of genes to BMD (6)(7) , QUS (8) , and bone geometry (9)(10) . Since fracture is a product of both bone strength and forces applied to the skeleton, muscle mass has also been considered to be a risk factor for fractures (11) ; moreover, it is characterized by a shared heritability with bone strength properties (12)(13) . ...
Multiple musculoskeletal traits assessed by various methods at different skeletal sites serve as surrogates for osteoporosis risk. However, it is a challenge to select the most relevant phenotypes for genetic study of fractures. Principal component analyses (PCA) were conducted in participants of the Framingham Osteoporosis Study on 17 measures including bond mineral density (BMD) (hip and spine), heel ultrasound, leg lean mass (LLM), and hip geometric indices, adjusting for covariates (age, height, body mass index [BMI]), in a combined sample of 1180 men and 1758 women, as well as in each sex. Four principal components (PCs) jointly explained ~69% of the total variability of musculoskeletal traits. PC1, explaining ~33% of the total variance, was referred to as the component of "Bone strength," because it included the hip and spine BMD as well as several hip cross-sectional properties. PC2 (20.5% variance) was labeled as "Femoral cross-sectional geometry;" PC3 (~8% variance) captured only ultrasound measures; PC4, explaining ~7% variance, was correlated with LLM and hip geometry. We then evaluated ~2.5 mil SNPs for association with PCs 1, 2, and 4. There were genome-wide significant associations (p < 5 × 10⁻⁸) between PC2 and HTR1E (that codes for one of the serotonin receptors) and PC4 with COL4A2 in women. In the sexes-combined sample, AKAP6 was associated with PC2 (p = 1.40 × 10⁻⁷). A single nucleotide polymorphism (SNP) in HTR1E was also associated with the risk of nonvertebral fractures in women (p = 0.005). Functions of top associated genes were enriched for the skeletal and muscular system development (p < 0.05). In conclusion, multivariate combination provides genetic associations not identified in the analysis of primary phenotypes. Genome-wide screening for the linear combinations of multiple osteoporosis-related phenotypes suggests that there are variants with potentially pleiotropic effects in established and novel pathways to be followed up to provide further evidence of their functions.
... The lack of differences in body size parameters (weight, height, BMI) among robustness tertiles in the current study further suggested the biological processes regulating robustness are not easily related to those regulating body size. In fact, heritability estimates for metacarpal width are high, ranging from 64–72% after adjusting for cofactors such as sex, age, height, and weight [6, 15]. Prior work in inbred mouse strains identified quantitative trait loci (QTLs) harboring genes regulating bone length, periosteal circumference, and body weight individually, as well as QTLs harboring genes regulating the relationship among these traits [23]. ...
A better understanding of bone growth will benefit efforts to reduce fracture incidence, because variation in elderly bone traits is determined primarily by adulthood. The natural variation in robustness was used as a model to understand how variable growth patterns define adult bone morphology. Longitudinally acquired hand radiographs of 29 boys and 30 girls were obtained from the Bolton-Brush study for 6 time points spanning 8 to 18 years of age. Segregating individuals into tertiles based on robustness revealed that the biological activity underlying bone growth varied significantly with the natural variation in robustness. For boys, slender metacarpals used an osteoblast-dependent growth pattern to establish function, whereas robust metacarpals used an osteoclast-dependent growth pattern. In contrast, differences in biological activity between girls with slender and robust metacarpals were largely based on the age at which the marrow surface changed from expansion to infilling. Importantly, cortical area for slender metacarpals was as much as 19.7% and 32.2% lower than robust metacarpals for boys and girls, respectively, indicating that robustness was a major determinant of adult cortical area. Finally, after accounting for robustness and body weight effects, we found that the inter-individual variation in cortical area was established as early as 8 years of age. While variation in the amount of bone acquired during growth has primarily been attributed to factors like nutrition, exercise, and genetic background, we showed that the natural variation in robustness was also a major determinant of cortical area, which is an important determinant of bone mass. This predictable relationship between robustness and cortical area should be incorporated into clinical diagnostic measures and experimental studies.
Schela Cladovei belongs to a series of well-known Late Mesolithic and Early Neolithic sites along a stretch of the Danube River in and near gorges through the Carpathian-Balkan mountain chain known as the Iron Gates region. However, the Schela Cladovei Mesolithic sample is an exception, being as tall, on average, as Early Upper Paleolithic (UP) samples from other parts of Europe, and taller than other Late UP samples. Thus, the decline in stature during the Terminal Pleistocene and early Holocene characteristic of Western Europe does not appear to apply to this region, although without UP material from the Balkans it is difficult to evaluate temporal trends in this area. The two available skeletal samples for the Balkans region represent two very different time periods, subsistence strategies, and likely genetic heritages. They share some similarities in that both lived in a rugged physical environment and neither practiced food production, that is, agriculture.
In many applications, it is of interest to simultaneously cluster row and column variables in a data set, identifying local subgroups within a data matrix that share some common characteristic. When a small set of variables is believed to be associated with a set of responses, block clustering or biclustering is a more appropriate technique to use compared to one-dimensional clustering. A flexible framework for Bayesian model-based block clustering, that can determine multiple block clusters in a data matrix through a novel and efficient evolutionary Monte Carlo-based methodology, is proposed. The performance of this methodology is illustrated through a number of simulation studies and an application to data from genome-wide association studies.
Efforts to find disease genes using high-density single-nucleotide polymorphism (SNP) maps will produce data sets that exceed the limitations of current computational tools. Here we describe a new, efficient method for the analysis of dense genetic maps in pedigree data that provides extremely fast solutions to common problems such as allele-sharing analyses and haplotyping. We show that sparse binary trees represent patterns of gene flow in general pedigrees in a parsimonious manner, and derive a family of related algorithms for pedigree traversal. With these trees, exact likelihood calculations can be carried out efficiently for single markers or for multiple linked markers. Using an approximate multipoint calculation that ignores the unlikely possibility of a large number of recombinants further improves speed and provides accurate solutions in dense maps with thousands of markers. Our multi-point engine for rapid likelihood inference (Merlin) is a computer program that uses sparse inheritance trees for pedigree analysis; it performs rapid haplotyping, genotype error detection and affected pair linkage analyses and can handle more markers than other pedigree analysis packages.
The major aim of this study was to test three hypotheses: 1) more complex traits of the hand are less prone to developmental insults and therefore show lower fluctuating asymmetry (FA) as compared with simple traits; 2) the manifestation of FA correlates with the variability of the trait (i.e., CV); and 3) FA is an organ-wide property, and therefore a concordance exists between the FA measures of different traits in hand bones. Seventy-two bilateral measurements of hand bones, were made from plain-film radiographs of 365 cadavers. A complex trait was considered as the total length of the three phalanges of a finger and their contiguous metacarpals. Simple traits were considered to be the lengths of individual bone that made up the complex trait. The following results were obtained: 1) on the average simple traits, composing the complex trait, show much higher FA than the corresponding complex trait, but this result is expected if there is no correlation (or low correlation) between FA of simple traits within the complex trait, due to random direction of right-left differences; 2) strong and highly significant correlation was observed between FA and CV of studied traits, regardless of sex and age of individuals; and 3) the majority of FA measurements of hand bones showed no correlation. However, correlations between some sets of FA traits were highly significant. They were interpreted, although not specifically tested, as the result of a tight relationship between traits related not only developmentally but also by active performance of the same function. Am J Phys Anthropol 107:125–136, 1998. © 1998 Wiley-Liss, Inc.
Cross-sectional data on 2799 subjects from five different populations and longitudinal data on 113 older adults indicate continuing adult bone growth in the second metacarpal. Similar 6-decade increases in the size of the cranium confirm continuing bone growth as a general phenomenon not necesarily related to weightbearing or flexion stress and representing an increase of approximately 10% in skeletal volume concomitant with the major age-associated decrease in skeletal mass.
The variance component method is now widely used for linkage analysis of quantitative traits. Although this approach offers many advantages, the importance of the underlying assumption of multivariate normality of the trait distribution within pedigrees has not been studied extensively. Simulation studies have shown that traits with leptokurtic distributions yield linkage test statistics that exhibit excessive Type I error when analyzed naively. We derive analytical formulae relating the deviation from the expected asymptotic distribution of the lod score to the kurtosis and total heritability of the quantitative trait. A simple correction constant yields a robust lod score for any deviation from normality and for any pedigree structure, and effectively eliminates the problem of inflated Type I error due to misspecification of the underlying probability model in variance component-based linkage analysis. Genet. Epidemiol. 19(Suppl 1):S8–S14, 2000. © 2000 Wiley-Liss, Inc.
Power to detect linkage and localization of a major gene were compared in univariate and bivariate variance components linkage analysis of three related quantitative traits in general pedigrees. Although both methods demonstrated adequate power to detect loci of moderate effect, bivariate analysis improved both power and localization for correlated quantitative traits mapping to the same chromosomal region, regardless of whether co-localization was the result of pleiotropy. Additionally, a test of pleiotropy versus co-incident linkage was shown to have adequate power and a low error rate. © 1997 Wiley-Liss, Inc.
Multipoint linkage analysis of quantitative-trait loci (QTLs) has previously been restricted to sibships and small pedigrees. In this article, we show how variance-component linkage methods can be used in pedigrees of arbitrary size and complexity, and we develop a general framework for multipoint identity-by-descent (IBD) probability calculations. We extend the sib-pair multipoint mapping approach of Fulker et al. to general relative pairs. This multipoint IBD method uses the proportion of alleles shared identical by descent at genotyped loci to estimate IBD sharing at arbitrary points along a chromosome for each relative pair. We have derived correlations in IBD sharing as a function of chromosomal distance for relative pairs in general pedigrees and provide a simple framework whereby these correlations can be easily obtained for any relative pair related by a single line of descent or by multiple independent lines of descent. Once calculated, the multipoint relative-pair IBDs can be utilized in variance-component linkage analysis, which considers the likelihood of the entire pedigree jointly. Examples are given that use simulated data, demonstrating both the accuracy of QTL localization and the increase in power provided by multipoint analysis with 5-, 10-, and 20-cM marker maps. The general pedigree variance component and IBD estimation methods have been implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package.
A relationship between stature and second metacarpal length was examined by means of a linear regression for sex, skeletal age and locality in 2056 children aged 6-19 years in five districts of Japan. Significant differences (p less than 0.05) were found for the regression of two measurements between immature and mature groups according to the TW2 method. Few significant differences were found in the regression with sex and locality in both immature and mature groups. Stature could be estimated from second metacarpal length with standard errors of 44mm in the immature group and 40mm in the mature group. Furthermore, from the bone length and TW2 age, stature could be estimated with a standard error of 38mm for each sex in combined groups. These figures are similar to the variability in stature at a given age and comparable to reliability of estimates from long bones. The second metacarpal length may be a reliable and practical marker in children for the estimation of stature by means of a general formula regardless of sex and locality in a population.
This longitudinal study was undertaken to ascertain the rate of bone loss and to identify aging, cohort and/or time effects on bone loss in male participants of the Baltimore Longitudinal Study of Aging. Hand-wrist radiographs were obtained from 1958-1981 and were evaluated for total width, medullary width, and length of the second metacarpal. Data were analyzed using an age-time matrix with 8-year intervals for three epochs and nine age groups. The bone measurements were analyzed in three perspectives (cross-sectional, longitudinal and time-series). The results demonstrate that there is both a cross-sectional and longitudinal loss of cortical bone with age in the second metacarpal. Furthermore, the results show that males lose approximately 14% of their cortical bone, at a rate of about 2% per decade, over the adult lifespan. The majority of this loss occurs between the ages of 45 and 69 and is due primarily to aging and is not an artifact of cohort differences or secular change.
An Indiana family segregating a syndrome of X-linked mental retardation and skeletal anomalies was tested for linkage of the mutant gene to X-chromosome molecular markers. Lod scores of 3.27 and 3.06 (theta = 0) for the molecular probes St14-1 (DXS52) and Dx13 (DXS15), respectively, indicate that the disease gene is located in the terminal portion of Xq.