Related Articles

Stature (adult body height), and body mass index (BMI) have a strong genetic component explaining observed variation in human populations, however, identifying those genetic components has been extremely challenging. It seems obvious that sample size is a critical determinant for successful identification of quantitative trait loci (QTL) that underlie the genetic architecture of these polygenic traits. The inherent shared environment and known genetic relationships in family studies provide clear advantages for gene mapping over studies utilizing unrelated individuals. To these ends, we combined the genotype and phenotype data from four previously performed family-based genome-wide screens resulting in a sample of 9.371 individuals from 3.032 African-American and European-American families and performed variance-components linkage analyses for stature and BMI. To our knowledge, this study represents the single largest family-based genome-wide linkage scan published for stature and BMI to date. This large study sample allowed us to pursue population-and sex-specific analyses as well. For stature we found evidence for linkage in previously reported loci on 11q23, 12q12, 15q25 and 18q23 as well as 15q26 and 19q13 which have not been linked to stature previously. For BMI we found evidence for two loci: one on 7q35 and another on 11q22 both of which have been previously linked to BMI in multiple populations. Our results show both the benefit of 1) combining data to maximize the sample size and 2) minimizing heterogeneity by analyzing subgroups where within-group variation can be reduced and suggest that the latter may be a more successful approach in genetic mapping.

To understand the underlying genetic architecture of cardiovascular disease (CVD) risk traits, we undertook a genome-wide linkage scan to identify CVD quantitative trait loci (QTLs) in 377 individuals from the Norfolk Island population. The central aim of this research focused on the utilization of a genetically and geographically isolated population of individuals from Norfolk Island for the purposes of variance component linkage analysis to identify QTLs involved in CVD risk traits. Substantial evidence supports the involvement of traits such as systolic and diastolic blood pressures (SBP and DBP), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), body mass index (BMI) and triglycerides (TG) as important risk factors for CVD pathogenesis. In addition to the environmental influences of poor diet, reduced physical activity, increasing age, cigarette smoking and alcohol consumption, many studies have illustrated a strong involvement of genetic components in the CVD phenotype through family and twin studies. We undertook a genome scan using 400 markers spaced approximately 10cM in 600 individuals from Norfolk Island. Genotype data was analyzed using the variance components methods of SOLAR. Our results gave a peak LOD score of 2.01 localizing to chromosome 1p36 for systolic blood pressure and replicated previously implicated loci for other CVD relevant QTLs.

Background

Family studies are often conducted in a cross-sectional manner without long-term follow-up data. The relative contribution of a gene to a specific trait could change over the lifetime. The Framingham Heart Study offers a unique opportunity to investigate potential gene × time interaction. We performed linkage analysis on the body mass index (BMI) measured in 1970, 1978, and 1986 for this project.

Results

We analyzed the data in two different ways: three genome-wide linkage analyses on each exam, and one genome-wide linkage analysis on the mean of the three measurements. Variance-component linkage analyses were performed by the SOLAR program. Genome-wide scans show consistent evidence of linkage of quantitative trait loci (QTLs) on chromosomes 3, 6, 9, and 16 in three measurements with a maximum multipoint LOD score > 2.2. However, only chromosome 9 has a LOD score = 2.14 when the mean values were analyzed. More interestingly, we found potential gene × environment interactions: increasing LOD scores with age on chromosomes 3, 9, and 16 and decreasing LOD scores on chromosome 6 in the three exams.

Conclusion

The results indicate two points: 1) it is possible that a gene (or genes) influencing BMI is (are) up- or down-regulated as people aged due to aging process or changes in lifestyle, environments, or genetic epistasis; 2) using mean values from longitudinal data may reduce the power to detect linkage and may have no power to detect gene × time, and/or gene × gene interactions.

Genome-wide association studies have identified many common genetic variants that are associated with polygenic traits, and have typically been performed with individuals of recent European ancestry. In these populations, many common variants are tightly correlated, with the perfect or near-perfect proxies for the functional or true variant showing equivalent evidence of association, considerably limiting the resolution of fine mapping. Populations with recent African ancestry often have less extensive and/or different patterns of linkage disequilibrium (LD), and have been proposed to be useful in fine-mapping studies. Here, we strongly replicate and fine map in populations of predominantly African ancestry the association between variation at the FTO locus and body mass index (BMI) that is well established in populations of European ancestry. We genotyped single nucleotide polymorphisms that are correlated with the signal of association in individuals of European ancestry but that have varying degrees of correlation in African-derived individuals. Most of the variants, including one previously proposed as functionally important, have no significant association with BMI, but two variants, rs3751812 and rs9941349, show strong evidence of association (P = 2.58 × 10−6 and 3.61 × 10−6 in a meta-analysis of 9881 individuals). Thus, we have both strongly replicated this association in African-ancestry populations and narrowed the list of potentially causal variants to those that are correlated with rs3751812 and rs9941349 in African-derived populations. This study illustrates the potential of using populations with different LD patterns to fine map associations and helps pave the way for genetically guided functional studies at the FTO locus.

Obesity is a complex phenotype affected by genetic and environmental influences such as sociocultural factors and individual behaviors. Previously, we performed two separate genome-wide investigations for adiposity-related traits (BMI, percentage body fat (%BF), abdominal circumference (ABDCIR), and serum leptin and serum adiponectin levels) in families from American Samoa and in families from Samoa. The two polities have a common evolutionary history but have lately been influenced by variations in economic development, leading to differences in income and wealth and in dietary and physical activity patterns. We now present a genome-wide linkage scan of the combined samples from the two polities. We adjust for environmental covariates, including polity of residence, education, cigarette smoking, and farm work, and use variance component methods to calculate univariate and bivariate multipoint lod scores. We identified a region on 9p22 with genome-wide significant linkage for the bivariate phenotypes ABDCIR–%BF (1-d.f. lod 3.30) and BMI–%BF (1-d.f. lod 3.31) and two regions with genome-wide suggestive linkage on 8p12 and 16q23 for adiponectin (lod 2.74) and the bivariate phenotype leptin–ABDCIR (1-d.f. lod 3.17), respectively. These three regions have previously been reported to be linked to adiposity-related phenotypes in independent studies. However, the differences in results between this study and our previous polity-specific studies suggest that environmental effects are of different importance in the samples. These results strongly encourage further genetic studies of adiposity-related phenotypes where extended sets of carefully measured environmental factors are taken into account.

Background: Many genome-wide scans aimed at complex traits have been statistically underpowered due to small sample size. Combining data from several genome-wide screens with comparable quantitative phenotype data should improve statistical power for the localisation of genomic regions contributing to these traits.

Objective: To perform a genome-wide screen for loci affecting adult stature by combined analysis of four previously performed genome-wide scans.

Methods: We developed a web based computer tool, Cartographer, for combining genetic marker maps which positions genetic markers accurately using the July 2003 release of the human genome sequence and the deCODE genetic map. Using Cartographer, we combined the primary genotype data from four genome-wide scans and performed variance components (VC) linkage analyses for human stature on the pooled dataset of 1417 individuals from 277 families and performed VC analyses for males and females separately.

Results: We found significant linkage to stature on 1p21 (multipoint LOD score 4.25) and suggestive linkages on 9p24 and 18q21 (multipoint LOD scores 2.57 and 2.39, respectively) in males-only analyses. We also found suggestive linkage to 4q35 and 22q13 (multipoint LOD scores 2.18 and 2.85, respectively) when we analysed both females and males and to 13q12 (multipoint LOD score 2.66) in females-only analyses.

Conclusions: We strengthened the evidence for linkage to previously reported quantitative trait loci (QTL) for stature and also found significant evidence of a novel male-specific QTL on 1p21. Further investigation of several interesting candidate genes in this region will help towards characterisation of this first sex-specific locus affecting human stature.

With the availability of longitudinal data, age-specific (stratified) or age-adjusted genetic analyses have the potential to localize different putative trait influencing loci. If age does not influence the locus-specific penetrance function within the range examined, age-stratified analyses will tend to yield comparable results for an individual trait. However, age-stratified results should vary across age strata when the locus-specific penetrance function is age dependent. In this paper, age-stratified and age-adjusted quantitative trait loci (QTL) linkage analyses were contrasted for height, weight, body mass index (BMI), and systolic blood pressure on a subset of the Framingham Heart Study. The strata comprised individuals with data present in each of three age groups: 31–49, 50–60, 61–79. Genome-wide QTL analyses were performed using SOLAR. Over all ages, a linkage signal for height was detected on chromosome 14q11.2 near marker GATA74E02A (LOD for ages 31–49 = 2.38, LOD for ages 50–60 = 1.84, LOD for ages 61–79 = 2.45). Evidence of linkage to BMI in the 31–49 age group was found on chromosome 3q22 (GATA3C02, LOD = 2.89, p = 0.0003) at the same location as the signal for weight (LOD = 3.10, p = 0.0002). Linkage was also supported on chromosome 1p22.1 for BMI (LOD = 2.21, p = 0.0014) and weight (LOD = 2.47, p = 0.0007) in the 31–49 age group. Our age-stratified results suggest that QTL that are expressed over long periods of time and affecting multiple, correlated traits may be identified using genome scan and variance-component methodology to help detect early and/or late gene expression.

To increase the likelihood of finding genetic variation conferring liability to eating disorders, we measured over 100 attributes thought to be related to liability to eating disorders on affected individuals from multiplex families and two cohorts: one recruited through a proband with anorexia nervosa (AN; AN cohort); the other recruited through a proband with bulimia nervosa (BN; BN cohort). By a multilayer decision process based on expert evaluation and statistical analysis, six traits were selected for linkage analysis (1): obsessionality (OBS), age at menarche (MENAR) and anxiety (ANX) for quantitative trait locus (QTL) linkage analysis; and lifetime minimum Body Mass Index (BMI), concern over mistakes (CM) and food-related obsessions (OBF) for covariate-based linkage analysis. The BN cohort produced the largest linkage signals: for QTL linkage analysis, four suggestive signals: (for MENAR, at 10p13; for ANX, at 1q31.1, 4q35.2, and 8q13.1); for covariate-based linkage analyses, both significant and suggestive linkages (for BMI, one significant [4q21.1] and three suggestive [3p23, 10p13, 5p15.3]; for CM, two significant [16p13.3, 14q21.1] and three suggestive [4p15.33, 8q11.23, 10p11.21]; and for OBF, one significant [14q21.1] and five suggestive [4p16.1, 10p13.1, 8q11.23, 16p13.3, 18p11.31]). Results from the AN cohort were far less compelling: for QTL linkage analysis, two suggestive signals (for OBS at 6q21 and for ANX at 9p21.3); for covariate-based linkage analysis, five suggestive signals (for BMI at 4q13.1, for CM at 11p11.2 and 17q25.1, and for OBF at 17q25.1 and 15q26.2). Overlap between the two cohorts was minimal for substantial linkage signals.

Background

Left ventricular mass (LVM) is an important risk factor for stroke and vascular disease. The genetic basis of LVM is unclear although a high heritability has been suggested. We sought to map quantitative trait loci (QTL) for LVM using large Dominican families.

Methods

Probands were selected from Dominican subjects of the population-based Northern Manhattan Study (NOMAS). LVM was measured by transthoracic echocardiography. A set of 405 microsatellite markers was used to screen the whole genome among 1360 subjects from 100 Dominican families who had complete phenotype data and DNA available. A polygenic covariate screening was run to identify the significant covariates. Variance components analysis was used to estimate heritability and to detect evidence for linkage, after adjusting for significant risk factors. Ordered-subset Analysis (OSA) was conducted to identify a more homogeneous subset for stratification analysis.

Results

LVM had a heritability of 0.58 in the studied population (p < 0.0001). The most significant evidence for linkage was found at chromosome 12p11 (MLOD = 3.11, empirical p = 0.0003) with peak marker at D12S1042. This linkage was significantly increased in a subset of families with the high average waist circumference (MLOD = 4.45, p = 0.0045 for increase in evidence for linkage).

Conclusion

We mapped a novel QTL near D12S1042 for LVM in Dominicans. Enhanced linkage evidence in families with larger waist circumference suggests that gene(s) residing within the QTL interact(s) with abdominal obesity to contribute to phenotypic variation of LVM. Suggestive evidence for linkage (LOD = 1.99) has been reported at the same peak marker for left ventricular geometry in a White population from the HyperGEN study, underscoring the importance of this QTL for left ventricular phenotype. Further fine mapping and validation studies are warranted to identify the underpinning genes.

In our ongoing effort to identify genes influencing the biological pathways that underlie the metabolic disturbances associated with obesity, we performed genome-wide scanning in 2,209 individuals distributed over 507 Caucasian families to localize quantitative trait loci (QTLs), which affect variation of plasma lipids. Pedigree-based analysis using a quantitative trait variance component linkage method that localized a QTL on chromosome 7q35-q36, which linked to variation in levels of plasma triglyceride [TG, logarithm of odds (LOD) score = 3.7] and was suggestive of linkage to LDL-cholesterol (LDL-C, LOD = 2.2). Covariates of the TG linkage included waist circumference, fasting insulin, and insulin:glucose, but not body mass index or hip circumference. Plasma HDL-cholesterol (HDL-C) levels were suggestively linked to a second QTL on chromosome 12p12.3 (LOD = 2.6). Five other QTLs with lower LOD scores were identified for plasma levels of LDL-C, HDL-C, and total cholesterol. These newly identified loci likely harbor genetic elements that influence traits underlying lipid adversities associated with obesity.

Many decades of scientific investigation have proved the role of selective pressure in Homo Sapiens at least at the level of individual genes or loci. Nevertheless, there are examples of polygenic traits that are bound to be under selection, but studies devoted to apply population genetics methods to unveil such occurrence are still lacking. Stature provides a relevant example of well-studied polygenic trait for which is now available a genome-wide association study which has identified the genes involved in this trait, and which is known to be under selection. We studied the behavior of FST in a simulated toy model to detect population differentiation on a generic polygenic phenotype under selection. The simulations showed that the set of alleles involved in the trait has a higher mean FST value than those undergoing genetic drift only. In view of this we looked for an increase in the mean FST value of the 180 variants associated to human height. For this set of alleles we found FST to be significantly higher than the genomic background (p = 0.0356). On the basis of a statistical analysis we excluded that the increase was just due to the presence of outliers. We also proved as marginal the role played by local adaptation phenomena, even on different phenotypes in linkage disequilibrium with genetic variants involved in height. The increase of FST for the set of alleles involved in a polygenic trait seems to provide an example of symmetry breaking phenomenon concerning the population differentiation. The splitting in the allele frequencies would be driven by the initial conditions in the population dynamics which are stochastically modified by events like drift, bottlenecks, etc, and other stochastic events like the born of new mutations.

Twin cohorts provide a unique advantage for investigations of the role of genetics and environment in the etiology of variation in common complex traits by reducing the variance due to environment, age, and cohort differences. The GenomEUtwin (http://www.genomeutwin.org) consortium consists of eight twin cohorts (Australian, Danish, Dutch, Finnish, Italian, Norwegian, Swedish, and United Kingdom) with the total resource of hundreds of thousands of twin pairs. We performed quantitative trait locus (QTL) analysis of one of the most heritable human complex traits, adult stature (body height) using genome-wide scans performed for 3,817 families (8,450 individuals) derived from twin cohorts from Australia, Denmark, Finland, Netherlands, Sweden, and United Kingdom with an approximate ten-centimorgan microsatellite marker map. The marker maps for different studies differed and they were combined and related to the sequence positions using software developed by us, which is publicly available (https://apps.bioinfo.helsinki.fi/software/cartographer.aspx). Variance component linkage analysis was performed with age, sex, and country of origin as covariates. The covariate adjusted heritability was 81% for stature in the pooled dataset. We found evidence for a major QTL for human stature on 8q21.3 (multipoint logarithm of the odds 3.28), and suggestive evidence for loci on Chromosomes X, 7, and 20. Some evidence of sex heterogeneity was found, however, no obvious female-specific QTLs emerged. Several cohorts contributed to the identified loci, suggesting an evolutionarily old genetic variant having effects on stature in European-based populations. To facilitate the genetic studies of stature we have also set up a website that lists all stature genome scans published and their most significant loci (http://www.genomeutwin.org/stature_gene_map.htm).

Author Summary

Twin cohorts provide a unique advantage for research of the role of genetics and environment behind common complex traits by reducing the variance due to environment, age, and cohort differences. The GenomEUtwin consortium consists of eight twin cohorts with the total resource of hundreds of thousands of twin pairs (http://www.genomeutwin.org). We performed quantitative family-based genetic linkage analysis for one of the most heritable human complex traits, adult stature (body height), using genome-wide scans derived from twin cohorts from Australia, Denmark, Finland, Netherlands, Sweden, and United Kingdom. Age, sex, and country were adjusted for in the data analyses. Human stature was found to be very heritable across all the cohorts and in the combined dataset. We found evidence for a shared genetic locus accounting for human stature on Chromosome 8, and suggestive evidence for loci on Chromosomes X, 7, and 20. Since twins from several countries contributed to the identified loci, an evolutionarily old genetic variant must influence stature in European-based populations. To facilitate the research in the field we have also set up a website that lists all stature genome scans published and their most significant loci (http://www.genomeutwin.org/stature_gene_map.htm).

The finger ridge count (a measure of pattern size) is one of the most heritable complex traits studied in humans and has been considered a model human polygenic trait in quantitative genetic analysis. Here, we report the results of the first genome-wide linkage scan for finger ridge count in a sample of 2,114 offspring from 922 nuclear families. Both univariate linkage to the absolute ridge count (a sum of all the ridge counts on all ten fingers), and multivariate linkage analyses of the counts on individual fingers, were conducted. The multivariate analyses yielded significant linkage to 5q14.1 (Logarithm of odds [LOD] = 3.34, pointwise-empirical p-value = 0.00025) that was predominantly driven by linkage to the ring, index, and middle fingers. The strongest univariate linkage was to 1q42.2 (LOD = 2.04, point-wise p-value = 0.002, genome-wide p-value = 0.29). In summary, the combination of univariate and multivariate results was more informative than simple univariate analyses alone. Patterns of quantitative trait loci factor loadings consistent with developmental fields were observed, and the simple pleiotropic model underlying the absolute ridge count was not sufficient to characterize the interrelationships between the ridge counts of individual fingers.

Author Summary

Finger ridge count (an index of the size of the fingerprint pattern) has been used as a model trait for the study of human quantitative genetics for over 80 years. Here, we present the first genome-wide linkage scan for finger ridge count in a large sample of 2,114 offspring from 922 nuclear families. Our results illustrate the increase in power and information that can be gained from a multivariate linkage analysis of ridge counts of individual fingers as compared to a univariate analysis of a summary measure (absolute ridge count). The strongest evidence for linkage was seen at 5q14.1, and the pattern of loadings was consistent with a developmental field factor whose influence is greatest on the ring finger, falling off to either side, which is consistent with previous findings that heritability for ridge count is higher for the middle three fingers. We feel that the paper will be of specific methodological interest to those conducting linkage and association analyses with summary measures. In addition, given the frequency with which this phenotype is used as a didactic example in genetics courses we feel that this paper will be of interest to the general scientific community.

This article combines social and genetic epidemiology to examine the influence of self-reported ethnicity on body mass index (BMI) among a sample of adolescents and young adults. We use genetic information from more than 5,000 single nucleotide polymorphisms in combination with principal components analysis to characterize population ancestry of individuals in this study. We show that non-Hispanic white and Mexican-American respondents differ significantly with respect to BMI and differ on the first principal component from the genetic data. This first component is positively associated with BMI and accounts for roughly 3% of the genetic variance in our sample. However, after controlling for this genetic measure, the observed ethnic differences in BMI remain large and statistically significant. This study demonstrates a parsimonious method to adjust for genetic differences among individual respondents that may contribute to observed differences in outcomes. In this case, adjusting for genetic background has no bearing on the influence of self-identified ethnicity.

Human growth has an estimated heritability of about 80%–90%. Nevertheless, the underlying cause of shortness of stature remains unknown in the majority of individuals. Genome-wide association studies (GWAS) showed that both common single nucleotide polymorphisms and copy number variants (CNVs) contribute to height variation under a polygenic model, although explaining only a small fraction of overall genetic variability in the general population. Under the hypothesis that severe forms of growth retardation might also be caused by major gene effects, we searched for rare CNVs in 200 families, 92 sporadic and 108 familial, with idiopathic short stature compared to 820 control individuals. Although similar in number, patients had overall significantly larger CNVs (p-value<1×10−7). In a gene-based analysis of all non-polymorphic CNVs>50 kb for gene function, tissue expression, and murine knock-out phenotypes, we identified 10 duplications and 10 deletions ranging in size from 109 kb to 14 Mb, of which 7 were de novo (p<0.03) and 13 inherited from the likewise affected parent but absent in controls. Patients with these likely disease causing 20 CNVs were smaller than the remaining group (p<0.01). Eleven (55%) of these CNVs either overlapped with known microaberration syndromes associated with short stature or contained GWAS loci for height. Haploinsufficiency (HI) score and further expression profiling suggested dosage sensitivity of major growth-related genes at these loci. Overall 10% of patients carried a disease-causing CNV indicating that, like in neurodevelopmental disorders, rare CNVs are a frequent cause of severe growth retardation.

Author Summary

With a frequency of 3%, shortness of stature is a common medical concern. Although family studies have clearly shown that gene defects play a pivotal role in the development of short stature, the underlying genetic variants involved remain unknown in about 80% of cases. In contrast to recent studies which aimed at the identification of common genetic variants to explain minor differences in the height variation in the general population, we targeted rare genomic variants where we expected a major gene effect on growth. By examining 200 patients clinically evaluated for short stature, we show that rare structural chromosomal aberrations (CNVs) are associated with shortness of stature in 10% of the cases. The identified CNVs were either de novo or segregated with short stature in the families and include genes that are functionally involved in growth regulation in humans or mice. We furthermore demonstrate an overlap of these CNVs with known microdeletion syndromes. Interestingly, 3 CNVs contain positions of common variants and confirm the localization of major growth-related genes. These findings are particularly important for identification of biological pathways leading to short stature, but also for further therapeutic approaches.

SUMMARY

We conducted a genome-wide scan in 46 pedigrees, with 671 phenotyped adults, from the independent nation of Samoa to map quantitative trait loci (QTLs) for adiposity-related phenotypes, including body mass index (BMI), abdominal circumference (ABDCIR), percent body fat (%BFAT), and fasting serum leptin and adiponectin. A set of 378 autosomal and 14 X chromosomal microsatellite markers were genotyped in 572 of the adults. Significant genetic correlations (0.82–0.96) were detected between pairs of BMI, ABDCIR, %BFAT and leptin. Suggestive linkages were found on 13q31 (LOD = 2.30 for leptin, LOD = 2.48 for %BFAT, LOD = 2.04 for ABDCIR, and LOD = 2.09 for BMI) and on 9p22 (LOD = 3.08 for ABDCIR and LOD = 2.53 for %BFAT). Furthermore, bivariate linkage analyses indicated that the genetic regions on 9p22 (bivariate LOD 2.35–3.10, LODeq (1df) 1.88–2.59) and 13q31 (bivariate LOD 1.96–2.64, LODeq 1.52–2.21) might harbor common major genes with pleiotropic effects. Other regions showing suggestive linkage included 4q22 (LOD = 2.95) and 7p14 (LOD = 2.64) for %BFAT, 2q13 for adiponectin (LOD = 2.05) and 19q12 for BMI-adjusted leptin (LOD = 2.03). Further fine mapping of these regions may help identify the genetic variants contributing to the development of obesity in Samoan adults.

Background

Age-related macular degeneration (AMD) is a complex disorder that is responsible for the majority of central vision loss in older adults living in developed countries. Phenotypic and genetic heterogeneity complicate the analysis of genome-wide scans for AMD susceptibility loci. The ordered subset analysis (OSA) method is an approach for reducing heterogeneity, increasing statistical power for detecting linkage, and helping to define the most informative data set for follow-up analysis. OSA assesses the linkage evidence in subsets of potentially more homogeneous families by rank-ordering family-specific lod scores with respect to trait-associated covariates or phenotypic features. Here, we present results of incorporating five continuous covariates into our genome-wide linkage analysis of 389 microsatellite markers in 62 multiplex families: Body mass index (BMI), systolic (SBP) and diastolic (DBP) blood pressure, intraocular pressure (IOP), and pack-years of cigarette smoking. Chromosome-wide significance of increases in nonparametric multipoint lod scores in covariate-defined subsets relative to the overall sample was assessed by permutation.

Results

Using a correction for testing multiple covariates, statistically significant lod score increases were observed for two chromosomal regions: 14q13 with a lod score of 3.2 in 28 families with average IOP ≤ 15.5 (p = 0.002), and 6q14 with a lod score of 1.6 in eight families with average BMI ≥ 30.1 (p = 0.0004). On chromosome 16p12, nominally significant lod score increases (p ≤ 0.05), up to a lod score of 2.9 in 32 families, were observed with several covariate orderings. While less significant, this was the only region where linkage evidence was associated with multiple clinically meaningful covariates and the only nominally significant finding when analysis was restricted to advanced forms of AMD. Families with linkage to 16p12 had higher averages of SBP, IOP and BMI and were primarily affected with neovascular AMD. For all three regions, linkage signals at or very near the peak marker have previously been reported.

Conclusion

Our results suggest that a susceptibility gene on chromosome 16p12 may predispose to AMD, particularly to the neovascular form, and that further research into the previously suggested association of neovascular AMD and systemic hypertension is warranted.

Background

Over the past two decades, the prevalence of overweight or obesity has increased in China. The aims of this study were to firstly assess the baseline prevelences and the risk factors for overweight and obesity, and secondly to detect the changes of body mass index (BMI) over a follow-up period in Chinese adults in Shanghai.

Methods

The data set of a population-based longitudinal study was analyzed. Anthropometric and biochemical data were collected for 5364 subjects (aged 25–95 years) during a period of 1998–2001. Among those individuals, 3032 subjects were interviewed and reexamined at the second survey from 2003 to 2004. Then the standardized prevalences for overweight and obesity were calculated using baseline data; the possible contributing factors of overweight and obesity were detected using binary logistic regression analysis; and the changes of BMI were evaluated after an average of 3.6-year follow-up period.

Results

(1) According to the WHO standard and the Chinese standard, the sex- and age-standardized prevalences were 27.5% and 32.4% for overweight, and 3.7% and 9.1% for obesity, respectively. (2) The risks of overweight and obesity differed among different age groups. Family history of obesity increased the risk of overweight and obesity by about 1.2-fold for both genders. Current male smokers had a lower risk of overweight and obesity (OR = 0.76, p < 0.05) than nonsmokers. In contrast, current male drinkers had a higher risk of overweight and obesity (OR = 1.42, p < 0.05) than nondrinkers. Compared with low-educated women, medium- and high- educated women were at lower risk of overweight and obesity, and the corresponding ORs (95% CIs) were 0.64 (0.52–0.79) and 0.50(0.36–0.68), respectively. (3) The annual changes of BMI means ranged from an increase of 0.1 kg/m2 to a decrease of 0.2 kg/m2 (by genders and age groups). Meanwhile, the BMI increase was statistically significant in the 35–44 years age group, and the BMI decrease was significant above 65 years for both genders.

Conclusion

This study showed high prevalence of overweight and obesity in Shanghai metropolis populations. The risk factors of overweight and obesity were multifactorial and gender specific. After 3.6 years, BMI means changed slightly, BMI increased mainly in middle-aged individuals and decreased in old individuals.

The prevalence of obesity (body mass index (BMI) ≥30 kg/m2) is higher in African Americans than in European Americans, even after adjustment for socioeconomic factors, suggesting that genetic factors may explain some of the difference. To identify genetic loci influencing BMI, we carried out a pooled analysis of genome-wide admixture mapping scans in 15,280 African Americans from 14 epidemiologic studies. Samples were genotyped at a median of 1,411 ancestry-informative markers. After adjusting for age, sex, and study, BMI was analyzed both as a dichotomized (top 20% versus bottom 20%) and a continuous trait. We found that a higher percentage of European ancestry was significantly correlated with lower BMI (ρ = −0.042, P = 1.6×10−7). In the dichotomized analysis, we detected two loci on chromosome X as associated with increased African ancestry: the first at Xq25 (locus-specific LOD = 5.94; genome-wide score = 3.22; case-control Z = −3.94); and the second at Xq13.1 (locus-specific LOD = 2.22; case-control Z = −4.62). Quantitative analysis identified a third locus at 5q13.3 where higher BMI was highly significantly associated with greater European ancestry (locus-specific LOD = 6.27; genome-wide score = 3.46). Further mapping studies with dense sets of markers will be necessary to identify the alleles in these regions of chromosomes X and 5 that may be associated with variation in BMI.

Author Summary

Obesity is about 1.5-fold more prevalent in African Americans than European Americans. To determine whether genetic background may contribute to this observed disparity, we scanned the genomes of African Americans, searching for genomic regions where obese individuals have a difference from the average proportion of African ancestry. By examining genetic data from more than 15,000 African Americans, we show that the proportion of European ancestry is inversely correlated with BMI. In obese individuals, we detect two loci with increased African ancestry on chromosome X (Xq13.1 and Xq25) and one locus with increased European ancestry on chromosome 5 (5q13.3). The 5q13.3 and Xq25 regions both contain genes that are known to be involved in appetite regulation. Our results suggest that genetic factors may contribute to the difference in obesity prevalence between African Americans and European Americans. Further studies of the regions may identify the causative variants affecting susceptibility to obesity.

Background

Idiopathic epilepsy in the Belgian shepherd dog is known to have a substantial genetic component. The objective of this study was to identify genomic regions associated with the expression of generalized seizures in the Belgian Tervuren and Sheepdog.

Results

DNA from 366 dogs, of which 74 were classified as epileptic, representing two extended families were subjected to a genome-wide linkage scan using 410 microsatellite markers yielding informative coverage averaging 5.95 ± 0.21 Mb. Though previous studies based on pedigree analyses proposed a major gene of influence, the present study demonstrated the trait to be highly polygenic. Studies of complex disorders in humans indicate that a liberal composite evaluation of genetic linkage is needed to identify underlying quantitative trait loci (QTLs). Four chromosomes yielded tentative linkage based upon LOD scores in excess of 1.0. Possible QTLs within these regions were supported also by analyses of multipoint linkage, allele frequency, TDT, and transmission of haplotype blocks.

Conclusions

Taken together the data tentatively indicate six QTLs, three on CFA 2, and one on each of CFA 6, 12, and 37, that support fine mapping for mutations associated with epilepsy in the Belgian shepherd. The study also underscores the complexity of genomic linkage studies for polygenic disorders.

Purpose

A previous genome-wide study in Orthodox Ashkenazi Jewish pedigrees showed significant linkage of ocular refraction to a Quantitative Trait Locus (QTL) on 1p34-36.1. We carried out a fine-mapping study of this region in Orthodox Ashkenazi Jewish (ASHK) and Old Order Amish (OOA) families to confirm linkage and narrow the candidate region.

Methods

Families were recruited from ASHK and OOA American communities. The samples included: 402 individuals in 53 OOA families; and 596 members in 68 ASHK families. Families were ascertained to contain multiple myopic individuals. Genotyping of 1,367 SNPs was carried out within a 35cM (~23.9 Mb) candidate QTL region on 1p34-36. Multipoint variance components (VC) and regression-based (REG) linkage analyses were carried out separately in OOA and ASHK groups, and in a combined analysis that included all families.

Results

Evidence of linkage of refractive error was found in both OOA (VC LOD=3.45, REG LOD=3.38 at ~59 cM) and ASHK families (VC LOD=3.12, REG LOD=4.263 at ~66 cM). Combined analyses showed three highly significant linkage peaks, separated by ~11cM (or 10 Mb), within the candidate region.

Conclusion

In a fine-mapping linkage study of OOA and ASHK families, we have confirmed linkage of refractive error to a QTL on 1p. The area of linkage has been narrowed down to a gene-rich region at 1p34.2-35.1 containing ~124 genes.

Background

Body fat mass distribution and deposition are determined by multiple environmental and genetic factors. Obesity is associated with insulin resistance, hyperinsulinemia, and type 2 diabetes. We previously identified evidence for genotype-by-diabetes interaction on obesity traits in Strong Heart Family Study (SHFS) participants. To localize these genetic effects, we conducted genome-wide linkage scans of obesity traits in individuals with and without type 2 diabetes, and in the combined sample while modeling interaction with diabetes using maximum likelihood methods (SOLAR 2.1.4).

Methods

SHFS recruited American Indians from Arizona, North and South Dakota, and Oklahoma. Anthropometric measures and diabetes status were obtained during a clinic visit. Marker allele frequencies were derived using maximum likelihood methods estimated from all individuals and multipoint identity by descent sharing was estimated using Loki. We used variance component linkage analysis to localize quantitative trait loci (QTLs) influencing obesity traits. We tested for evidence of additive and QTL-specific genotype-by-diabetes interactions using the regions identified in the diabetes-stratified analyses.

Results

Among 245 diabetic and 704 non-diabetic American Indian individuals, we detected significant additive gene-by-diabetes interaction for weight and BMI (P < 0.02). In analysis accounting for QTL-specific interaction (P < 0.001), we detected a QTL for weight on chromosome 1 at 242 cM (LOD = 3.7). This chromosome region harbors the adiponectin receptor 1 gene, which has been previously associated with obesity.

Conclusion

These results suggest distinct genetic effects on body mass in individuals with diabetes compared to those without diabetes, and a possible role for one or more genes on chromosome 1 in the pathogenesis of obesity.

Human quantitative trait locus (QTL) linkage mapping, although based on classical statistical genetic methods that have been around for many years, has been employed for genome-wide screening for only the last 10-15 years. In this time, there have been many success stories, ranging from QTLs that have been replicated in independent studies to those for which one or more genes underlying the linkage peak have been identified to a few with specific functional variants that have been confirmed in in vitro laboratory assays. Despite these successes, there is a general perception that linkage approaches do not work for complex traits, possibly because many human QTL linkage studies have been limited in sample size and have not employed the family configurations that maximize the power to detect linkage. We predict that human QTL linkage studies will continue to be productive for the next several years, particularly in combination with RNA expression level traits that are showing evidence of regulatory QTLs of large effect sizes and in combination with high-density genome-wide SNP panels. These SNP panels are being used to identify QTLs previously localized by linkage and linkage results are being used to place informative priors on genome-wide association studies.

Obesity is a heritable trait and a major risk factor for highly prevalent common diseases such as hypertension, cardiac diseases and type 2 diabetes. Obesity is a major public health concern worldwide. Previously we showed that BMI was positively correlated with African ancestry among the African American (AA) participants in the NHLBI’s Family Blood Pressure Program (FBPP). Using Individual Ancestry (IA) estimates at 284 marker locations across the genome, we now present a Quantitative Admixture Mapping (QAM) analysis of body mass index (BMI) in the same population. We used a set of unrelated individuals from Nigeria to represent the African ancestral population and the European Americans in the FBPP as the European ancestral population. The analysis was based on a common set of 284 microsatellite markers genotyped in all three groups. We considered the quantitative trait, BMI, as the response variable in a regression analysis with the marker location specific excess European ancestry as the explanatory variable. After suitably adjusting for different covariates such as sex, age and study center, we found strong evidence for a positive association with European ancestry at chromosome locations 3q29 and 5q14 and a negative association on chromosome 15q26. These results suggest that these regions may harbor genes influencing BMI in the AA population.

BACKGROUND AND OBJECTIVES:

No previous study has provided a detailed description of regional variations of growth within the various regions of Saudi Arabia. Thus, we sought to demonstrate differences in growth of children and adolescents in different regions.

SUBJECTS AND METHODS:

The 2005 Saudi reference was based on a cross-sectional representative sample of the Saudi population of healthy children and adolescents from birth to 18 years of age. Body measurements of the length, stature, weight, head circumference and calculation of the BMI were performed according to standard recommendations. Percentile construction and smoothing were performed using the LMS (lambda, mu and sigma) methodology, followed by transformation of all individual measurements into standard deviation scores. Factors such as weight for age, height for age, weight for height, and head circumference for children from birth to 3 years, stature for age, head circumference and body mass index for children between 2-18 years of age were assessed. Subsequently, variations in growth between the three main regions in the north, southwest, and center of Saudi Arabia were calculated, with the Bonferroni: method used to assess the significance of differences between regions.

RESULTS:

There were significant differences in growth between regions that varied according to age, gender, growth parameter and region. The highest variation was found between children and adolescents of the southwestern region and those of the other two regions The regression lines for all growth parameters in children <3 years of age were significantly different from one region to another reaching – 0.65 standard deviation scores for the southwestern regions (P=.001). However, the difference between the northern and central regions were not significant for the head circumference and for weight for length. For older children and adolescents a significant difference was found in all parameters except between the northern and central regions in BMI in girls and head circumference in boys. Finally, the difference in head circumference of girls between southwestern and northern regions was not significant. Such variation affected all growth parameters for both boys and girls.

CONCLUSION:

Regional variations in growth need to be taken into consideration when assessing the growth of Saudi children and adolescents.