Microsatellites are a major type of molecular markers in genetics studies. Their mutational dynamics are not clear. We investigated the patterns and characteristics of 97 mutation events unambiguously identified, from 53 multigenerational pedigrees with 630 subjects, at 362 autosomal dinucleotide microsatellite loci. A size-dependent mutation bias (in which long alleles are biased toward contraction, whereas short alleles are biased toward expansion) is observed. There is a statistically significant negative relationship between the magnitude (repeat numbers changed during mutation) and direction (contraction or expansion) of mutations and standardized allele size. Contrasting with earlier findings in humans, most mutation events (63%) in our study are multistep events that involve changes of more than one repeat unit. There was no correlation between mutation rate and recombination rate. Our data indicate that mutational dynamics at microsatellite loci are more complicated than the generalized stepwise mutation models.

Obesity is an increasingly serious health problem in the world. Body mass index (BMI), percentage fat mass, and body fat mass are important indices of obesity. For a sample of pedigrees that contains >10,000 relative pairs (including 1,249 sib pairs) that are useful for linkage analyses, we performed a whole-genome linkage scan, using 380 microsatellite markers to identify genomic regions that may contain quantitative-trait loci (QTLs) for obesity. Each pedigree was ascertained through a proband who has extremely low bone mass, which translates into a low BMI. A major QTL for BMI was identified on 2q14 near the marker D2S347 with a LOD score of 4.04 in two-point analysis and a maximum LOD score (MLS) of 4.44 in multipoint analysis. The genomic region near 2q14 also achieved an MLS>2.0 for percentage of fat mass and body fat mass. For the putative QTL on 2q14, as much as 28.2% of BMI variation (after adjustment for age and sex) may be attributable to this locus. In addition, several other genomic regions that may contain obesity-related QTLs are suggested. For example, 1p36 near the marker D1S468 may contain a QTL for BMI variation, with a LOD score of 2.75 in two-point analysis and an MLS of 2.09 in multipoint analysis. The genomic regions identified in this and earlier reports are compared for further exploration in extension studies that use larger samples and/or denser markers for confirmation and fine-mapping studies, to eventually identify major functional genes involved in obesity.

We investigate the relevance of the genetic determination of bone mineral density (BMD) variation to that of differential risk to osteoporotic fractures (OF). The high heritability (h(2)) of BMD and the significant phenotypic correlations between high BMD and low risk to OF are well known. Little is reported on h(2) for OF. Extensive molecular genetic studies aimed at uncovering genes for differential risks to OF have focussed on BMD as a surrogate phenotype. However, the relevance of the genetic determination of BMD to that of OF is unknown. This relevance can be characterized by genetic correlation between BMD and OF. For 50 Caucasian pedigrees, we estimated that h(2) at the hip is 0.65 (P < 0.0001) for BMD and 0.53 (P < 0.05) for OF; however, the genetic correlation between BMD and OF is nonsignificant (P > 0.45) and less than 1% of additive genetic variance is shared between them. Hence, most genes found important for BMD may not be relevant to OF at the hip. The phenotypic correlation between high BMD and low risk to OF at the hip (approximately -0.30) is largely due to an environmental correlation (rho(E) = -0.73, P < 0.0001). The search for genes for OF should start with a significant h(2) for OF and should include risk factors (besides BMD) that are genetically correlated with OF. All genes found important for various risk factors must be tested for their relevance to OF. Ideally, employing OF per se as a direct phenotype for gene hunting and testing can ensure the importance and direct relevance of the genes found for the risk of OF. This study may have significant implications for the common practice of gene search for complex diseases through underlying risk factors (usually quantitative traits).

Osteoporosis is an important health problem, particularly in the elderly women. Bone mineral density (BMD) is a major determinant of osteoporosis. For a sample of 53 pedigrees that contain 1249 sibling pairs, 1098 grandparent-grandchildren pairs, and 2589 first cousin pairs, we performed a whole- genome linkage scan using 380 microsatellite markers to identify genomic regions that may contain quantitative trait loci (QTL) of BMD. Each pedigree was ascertained through a proband with BMD values belonging to the bottom 10% of the population. We conducted two-point and multipoint linkage analyses. Several potentially important genomic regions were suggested. For example, the genomic region near the marker D10S1651 may contain a QTL for hip BMD variation (with two-point analysis LOD score of 1.97 and multipoint analysis LOD score of 2.29). The genomic regions near the markers D4S413 and D12S1723 may contain QTLs for spine BMD variation (with two-point analysis LOD score of 2.12 and 2.17 and multipoint analysis LOD score of 3.08 and 2.96, respectively). The genomic regions identified in this and some earlier reports are compared for exploration in extension studies with larger samples and/or denser markers for confirmation and fine mapping to eventually identify major functional genes involved in osteoporosis.

Human height is a complex trait under the control of both genetic and environment factors. In order to identify genomic regions underlying the variation of stature, we performed a whole-genome linkage analysis on a sample of 53 human pedigrees containing 1,249 sib pairs, 1,098 grandparent-grandchildren pairs, 1,993 avuncular pairs, and 1,172 first-cousin pairs. Several genomic regions were suggested by our study to be linked with human height variation. These regions include 5q31 at 144 cM from pter on chromosome 5 (with a maximum LOD score of 2.14 in multipoint linkage analyses), Xp22 at the marker DXS1060, and Xq25 at DXS1001 on the X chromosome (with LOD scores of 1.95 and 1.91, respectively, in two-point linkage analyses). Noticeably, Xp22 happens to be the very region where a newly identified gene underlying idiopathic short stature, SHOX, maps. Based on our findings, further confirmation and fine-mapping studies are to be pursued on expanded samples and/or with denser markers for eventual identification of major functional genes involved in human height variation.

A genome-wide screen was conducted using a large white sample to identify QTLs for FNCS geometry. We found significant linkage of FNCS parameters to 20q12 and Xq25, plus significant epistatic interactions and sex-specific QTLs influencing FNCS geometry variation.Introduction: Bone geometry, a highly heritable trait, is a critical component of bone strength that significantly determines osteoporotic fracture risk. Specifically, femoral neck cross-sectional (FNCS) geometry is significantly associated with hip fracture risk as well as genetic factors. However, genetic research in this respect is still in its infancy. Materials and Methods:To identify the underlying genomic regions influencing FNCS variables, we performed a remarkably large-scale whole genome linkage scan involving 3998 individuals from 434 pedigrees for four FNCS geometry parameters, namely buckling ratio (BR), cross-sectional area (CSA), cortical thickness (CT), and section modulus (Z). The major statistical approach adopted is the variance component method implemented in SOLAR. Results: Significant linkage evidence (threshold LOD ‫ס‬ 3.72 after correction for tests of multiple phenotypes) was found in the regions of 20q12 and Xq25 for CT (LOD ‫ס‬ 4.28 and 3.90, respectively). We also identified eight suggestive linkage signals (threshold LOD ‫ס‬ 2.31 after correction for multiple tests) for the respective geometry traits. The above findings were supported by principal component linkage analysis. Of them, 20q12 was of particular interest because it was linked to multiple FNCS geometry traits and significantly interacted with five other genomic loci to influence CSA variation. The effects of 20q12 on FNCS geometry were present in both male and female subgroups. Subgroup analysis also revealed the presence of sex-specific quantitative trait loci (QTLs) for FNCS traits in the regions such as 2p14, 3q26, 7q21 and 15q21. Conclusions: Our findings laid a foundation for further replication and fine-mapping studies as well as for positional and functional candidate gene studies, aiming at eventually finding the causal genetic variants and hidden mechanisms concerning FNCS geometry variation and the associated hip fractures.

Much work has been done on the association between vitamin D receptor (VDR) genotypes and bone mineral density (BMD). Despite considerable effort, the results are inconsistent. While the VDR association remains unresolved, studies have expanded to other candidate genes (i.e., estrogen receptor (ER) genotypes), also yielding inconsistent results. A few studies have suggested that interaction effects between VDR and ER genotypes significantly affect BMD. We assessed associations of BMD with VDR BsmI genotypes, and ER XbaI and PvuII polymorphisms (denoted as ERX and ERP respectively) with spine, femoral neck, distal radius BMD, and with total body bone mineral content (tbBMC) in 108 US Mid-western postmenopausal Caucasian women. We statistically controlled for confounding factors such as height, weight, etc., in the analysis. No significant association was detected for ER genotypes with spine and radius BMD, or for VDR genotypes with femoral neck and radius BMD and tbBMC. No significant interaction between VDR and ER genotypes was detected in our sample. However, the VDR genotypes are significantly (p = 0.004) associated with approximately 5.8% spine BMD variation. Both ERX and ERP genotypes are significantly (p = 0.02) associated with approximately 3.5% femoral neck BMD variation. ERX genotypes are significantly (p = 0.03) associated with approximately 2.4% tbBMC variation. However, if the data were analyzed by simple ANOVA as in some previous studies, without adjusting statistically for confounding factors, all the significant results we found here would have gone undetected. Our findings suggest that: (1) VDR and ER genotypes may have different effects on BMD at different sites and on tbBMC; and (2) if significant factors influencing bone are not appropriately controlled, true significant associations can easily be missed. These findings may offer a partial explanation for some of the earlier inconsistent results of association studies on BMD with VDR and ER genotypes.

Human height is an important and heritable trait. Our previous two genome-wide linkage studies using 630 (WG1 study) and an extended sample of 1,816 Caucasians (WG2 study) identified 9q22 [maximum LOD score (MLS)=2.74 in the WG2 study] and preliminarily confirmed Xq24 (two-point LOD score=1.91 in the WG1 study, 2.64 in the WG2 study) linked to height. Here, with a much further extended large sample containing 3,726 Caucasians, we performed a new genome-wide linkage scan and confirmed, in high significance, the two regions' linkage to height. An MLS of 4.34 was detected on 9q22 and a two-point LOD score of 5.63 was attained for Xq24. In an independent sub-sample (i.e., the subjects not involved in the WG1 and WG2 studies), the two regions also achieved significant empirical P values (0.002 and 0.004, respectively) for "region-wise" linkage confirmation. Importantly, the two regions were replicated on a genotyping platform different from the WG1 and WG2 studies (i.e., a different set of markers and different genotyping instruments). Interestingly, 9q22 harbors the ROR2 gene, which is required for growth plate development, and Xq24 was linked to short stature. With the largest sample from a single population of the same ethnicity in the field of linkage studies for complex traits, our current study, together with two previous ones, provided overwhelming evidence substantiating 9q22 and Xq24 for height variation. In particular, our three consecutive whole genome studies are uniquely valuable as they represent the first practical (rather than simulated) example of how significant increase in sample size may improve linkage detection for human complex traits.