Use of a random coefficient regression (RCR) model to estimate growth parameters

We used a random coefficient regression (RCR) model to estimate growth parameters for the time series of observed serum glucose levels in the Replicate 1 of the Genetic Analysis Workshop 13 simulated data. For comparison, a two time-point interval was also selected and the slope between these two observations was calculated. This process yielded four phenotypes: the RCR growth phenotype, a two time-point slope phenotype, and Time 1 and Time 2 serum glucose level phenotypes. These four phenotypes were used for linkage analyses on simulated chromosomes 5, 7, 9, and 21, those chromosomes that contained loci affecting the growth course for serum glucose levels. The linkage analysis of the RCR-derived phenotype showed overwhelming evidence for linkage at one locus (LOD 65.78 on chromosome 5), while showing elevated but nonsignificant LOD scores for two other loci (LOD 1.25 on chromosome 7, LOD 1.10 on chromosome 9), and no evidence of linkage for the final locus. The two time-point slope phenotype showed evidence for linkage at one locus (LOD 4.16 on chromosome 5) but no evidence for linkage at any of the other loci. A parallel cross-sectional approach, using as input phenotypes the endpoints of the two-point slope phenotype, gave strong linkage results for the major locus on chromosome 5 (maximal LOD scores of 17.90 and 27.24 for Time 1 and Time 2, respectively) while showing elevated but nonsignificant linkage results on chromosome 7 (maximal LOD scores of 1.71 and 1.48) and no evidence for linkage at the two remaining loci. The RCR growth parameter showed more power to detect linkage to the major locus than either the cross-sectional or two-point slope approach, but the cross-sectional approach gave a higher maximal LOD score for one of the minor loci.     

Genomic screen for substance dependence and body mass index in southwest California Indians

Substance abuse and obesity are health disparities that may afflict Native Americans more than some other ethnic groups. One theoretical assumption concerning Native people is that the long history of dependence on foraging and subsistence agriculture may have led to selective enrichment of traits that improve genetic fitness, so called 'thrifty' or 'fat sparing' genes. We have speculated that this same selective pressure may have enriched for genetic variants that increase the risk for consumption of alcohol and drugs of abuse. Here, we report the results of a genome scan that compared findings for two consumption phenotypes: 'any drug dependence and/or regular tobacco use' and body mass index (BMI) in southwest California (SWC) Indian families. Variance component analyses from SOLAR were used to generate log of the odds ratio (LOD) scores. Evidence for linkage was found on chromosome 6 for both the 'any drug' (LOD score = 3.3) and BMI (LOD score = 2.3) phenotypes. Bivariate analyses of the two phenotypes revealed a combined LOD score of 4.1 at that location. Additional loci on chromosomes 6, 15, 16 and 21 were found for the 'any drug' phenotype, and on chromosomes 8, 16 and 18 for BMI (LOD scores ranged between 1.2 and 2.3). These results provide suggestive evidence for linkage for substance abuse and BMI in this Mission Indian population and, furthermore, provide preliminary data suggesting that 'consumption phenotypes' may share some genetic determinants.     

Variance‐Component Analysis of Obesity in Type 2 Diabetes Confirms Loci on Chromosomes 1q and 11q

To study genetic loci influencing obesity in nuclear families with type 2 diabetes, we performed a genome‐wide screen with 325 microsatellite markers that had an average spacing of 11 cM and a mean heterozygosity of ～75% covering all 22 autosomes. Genotype data were obtained from 562 individuals from 178 families from the Breda Study Cohort. These families were determined to have at least two members with type 2 diabetes. As a measure of obesity, the BMI of each diabetes patient was determined. The genotypes were analyzed using variance components (VCs) analysis implemented in GENEHUNTER 2 to determine quantitative trait loci influencing BMI. The VC analysis revealed two genomic regions showing VC logarithm of odds (LOD) scores ≥1.0 on chromosome 1 and chromosome 11. The regions of interest on both chromosomes were further investigated by fine‐mapping with additional markers, resulting in a VC LOD score of 1.5 on chromosome 1q and a VC LOD of 2.4 on chromosome 11q. The locus on chromosome 1 has been implicated previously in diabetes. The locus on chromosome 11 has been implicated previously in diabetes and obesity. Our study to determine linkage for BMI confirms the presence of quantitative trait loci influencing obesity in subjects with type 2 diabetes on chromosomes 1q31‐q42 and 11q14‐q24.     

A whole-genome scan for obstructive sleep apnea and obesity

Obstructive sleep apnea (OSA) is a common, chronic, complex disease associated with serious cardiovascular and neuropsychological sequelae and with substantial social and economic costs. Along with male gender, obesity is the most characteristic feature of OSA in adults. To identify susceptibility loci for OSA, we undertook a 9-cM genome scan in 66 white pedigrees (n=349 subjects) ascertained on the basis of either an affected individual with laboratory-confirmed OSA or a proband who was a neighborhood control individual. Multipoint variance-component linkage analysis was performed for the OSA-associated quantitative phenotypes apnea-hypopnea index (AHI) and body mass index (BMI). Candidate regions on chromosomes 1p (LOD score 1.39), 2p (LOD score 1.64), 12p (LOD score 1.43), and 19p (LOD score 1.40) gave the most evidence for linkage to AHI. BMI was also linked to multiple regions, most significantly to markers on chromosomes 2p (LOD score 3.08), 7p (LOD score 2.53), and 12p (LOD score 3.41). Extended modeling indicated that the evidence for linkage to AHI was effectively removed after adjustment for BMI, with the exception of the candidate regions on chromosomes 2p (adjusted LOD score 1.33) and 19p (adjusted LOD score 1.45). After adjustment for AHI, the primary linkages to BMI remained suggestive but were roughly halved. Our results suggest that there are both shared and unshared genetic factors underlying susceptibility to OSA and obesity and that the interrelationship of OSA and obesity in white individuals may be partially explained by a common causal pathway involving one or more genes regulating both AHI and BMI levels.     

Quantitative trait linkage analysis of longitudinal change in body weight

One of the great strengths of the Framingham Heart Study data, provided for the Genetic Analysis Workshop 13, is the long-term survey of phenotypic data. We used this unique data to create new phenotypes representing the pattern of longitudinal change of the provided phenotypes, especially systolic blood pressure and body weight. We performed a linear regression of body weight and systolic blood pressure on age and took the slopes as new phenotypes for quantitative trait linkage analysis using the SOLAR package. There was no evidence for heritability of systolic blood pressure change. Heritability was estimated as 0.15 for adult life "body weight change", measured as the regression slope, and "body weight gain" (including only individuals with a positive regression slope), and as 0.22 for body weight "change up to 50" (regression slope of weight on age up to an age of 50). With multipoint analysis, two regions on the long arm of chromosome 8 showed the highest LOD scores of 1.6 at 152 cM for "body weight change" and of >1.9 around location 102 cM for "body weight gain" and "change up to 50". The latter two LOD scores almost reach the threshold for suggestive linkage. We conclude that the chromosome 8 region may harbor a gene acting on long-term body weight regulation, thereby contributing to the development of the metabolic syndrome.     

A Genome‐Wide Scan for Body Mass Index among Nigerian Families

Objective: Interest in mapping genetic variants that are associated with obesity remains high because of the increasing prevalence of obesity and its complications worldwide. Data on genetic determinants of obesity in African populations are rare. Research Methods and Procedures: We have undertaken a genome‐wide scan for body mass index (BMI) in 182 Nigerian families that included 769 individuals. Results: The prevalence of obesity was only 5%, yet polygenic heritability for BMI was in the expected range (0.46 ± 0.07). Tandem repeat markers (402) were typed across the genome with an average map density of 9 cM. Pedigree‐based analysis using a variance components linkage model demonstrated evidence for linkage on chromosome 7 (near marker D7S817 at 7p14) with a logarithm of odds (LOD) score of 3.8 and on chromosome 11 (marker D11S2000 at 11q22) with an LOD score of 3.3. Weaker evidence for linkage was found on chromosomes 1 (1q21, LOD = 2.2) and 8 (8p22, LOD = 2.3). Several candidate genes, including neuropeptide Y, DRD2, APOA4, lamin A/C, and lipoprotein lipase, lie in or close to the chromosomal regions where strong linkage signals were found. Discussion: The findings of this study suggest that, as in other populations with higher prevalences of obesity, positive linkage signals can be found on genome scans for obesity‐related traits. Follow‐up studies may be warranted to investigate these linkages, especially the one on chromosome 11, which has been reported in a population at the opposite end of the BMI distribution.     

Linkage of body mass index to chromosome 20 in Utah pedigrees

Several linkage studies have hinted at the existence of an obesity predisposition locus on chromosome 20, but none of these studies has produced conclusive results. Therefore, we analyzed 48 genetic markers on chromosome 20 for linkage to severe obesity (BMI> or =35) in 103 extended Utah pedigrees (1,711 individuals), all of which had strong aggregation of severe obesity. A simple dominant model produced a maximum multipoint heterogeneity LOD score of 3.5 at D20S438 (55.1 cM). Two additional analyses were performed. First, a one-gene, two-mutation model (with one dominant mutation and one recessive mutation) increased the LOD score to 4.2. Second, a two-locus model (with one locus dominant and one recessive) generated a multipoint LOD score of 4.9. We conclude that one or more severe obesity predisposing genes lie within an interval of approx. 10 cM on chromosome 20. This study generated significant LOD scores which confirm suggestive linkage reports from previous studies. In addition, our analyses suggest that the predisposing gene(s) is localized very near the chromosome 20 centromere.     

Interacting genetic loci on chromosomes 20 and 10 influence extreme human obesity

Obesity is a multigenic trait that has a substantial genetic component. Animal models confirm a role for gene-gene interactions, and human studies suggest that as much as one-third of the heritable variance may be due to nonadditive gene effects. To evaluate potential epistatic interactions among five regions, on chromosomes 7, 10, and 20, that have previously been linked to obesity phenotypes, we conducted pairwise correlation analyses based on alleles shared identical by descent (IBD) for independent obese affected sibling pairs (ASPs), and we determined family-specific nonparametric linkage (NPL) scores in 244 families. The correlation analyses were also conducted separately, by race, through use of race-specific allele frequencies. Conditional analyses for a qualitative trait (body mass index [BMI] >/=27) and hierarchical models for quantitative traits were used to further refine evidence of gene interaction. Both the ASP-specific IBD-sharing probability and the family-specific NPL score revealed that there were strong positive correlations between 10q (88-97 cM) and 20q (65-83 cM), through single-point and multipoint analyses with three obesity thresholds (BMI >/=27, >/=30, and >/=35) across African American and European American samples. Conditional analyses for BMI >/=27 found that the LOD score at 20q rises from 1.53 in the baseline analysis to 2.80 (empirical P=.012) when families were weighted by evidence for linkage at 10q (D10S1646) through use of zero-one weights (weight(0-1)) and to 3.32 (empirical P<.001) when proportional weights (weight(prop)) were used. For percentage fat mass, variance-component analysis based on a two-locus epistatic model yielded significant evidence for interaction between 20q (75 cM) and the chromosome 10 centromere (LOD = 1.74; P=.024), compared with a two-locus additive model (LOD = 0.90). The results from multiple methods and correlated phenotypes are consistent in suggesting that epistatic interactions between loci in these regions play a role in extreme human obesity.     

A novel obesity locus on chromosome 4q: the Strong Heart Family Study

Objective: Obesity is a growing and important public health problem in Western countries and worldwide. There is ample evidence that both environmental and genetic factors influence the risk of developing obesity. Although a number of genes influencing obesity and obesity‐related measures have been localized, it is clear that others remain to be identified. The rate of obesity is particularly high in American Indian populations. This study reports the results of a genome‐wide scan for loci influencing BMI and weight in 963 individuals in 58 families from three American Indian populations in Arizona, Oklahoma, and North and South Dakota participating in the Strong Heart Family Study. Research Methods and Procedures: Short tandem repeat markers were genotyped, resulting in a marker map with an average spacing of 10 centimorgans. Standard multipoint variance component linkage methods were used. Results: Significant evidence of linkage was observed in the overall sample, including all three study sites, for a locus on chromosome 4q35 [logarithm of the odds (LOD) = 5.17 for weight, 5.08 for BMI]. Analyses of the three study sites individually showed that the greatest linkage support for the chromosome 4 locus came from Arizona (LOD = 2.6 for BMI), but that LOD scores for weight were >1 in all three samples. Suggestive linkage signals (LOD >2) were also observed on chromosomes 5, 7, 8, and 10. Discussion: The chromosome 4 locus detected in this scan is in a region lacking any obvious positional candidate genes with known functions related to obesity. This locus may represent a novel obesity gene.     

Evidence of a novel quantitative-trait locus for obesity on chromosome 4p in Mexican Americans

Although several genomewide scans have identified quantitative-trait loci influencing several obesity-related traits in humans, genes influencing normal variation in obesity phenotypes have not yet been identified. We therefore performed a genome scan of body mass index (BMI) on Mexican Americans, a population prone to obesity and diabetes, using a variance-components linkage analysis to identify loci that influence BMI. We used phenotypic data from 430 individuals (26% diabetics, 59% females, mean age +/- SD = 43 +/- 17 years, mean BMI +/- SD = 30.0 +/- 6.7, mean leptin (ng/ml) +/- SD = 22.1 +/- 17.1) distributed across 27 low-income Mexican American pedigrees who participated in the San Antonio Family Diabetes Study (SAFDS) for whom a 10-15-cM map is available. In this genomewide search, after accounting for the covariate effects of age, sex, diabetes, and leptin, we identified a genetic region exhibiting the most highly significant evidence for linkage (LOD 4.5) with BMI on chromosome 4p (4p15.1) at 42 cM, near marker D4S2912. This linkage result has been confirmed in an independent linkage study of severe obesity in Utah pedigrees. Two strong positional candidates, the human peroxisome proliferator-activated receptor gamma coactivator 1 (PPARGC1) and cholecystokinin A receptor (CCKAR) with major roles in the development of obesity, are located in this region. In conclusion, we identified a major genetic locus influencing BMI on chromosome 4p in Mexican Americans.     

A genome-wide scan for loci affecting normal adult height in the Framingham Heart Study

OBJECTIVE: To map loci influencing normal adult height in 335 families from the Framingham Heart Study. METHODS: We analyzed data consisting of 1,702 genotyped individuals who have been followed over time. The first height measurement for individuals between the ages 20-55 years was analyzed in a genome-wide scan using variance component linkage analysis. Sex, age, and cohort effects were removed before analysis. RESULTS: Two regions (18pter-p11, 22q11.2) with multipoint LOD scores >1.0 (-log p values >2.0) were detected: we obtained LOD scores of 1.38 at D18S1364, and of 1.10 at D22S345. Analysis of height as a sex-limited phenotype revealed a peak in the 9p21 region near D9S319 with a maximum LOD score of 1.65 (-log p value >3.0) when only male height phenotypes were used. When only female phenotypes were used, a peak with a maximum LOD score of 1.85 (-log p value of 2.70) was observed in the 11q25-qter region near D11S2359. CONCLUSIONS: Our region of interest on chromosome 9 has been implicated by two prior studies. Variance components analysis appeared to be sensitive to pedigree structures as well as the method of IBD computation used.     

Effect of genotype x alcoholism interaction on linkage analysis of an alcoholism-related quantitative phenotype

Studies have shown that genetic and environmental factors and their interactions affect several alcoholism phenotypes. Genotype x alcoholism (GxA) interaction refers to the environmental (alcoholic and non-alcoholic) influences on the autosomal genes contributing to variation in an alcoholism-related quantitative phenotype. The purpose of this study was to examine the effects of GxA interaction on the detection of linkage for alcoholism-related phenotypes. We used phenotypic and genotypic data from the Collaborative Study on the Genetics of Alcoholism relating to 1,388 subjects as part of Genetic Analysis Workshop 14 problem 1. We analyzed the MXDRNK phenotype to detect GxA interaction using SOLAR. Upon detecting significant interaction, we conducted variance-component linkage analyses using microsatellite marker data. For maximum number of drinks per a 24 hour period, the highest LODs were observed on chromosomes 1, 4, and 13 without GxA interaction. Interaction analysis yielded four regions on chromosomes 1, 4, 13, and 15. On chromosome 4, a maximum LOD of 1.5 at the same location as the initial analysis was obtained after incorporating GxA interaction effects. However, after correcting for extra parameters, the LOD score was reduced to a corrected LOD of 1.1, which is similar to the LOD observed in the non-interaction analysis. Thus, we see little differences in LOD scores, while some linkage regions showed large differences in the magnitudes of estimated quantitative trait loci heritabilities between the alcoholic and non-alcoholic groups. These potential hints of differences in genetic effect may influence future analyses of variants under these linkage peaks.     

Genome-wide linkage analysis using cross-sectional and longitudinal traits for body mass index in a subsample of the Framingham Heart Study

To evaluate linkage evidence for body mass index (BMI) using both cross-sectional and longitudinal data, we performed genome-wide multipoint linkage analyses on subjects who had complete data at four selected time points (initial, 8th, 12th, and 16th year following the initial visit) from the Framingham Heart Study. The cross-sectional measures included BMI at each of the four selected time points and the longitudinal measure was the within-subject mean of BMI at the above four time points. Using the variance components method, we consistently observed the maximum LOD score out of the genome scan using BMI at each time point and the mean of BMI between 049xd2 and GATA71H05 on chromosome 16. The highest LOD score (3.0) was at time point 1, while the lowest (1.9) was at time point 4. We also observed other suggestive linkages on chromosome 6, 10, and 18 at time point 1 only. The longitudinal measure we studied (mean of BMI) did not provide greater power to identify a positive linkage than some of the cross-sectional measures (e.g., time point 1). The changing of linkage evidence over time provided some insights on the variation of genetic effect on BMI with aging. There may be a QTL on chromosome 16 that contributes to BMI and this locus, and maybe others, is more likely to affect BMI during early adulthood.     

Comparison of the linkage results of two phenotypic constructs from longitudinal data in the Framingham Heart Study: analyses on data measured at three time points and on the average of three measurements

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.

     

Linkage analysis of extremely discordant and concordant sibling pairs identifies quantitative trait loci influencing variation in human menopausal age

Age at natural menopause may be used as parameter for evaluating the rate of ovarian aging. Environmental factors determine only a small part of the large variation in menopausal age. Studies have shown that genetic factors are likely to be involved in variation in menopausal age. To identify quantitative-trait loci for this trait, we performed a genomewide linkage study with age at natural menopause as a continuous quantitative phenotype in Dutch sister pairs, through use of a selective sampling scheme. A total of 165 families were ascertained using extreme selected sampling and were genotyped for 417 markers. Data were analyzed by Haseman-Elston regression and by an adjusted variance-components analysis. Subgroup analyses for early and late menopausal age were conducted by Haseman-Elston regression. In the adjusted variance-components analysis, 12 chromosomes had a LOD score of > or =1.0. Two chromosomal regions showed suggestive linkage: 9q21.3 (LOD score 2.6) and Xp21.3 (LOD score 3.1). Haseman-Elston regression showed rather similar locations of the peaks but yielded lower LOD scores. A permutation test to obtain empirical P values resulted in a significant peak on the X chromosome. To our knowledge, this is the first study to attempt to identify loci responsible for variability in menopausal age and in which several chromosomal regions were identified with suggestive and significant linkage. Although the finding of the region on the X chromosome comes as no surprise, because of its widespread involvement in premature ovarian failure, the definition of which particular gene is involved is of great interest. The region on chromosome 9 deserves further consideration. Both findings require independent confirmation.     

Bivariate linkage analysis of the insulin resistance syndrome phenotypes on chromosome 7q

Metabolic abnormalities of the insulin resistance syndrome (IRS) have been shown to aggregate in families and to exhibit trait-pair correlations, suggesting a common genetic component. A broad region on chromosome 7q has been implicated in several studies to contain loci that cosegregate with IRS-related traits. However, it is not clear whether such loci have any common genetic (pleiotropic) influences on the correlated traits. Also, it is not clear whether the chromosomal regions contain more than one locus influencing the IRS-related phenotypes. In this study we present evidence for linkage of five IRS-related traits [body mass index (BMI), waist circumference (WC), In split proinsulin (LSPI), In triglycerides (LTG), and high-density lipoprotein cholesterol (HDLC)] to a region at 7q11.23. Subsequently, to gain further insight into the genetic component(s) mapping to this region, we explored whether linkage of these traits is due to pleiotropic effects using a bivariate linkage analytical technique, which has been shown to localize susceptibility regions with precision. Four hundred forty individuals from 27 Mexican American families living in Texas were genotyped for 19 highly polymorphic markers on chromosome 7. Multipoint variance component linkage analysis was used to identify genetic location(s) influencing IRS-related traits of obesity (BMI and WC), dyslipidemia (LTG and HDLC), and insulin levels (LSPI); the analysis identified a broad chromosomal region spanning approximately 24 cM. To gain more precision in localization, we used a bivariate linkage approach for each trait pair. These analyses suggest localization of most of these bivariate traits to an approximately 6-cM region near marker D7S653 [7q11.23, 103-109 cM; a maximum bivariate LOD of 4.51 was found for the trait pair HDLC and LSPI (the LODeq score is 3.94)]. We observed evidence of pleiotropic effects in this region on obesity and insulin-related trait pairs.     

Genomewide linkage analysis for internal carotid artery intimal medial thickness: evidence for linkage to chromosome 12

Carotid intimal medial thickness (IMT) is a heritable quantitative measure of atherosclerosis. A genomewide linkage analysis was conducted to localize a quantitative-trait locus (QTL) influencing carotid IMT. Carotid IMT was measured in 596 men and 629 women from 311 extended families (1,242 sib pairs) in the Framingham Heart Study Offspring cohort. B-mode carotid ultrasonography was used to define mean IMT of the carotid artery segments. Multipoint variance-component linkage analysis was performed. Evidence for significant linkage to internal carotid artery (ICA) IMT (two-point log odds [LOD] score 4.1, multipoint LOD score 3.4) was found 161 cM from the tip of the short arm of chromosome 12; these results were confirmed using the GENEHUNTER package (multipoint LOD score 4.3). No LOD scores >2.0 were observed for common carotid artery (CCA) IMT. Association analysis of a single-nucleotide-polymorphism variant of SCARB1 (minor allele frequency 0.13), a gene in close proximity to the region of peak linkage, revealed a protective association of the missense variant allele in exon 1 of SCARB1, with decreased ICA IMT compared with subjects homozygous for the common allele. Although the exon 1 variant contributed 2% to overall variation in ICA IMT, there was no significant change in the peak LOD score after adjustment in the linkage analyses. These data provide substantial evidence for a QTL on chromosome 12 influencing ICA IMT and for association of a rare variant of SCARB1, or a nearby locus, with ICA IMT. Because this rare SCARB1 variant does not account for our observed linkage, further investigations are warranted to identify additional candidate-gene variants on chromosome 12 predisposing to atherosclerosis phenotypes and clinical vascular disease.     

Genomewide linkage analysis of quantitative spirometric phenotypes in severe early-onset chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a common, complex disease associated with substantial morbidity and mortality. COPD is defined by irreversible airflow obstruction; airflow obstruction is typically determined by reductions in quantitative spirometric indices, including forced expiratory volume at 1 s (FEV(1)) and the ratio of FEV(1) to forced vital capacity (FVC). To identify genetic determinants of quantitative spirometric phenotypes, an autosomal 10-cM genomewide scan of short tandem repeat (STR) polymorphic markers was performed in 72 pedigrees (585 individuals) ascertained through probands with severe early-onset COPD. Multipoint variance-component linkage analysis (using SOLAR) was performed for quantitative phenotypes, including FEV(1), FVC, and FEV(1)/FVC. In the initial genomewide scan, significant evidence for linkage to FEV(1)/FVC was demonstrated on chromosome 2q (LOD score 4.12 at 222 cM). Suggestive evidence was found for linkage to FEV(1)/FVC on chromosomes 1 (LOD score 1.92 at 120 cM) and 17 (LOD score 2.03 at 67 cM) and to FVC on chromosome 1 (LOD score 2.05 at 13 cM). The highest LOD score for FEV(1) in the initial genomewide scan was 1.53, on chromosome 12, at 36 cM. After inclusion of 12 additional STR markers on chromosome 12p, which had been previously genotyped in this population, suggestive evidence for linkage of FEV(1) (LOD score 2.43 at 37 cM) to this region was demonstrated. These observations provide both significant evidence for an early-onset COPD-susceptibility locus on chromosome 2 and suggestive evidence for linkage of spirometry-related phenotypes to several other genomic regions. The significant linkage of FEV(1)/FVC to chromosome 2q could reflect one or more genes influencing the development of airflow obstruction or dysanapsis.     

Genetic determinants of obesity-related lipid traits

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.     

Meta-analysis of genome-wide linkage studies in BMI and obesity

Objective: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI‐defined obesity using a nonparametric genome scan meta‐analysis. Research Methods and Procedures: We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome‐wide logarithm of the odds (LOD) scores, non‐parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI‐defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry. Results: Bins at chromosome 13q13.2‐ q33.1, 12q23‐q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3‐22.3 were also observed for BMI‐defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values <0.05 is not different from that expected by chance. The strongest evidence was obtained in the families ascertained for hypertension at 9q31.1‐qter and 12p11.21‐q23 (p < 0.01). Conclusion: Despite having substantial statistical power, we did not unequivocally implicate specific loci for BMI or obesity. This may be because genes influencing adiposity are of very small effect, with substantial genetic heterogeneity and variable dependence on environmental factors. However, the observation that the FTO gene maps to one of the highest ranking bins for obesity is interesting and, while not a validation of this approach, indicates that other potential loci identified in this study should be investigated further.