Related Articles

Background

We investigate the power of heterogeneity LOD test to detect linkage when a trait is determined by several major genes using Genetic Analysis Workshop 13 simulated data. We consider three traits, two of which are disease-causing traits: 1) the rate of change in body mass index (BMI); and 2) the maximum BMI; and 3) the disease itself (hypertension). Of interest is the power of "HLOD2", the maximum heterogeneity LOD obtained upon maximizing over the two genetic models.

Results

Using a trait phenotype Obesity Slope, we observe that the power to detect the two markers closest to the two genes (S1, S2) at the 0.05 level using HLOD2 is 13% and 10%. The power of HLOD2 for Max BMI phenotype is 12% and 9%. The corresponding values for the Hypertension phenotype are 8% and 6%.

Conclusion

The power to detect linkage to the slope genes is quite low. But the power using disease-related traits as a phenotype is greater than the power using the disease (hypertension) phenotype.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome, including emphysema and airway disease. Phenotypes defined on the basis of chest computed tomography (CT) may decrease disease heterogeneity and aid in the identification of candidate genes for COPD subtypes. To identify these genes, we performed genome-wide linkage analysis in extended pedigrees from the Boston Early-Onset COPD Study, stratified by emphysema status (defined by chest CT scans) of the probands, followed by genetic association analysis of positional candidate genes. A region on chromosome 1p showed strong evidence of linkage to lung function traits in families of emphysema-predominant probands in the stratified analysis (LOD score = 2.99 in families of emphysema-predominant probands versus 1.98 in all families). Association analysis in 949 individuals from 127 early-onset COPD pedigrees revealed association for COPD-related traits with an intronic single-nucleotide polymorphism (SNP) in transforming growth factor-β receptor-3 (TGFBR3) (P = 0.005). This SNP was significantly associated with COPD affection status comparing 389 cases from the National Emphysema Treatment Trial to 472 control smokers (P = 0.04), and with FEV1 (P = 0.004) and CT emphysema (P = 0.05) in 3,117 subjects from the International COPD Genetics Network. Gene-level replication of association with lung function was seen in 427 patients with COPD from the Lung Health Study. In conclusion, stratified linkage analysis followed by association testing identified TGFBR3 (betaglycan) as a potential susceptibility gene for COPD. Published human microarray and murine linkage studies have also demonstrated the importance of TGFBR3 in emphysema and lung function, and our group and others have previously found association of COPD-related traits with TGFB1, a ligand for TGFBR3.

Background

Epidemiological studies have indicated that obesity and low high-density lipoprotein (HDL) levels are strong cardiovascular risk factors, and that these traits are inversely correlated. Despite the belief that these traits are correlated in part due to pleiotropy, knowledge on specific genes commonly affecting obesity and dyslipidemia is very limited. To address this issue, we first conducted univariate multipoint linkage analysis for body mass index (BMI) and HDL-C to identify loci influencing variation in these phenotypes using Framingham Heart Study data relating to 1702 subjects distributed across 330 pedigrees. Subsequently, we performed bivariate multipoint linkage analysis to detect common loci influencing covariation between these two traits.

Results

We scanned the genome and identified a major locus near marker D6S1009 influencing variation in BMI (LOD = 3.9) using the program SOLAR. We also identified a major locus for HDL-C near marker D2S1334 on chromosome 2 (LOD = 3.5) and another region near marker D6S1009 on chromosome 6 with suggestive evidence for linkage (LOD = 2.7). Since these two phenotypes have been independently mapped to the same region on chromosome 6q, we used the bivariate multipoint linkage approach using SOLAR. The bivariate linkage analysis of BMI and HDL-C implicated the genetic region near marker D6S1009 as harboring a major gene commonly influencing these phenotypes (bivariate LOD = 6.2; LODeq = 5.5) and appears to improve power to map the correlated traits to a region, precisely.

Conclusions

We found substantial evidence for a quantitative trait locus with pleiotropic effects, which appears to influence both BMI and HDL-C phenotypes in the Framingham data.

Summary

Preliminary studies suggested that age at onset (AAO) may help to define homogeneous bipolar affective disorder (BPAD) subtypes. This candidate symptom approach might be useful to identify vulnerability genes. Thus, the probability of detecting major disease-causing genes might be increased by focusing on families with early-onset BPAD type I probands. This study was conducted as part of the European Collaborative Study of Early Onset BPAD (France, Germany, Ireland, Scotland, Switzerland, England, Slovenia). We performed a genome-wide search with 384 microsatellite markers using non parametric linkage analysis in 87 sib-pairs ascertained through an early-onset BPAD type I proband (age at onset of 21 years or below). Non parametric multi-point analysis suggested eight regions of linkage with p-values <0.01 (2p21, 2q14.3, 3p14, 5q33, 7q36, 10q23, 16q23 and 20p12). The 3p14 region showed the most significant linkage (genome-wide p-value estimated over 10.000 simulated replicates of 0.015 [0.01–0.02]). After genome-wide search analysis, we performed additional linkage analyses with increase marker density using markers in four regions suggestive for linkage and having an information contents lower than 75% (3p14, 10q23, 16q23 and 20p12). For these regions, the information content improved by about 10%. In chromosome 3, the non parametric linkage score increased from 3.51 to 3.83. This study is the first to use early onset bipolar type I probands in an attempt to increase sample homogeneity. These preliminary findings require confirmation in independent panels of families.

Background

Age at onset of Huntington's disease (HD) is correlated with the size of the abnormal CAG repeat expansion in the HD gene; however, several studies have indicated that other genetic factors also contribute to the variability in HD age at onset. To identify modifier genes, we recently reported a whole-genome scan in a sample of 629 affected sibling pairs from 295 pedigrees, in which six genomic regions provided suggestive evidence for quantitative trait loci (QTL), modifying age at onset in HD.

Methods

In order to test the replication of this finding, eighteen microsatellite markers, three from each of the six genomic regions, were genotyped in 102 newly recruited sibling pairs from 69 pedigrees, and data were analyzed, using a multipoint linkage variance component method, in the follow-up sample and the combined sample of 352 pedigrees with 753 sibling pairs.

Results

Suggestive evidence for linkage at 6q23-24 in the follow-up sample (LOD = 1.87, p = 0.002) increased to genome-wide significance for linkage in the combined sample (LOD = 4.05, p = 0.00001), while suggestive evidence for linkage was observed at 18q22, in both the follow-up sample (LOD = 0.79, p = 0.03) and the combined sample (LOD = 1.78, p = 0.002). Epistatic analysis indicated that there is no interaction between 6q23-24 and other loci.

Conclusion

In this replication study, linkage for modifier of age at onset in HD was confirmed at 6q23-24. Evidence for linkage was also found at 18q22. The demonstration of statistically significant linkage to a potential modifier locus opens the path to location cloning of a gene capable of altering HD pathogenesis, which could provide a validated target for therapeutic development in the human patient.

We performed a bivariate analysis on cholesterol and triglyceride levels on data from the Framingham Heart Study using a new score statistic developed for the detection of potential pleiotropic, or cluster, genes. Univariate score statistics were also computed for each trait. At a significance level 0.001, linkage signals were found at markers GATA48B01 on chromosome 1, GATA21C12 on chromosome 8, and ATA55A11 on chromosome 16 using the bivariate analysis. At the same significance level, linkage signals were found at markers 036yb8 on chromosome 3 and GATA3F02 on chromosome 12 using the univariate analysis. A strong linkage signal was also found at marker GATA112F07 by both the bivariate analysis and the univariate analysis, a marker for which evidence for linkage had been reported previously in a related study.

Gene expression, as a heritable complex trait, has recently been used in many genome-wide linkage studies. The estimated overall heritability of each trait may be considered as evidence of a genetic contribution to the total phenotypic variation, which implies the possibility of mapping genome regions responsible for the gene expression variation via linkage analysis. However, heritability has been found to be an inconsistent predictor of significant linkage signals. To investigate this issue in human studies, we performed genome-wide linkage analysis on the 3554 gene expression traits of 194 Centre d'Etude du Polymorphisme Humain individuals provided by Genetic Analysis Workshop 15. Out of the 422 expression traits with significant linkage signals identified (LOD > 5.3), 89 traits have low estimated heritability (h2 < 10%), among which 23 traits have an estimated heritability equal to 0. The linkage analysis on individual pedigree shows that the overall LOD scores may result from a few pedigrees with strong linkage signals. Screening gene expressions before linkage analysis using a relatively low heritability (h2 < 20%) may result in a loss of significant linkage signals, especially for trans-acting expression quantitative trait loci (49%).

Background

There is substantial evidence for a significant genetic component to the risk for alcoholism. However, susceptibility loci or genes for alcohol dependence remain largely unknown. To identify susceptibility loci for alcohol dependence, we selected 329 extended families from the Framingham Heart Study population in which at least one family member reported alcohol consumption during the interview in 1970–1971, and performed genome-wide linkage analyses using various analytical methods.

Results

Multi-point sib-pair regression analysis using the SIBPAL program of S.A.G.E. provided strong evidence for linkage of alcohol dependence to chromosomes 9 (p-value < 0.0001) and weak evidence to chromosomes 15 and 16 (p-value < 0.005). To confirm these findings, we re-analyzed the same data set by various methods implemented in GENEHUNTER and found that only one region was significant with a LOD score > 2.0 by the variance-component method. This region is located on chromosome 9 between markers GATA21F05 and GATA81C04.

Conclusion

Analyses of the Framingham Heart Study population provided evidence of genetic susceptibility loci for alcohol dependence on chromosomes 9, 15, and 16. The genomic region identified on chromosome 9 was particularly interesting because the region has also been previously reported to be linked to alcohol dependence in the American Indian population by another group.

To study the genetic basis of natural variation in gene expression, we previously carried out genome-wide linkage analysis and mapped the determinants of ~1,000 expression phenotypes1. In the present study, we carried out association analysis with dense sets of single-nucleotide polymorphism (SNP) markers from the International HapMap Project2. For 374 phenotypes, the association study was performed with markers only from regions with strong linkage evidence; these regions all mapped close to the expressed gene. For a subset of 27 phenotypes, analysis of genome-wide association was performed with >770,000 markers. The association analysis with markers under the linkage peaks confirmed the linkage results and narrowed the candidate regulatory regions for many phenotypes with strong linkage evidence. The genome-wide association analysis yielded highly significant results that point to the same locations as the genome scans for about 50% of the phenotypes. For one candidate determinant, we carried out functional analyses and confirmed the variation in cis-acting regulatory activity. Our findings suggest that association studies with dense SNP maps will identify susceptibility loci or other determinants for some complex traits or diseases.

Complex disease mapping usually involves a combination of linkage and association techniques. Linkage analysis can scan the entire genome in a few hundred tests. Association tests may involve an even greater number of tests. However, association tests can localize the susceptibility genes more accurately. Using a recently developed combined linkage and association strategy, we analyzed a subset of the Collaborative Study on the Genetics of Alcoholism (COGA) data for the Genetic Analysis Workshop 14 (GAW14). In this analysis, we first employed linkage analysis based on frailty models that take into account age of onset information to establish which regions along the chromosome are likely to harbor disease susceptibility genes for alcohol dependence. Second, we used an association analysis by exploiting linkage disequilibrium to narrow down the peak regions. We also compare the methods with mean identity-by-descent tests and transmission/disequilibrium tests that do not use age of onset information.

Rheumatoid arthritis is the most common systematic autoimmune disease and its etiology is believed to have both strong genetic and environmental components. We demonstrate the utility of including genetic and clinical phenotypes as covariates within a linkage analysis framework to search for rheumatoid arthritis susceptibility loci. The raw genotypes of 1302 affected relative pairs were combined from four large family-based samples (North American Rheumatoid Arthritis Consortium, United Kingdom, European Consortium on Rheumatoid Arthritis Families, and Canada). The familiality of the clinical phenotypes was assessed. The affected relative pairs were subjected to autosomal multipoint affected relative-pair linkage analysis. Covariates were included in the linkage analysis to take account of heterogeneity within the sample. Evidence of familiality was observed with age at onset (p << 0.001) and rheumatoid factor (RF) IgM (p << 0.001), but not definite erosions (p = 0.21). Genome-wide significant evidence for linkage was observed on chromosome 6. Genome-wide suggestive evidence for linkage was observed on chromosomes 13 and 20 when conditioning on age at onset, chromosome 15 conditional on gender, and chromosome 19 conditional on RF IgM after allowing for multiple testing of covariates.

Background

Body mass index (BMI) and adult height are moderately and highly heritable traits, respectively. To investigate the genetic background of these quantitative phenotypes, we performed a linkage genome scan in the extended pedigrees of the Framingham Heart Study. Two variance-components approaches (SOLAR and MERLIN-VC) and one regression method (MERLIN-REGRESS) were applied to the data.

Results

Evidence for linkage to BMI was found on chromosomes 16 and 6 with maximum LOD scores of 3.2 and 2.7, respectively. For height, all markers showing a LOD score greater than 1 in our analysis correspond to previously reported linkage regions, including chromosome 6q with a maximum LOD score of 2.45 and chromosomes 9, 12, 14, 18, and 22. Regarding the analysis, the three applied methods gave very similar results in this unselected sample with approximately normally distributed traits.

Conclusion

Our analysis resulted in the successful identification of linked regions. In particular, we consider the regions on chromosomes 6 and 16 for BMI and the regions on chromosomes 6, 9, and 12 for stature interesting for fine mapping and candidate gene studies.

Purpose

To investigate quantitative trait loci linked to refractive error, we performed a genome-wide quantitative trait linkage analysis using single nucleotide polymorphism markers and family data from five international sites.

Methods

Genomic DNA samples from 254 families were genotyped by the Center for Inherited Disease Research using the Illumina Linkage Panel IVb. Quantitative trait linkage analysis was performed on 225 Caucasian families and 4,656 markers after accounting for linkage disequilibrium and quality control exclusions. Two refractive quantitative phenotypes, sphere (SPH) and spherical equivalent (SE), were analyzed. The SOLAR program was used to estimate identity by descent probabilities and to conduct two-point and multipoint quantitative trait linkage analyses.

Results

We found 29 markers and 11 linkage regions reaching peak two-point and multipoint logarithms of the odds (LODs)>1.5. Four linkage regions revealed at least one LOD score greater than 2: chromosome 6q13–6q16.1 (LOD=1.96 for SPH, 2.18 for SE), chromosome 5q35.1–35.2 (LOD=2.05 for SPH, 1.80 for SE), chromosome 7q11.23–7q21.2 (LOD=1.19 for SPH, 2.03 for SE), and chromosome 3q29 (LOD=1.07 for SPH, 2.05 for SE). Among these, the chromosome 6 and chromosome 5 regions showed the most consistent results between SPH and SEM. Four linkage regions with multipoint scores above 1.5 are near or within the known myopia (MYP) loci of MYP3, MYP12, MYP14, and MYP16. Overall, we observed consistent linkage signals across the SPH and SEM phenotypes, although scores were generally higher for the SEM phenotype.

Conclusions

Our quantitative trait linkage analyses of a large myopia family cohort provided additional evidence for several known MYP loci, and identified two additional potential loci at chromosome 6q13–16.1 and chromosome 5q35.1–35.2 for myopia. These results will benefit the efforts toward determining genes for myopic refractive error.

Alzheimer’s disease (AD) is a common neurodegenerative disorder of late life with a complex genetic basis. Although several genes are known to play a role in rare early-onset AD, only the APOE gene is known to have a high contribution to risk of the common late-onset form of the disease (LOAD, onset > 60 years). APOE genotypes vary in their AD risk as well as age-at-onset distributions, and it is likely that other loci will similarly affect AD age-at-onset. Here we present the first analysis of age-at-onset in the NIMH LOAD sample that allows for both a multilocus trait model and genetic heterogeneity among the contributing sites, while at the same time accommodating age censoring, effects of known genetic covariates, and full pedigree and marker information. The results provide evidence for genomic regions not previously implicated in this data set, including regions on chromosomes 7q, 15, and 19p. They also affirm evidence for loci on chromosomes 1q, 6p, 9q, 11, and, of course, the APOE locus on 19q, all of which have been reported previously in the same sample. The analyses failed to find evidence for linkage to chromosome 10 with inclusion of unaffected subjects and extended pedigrees. Several regions implicated in these analyses in the NIMH sample have been previously reported in genome scans of other AD samples. These results, therefore, provide independent confirmation of AD loci in family-based samples on chromosomes 1q, 7q, 19p, and suggest that further efforts towards identifying the underlying causal loci are warranted.

Objective

Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose methods for age at onset genetic linkage analysis in sibling pairs, taking into account parental age at onset.

Methods

Two new score statistics are derived, one from an additive gamma frailty model and one from a log-normal frailty model. The score statistics are classical non-parametric linkage (NPL) statistics weighted by a function of the age at onset of the four family members. The weight depends on information from registries (age-specific incidences) and family studies (sib-sib and father-mother correlation).

Results

In order to investigate how age at onset of sibs and their parents affect the information for linkage analysis the weight functions were studied for rare and common disease models, realistic models for breast cancer and human lifespan. We studied the performance of the weighted NPL methods by simulations. As illustration, the score statistics were applied to the GAW12 data. The results show that it is useful to include parental age at onset information in genetic linkage analysis.

Families with early-onset Alzheimer’s disease (AD) sharing a single PSEN2 mutation exhibit a wide range of age-at-onset, suggesting that modifier loci segregate within these families. While APOE is known to be an age-at-onset modifier, it does not explain all of this variation. We performed a genome scan within nine such families for loci influencing age-at-onset, while simultaneously controlling for variation in the primary PSEN2 mutation (N141I) and APOE. We found significant evidence of linkage between age-at-onset and chromosome 1q23.3 (P < 0.001) when analysis included all families, and to chromosomes 1q23.3 (P < 0.001), 17p13.2 (P = 0.0002), 7q33 (P = 0.017), and 11p14.2 (P = 0.017) in a single large pedigree. Simultaneous analysis of these four chromosomes maintained strong evidence of linkage to chromosomes 1q23.3 and 17p13.2 when all families were analyzed, and to chromosomes 1q23.3, 7q33, and 17p13.2 within the same single pedigree. Inclusion of major gene covariates proved essential to detect these linkage signals, as all linkage signals dissipated when PSEN2 and APOE were excluded from the model. The four chromosomal regions with evidence of linkage all coincide with previous linkage signals, associated SNPs, and/or candidate genes identified in independent AD study populations. This study establishes several candidate regions for further analysis and is consistent with an oligogenic model of AD risk and age-at-onset. More generally, this study also demonstrates the value of searching for modifier loci in existing datasets previously used to identify primary causal variants for complex disease traits.

Chromosomal synteny between the mouse model and humans was used to map a gene for the complex trait of obesity. Analysis of NZB/BINJ x SM/J intercross mice located a quantitative trait locus (QTL) for obesity on distal mouse chromosome 2, in a region syntenic with a large region of human chromosome 20, showing linkage to percent body fat (likelihood of the odds [LOD] score 3.6) and fat mass (LOD score 4.3). The QTL was confirmed in a congenic mouse strain. To test whether the QTL contributes to human obesity, we studied linkage between markers located within a 52-cM region extending from 20p12 to 20q13.3 and measures of obesity in 650 French Canadian subjects from 152 pedigrees participating in the Quebec Family Study. Sib-pair analysis based on a maximum of 258 sib pairs revealed suggestive linkages between the percentage of body fat (P < 0.004), body mass index (P < 0.008), and fasting insulin (P < 0.0005) and a locus extending approximately from ADA (the adenosine deaminase gene) to MC3R (the melanocortin 3 receptor gene). These data provide evidence that a locus on human chromosome 20q contributes to body fat and insulin in a human population, and demonstrate the utility of using interspecies syntenic relationships to find relevant disease loci in humans.

Background

Chickpea is a major crop in many drier regions of the world where it is an important protein-rich food and an increasingly valuable traded commodity. The wild annual Cicer species are known to possess unique sources of resistance to pests and diseases, and tolerance to environmental stresses. However, there has been limited utilization of these wild species by chickpea breeding programs due to interspecific crossing barriers and deleterious linkage drag. Molecular genetic diversity analysis may help predict which accessions are most likely to produce fertile progeny when crossed with chickpea cultivars. While, trait-markers may provide an effective tool for breaking linkage drag. Although SSR markers are the assay of choice for marker-assisted selection of specific traits in conventional breeding populations, they may not provide reliable estimates of interspecific diversity, and may lose selective power in backcross programs based on interspecific introgressions. Thus, we have pursued the development of gene-based markers to resolve these problems and to provide candidate gene markers for QTL mapping of important agronomic traits.

Results

An EST library was constructed after subtractive suppressive hybridization (SSH) of root tissue from two very closely related chickpea genotypes (Cicer arietinum). A total of 106 EST-based markers were designed from 477 sequences with functional annotations and these were tested on C. arietinum. Forty-four EST markers were polymorphic when screened across nine Cicer species (including the cultigen). Parsimony and PCoA analysis of the resultant EST-marker dataset indicated that most accessions cluster in accordance with the previously defined classification of primary (C. arietinum, C. echinospermum and C. reticulatum), secondary (C. pinnatifidum, C. bijugum and C. judaicum), and tertiary (C. yamashitae, C. chrossanicum and C. cuneatum) gene-pools. A large proportion of EST alleles (45%) were only present in one or two of the accessions tested whilst the others were represented in up to twelve of the accessions tested.

Conclusion

Gene-based markers have proven to be effective tools for diversity analysis in Cicer and EST diversity analysis may be useful in identifying promising candidates for interspecific hybridization programs. The EST markers generated in this study have detected high levels of polymorphism amongst both common and rare alleles. This suggests that they would be useful for allele-mining of germplasm collections for identification of candidate accessions in the search for new sources of resistance to pests / diseases, and tolerance to abiotic stresses.

Background

Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for hypertension phenotypes as well as epidemiological risk factors, respectively. Direct modelling of a large number of serially and biologically correlated traits in the context of linkage analysis can be prohibitively complex. Alternatively, we may consider using univariate transformation for linkage analysis of longitudinal repeated measures.

Results

We evaluated the utility of three conventional summary measures (mean, slope, and principal components) for genetic linkage analysis of longitudinal phenotypes by analyzing the chromosome 10 data of the Framingham Heart Study. Except for the temporal slope, all of the summary methods and the multivariate analysis identified the previously reported region, marker GATA64A09, for systolic blood pressure or high blood pressure. Further analysis revealed that this region may harbor gene(s) affecting human blood pressure at multiple stages of life.

Conclusion

We conclude that mean and principal components are feasible alternatives for genetic linkage analysis of longitudinal phenotypes, but the slope might have a separate genetic basis from that of the original longitudinal phenotypes.

An association between exposure to a risk factor and age-at-onset of disease may reflect an effect on the rate of disease occurrence or an acceleration of the disease process. The difference in age-at-onset arising from case-only studies, however, may also reflect secular trends in the prevalence of exposure to the risk factor. Comparisons of age-at-onset associated with risk factors are commonly performed in case series enrolled for genetic linkage analysis of late onset diseases. We describe how the results of age-at-onset studies of environmental risk factors reflect the underlying structure of the source population, rather than an association with age-at-onset, by contrasting the effects of coffee drinking and cigarette smoking on Parkinson disease age-at-onset with the effects on age-at-enrollment in a population based study sample. Despite earlier evidence to suggest a protective association of coffee drinking and cigarette smoking with Parkinson disease risk, the age-at-onset results are comparable to the patterns observed in the population sample, and thus a causal inference from the age-at-onset effect may not be justified. Protective effects of multivitamin use on PD age-at-onset are also shown to be subject to a bias from the relationship between age and multivitamin initiation. Case-only studies of age-at-onset must be performed with an appreciation for the association between risk factors and age and ageing in the source population.

Multivariate linkage analysis using several correlated traits may provide greater statistical power to detect susceptibility genes in loci whose effects are too small to be detected in univariate analysis. In this analysis, we apply a new approach and perform a linkage analysis of several electrophysiological phenotypes of the Collaborative Study on the Genetics of Alcoholism data of the Genetic Analysis Workshop 14. Our approach is based on a variance-component model to map candidate genes using repeated or longitudinal measurements. It can take into account covariate effects and time-dependent genetic effects in general pedigree data. We compare our results with the ones obtained by SOLAR using single measurement data. Our multivariate linkage analysis found linkage evidence on two regions on chromosome 4: around marker GABRB1 at 51.4 cM and marker FABP2 at 116.8 cM (unadjusted p-value = 0.00006).

The triple curve pattern (three lateral curvatures of equal severity) has been recognized as a distinct and unique clinical subtype of scoliosis. As part of a large study of familial idiopathic scoliosis (FIS), a subset of five families with a triple curve pattern (at least one member of each family having a triple curve) was evaluated to determine if this curve pattern was linked to any of the markers previously genotyped as part of the STRP-based previous linkage screen. Model independent linkage analysis (SIBPAL, v4.5) of the initial genomic screen identified candidate regions on chromosomes 6 and 10 when FIS was analyzed both as qualitative and quantitative traits in single- and multipoint linkage analyses. Additional fine mapping analyses of this subgroup with SNPs corroborated the findings in these regions (P <0.001). These regions have been previously linked to FIS, however, this is the first time these regions have been implicated in a clinically well-defined subgroup and may suggest a unique genetic etiology for the formation of a triple curve.

A broad region of chromosome 10 (chr10) has engendered continued interest in the etiology of late-onset Alzheimer Disease (LOAD) from both linkage and candidate gene studies. However, there is a very extensive heterogeneity on chr10. We converged linkage analysis and gene expression data using the concept of genomic convergence that suggests that genes showing positive results across multiple different data types are more likely to be involved in AD. We identified and examined 28 genes on chr10 for association with AD in a Caucasian case-control dataset of 506 cases and 558 controls with substantial clinical information. The cases were all LOAD (minimum age at onset ≥ 60 years). Both single marker and haplotypic associations were tested in the overall dataset and 8 subsets defined by age, gender, ApoE and clinical status. PTPLA showed allelic, genotypic and haplotypic association in the overall dataset. SORCS1 was significant in the overall data sets (p=0.0025) and most significant in the female subset (allelic association p=0.00002, a 3-locus haplotype had p=0.0005). Odds Ratio of SORCS1 in the female subset was 1.7 (p<0.0001). SORCS1 is an interesting candidate gene involved in the Aβ pathway. Therefore, genetic variations in PTPLA and SORCS1 may be associated and have modest effect to the risk of AD by affecting Aβ pathway. The replication of the effect of these genes in different study populations and search for susceptible variants and functional studies of these genes are necessary to get a better understanding of the roles of the genes in Alzheimer disease.

Background

Using the dataset provided for Genetic Analysis Workshop 14 by the Collaborative Study on the Genetics of Alcoholism, we performed genome-wide linkage analysis of age at onset of alcoholism to compare the utility of microsatellites and single-nucleotide polymorphisms (SNPs) in genetic linkage study.

Methods

A multipoint nonparametric variance component linkage analysis method was applied to the survival distribution function obtained from semiparametric proportional hazards model of the age at onset phenotype of alcoholism. Three separate linkage analyses were carried out using 315 microsatellites, 2,467 and 9,467 SNPs, spanning the 22 autosomal chromosomes.

Results

Heritability of age at onset was estimated to be approximately 12% (p < 0.001). We observed weak correlation, both in trend and strength, of genome-wide linkage signals between microsatellites and SNPs. Results from SNPs revealed more and stronger linkage signals across the genome compared with those from microsatellites. The only suggestive evidence of linkage from microsatellites was on chromosome 1 (LOD of 1.43). Differences in map densities between the two sets of SNPs used in this study did not appear to confer an advantage in terms of strength of linkage signals.

Conclusion

Our study provided support for better performance of dense SNP maps compared with the sparse mirosatellite maps currently available for linkage analysis of quantitative traits. This better performance could be attributable to precise definition and high map resolutions achievable with dense SNP maps, thus resulting in increased power to detect possible loci affecting given trait or disease.

Natural variation in gene expression is extensive in humans and other organisms, and variation in the baseline expression level of many genes has a heritable component. To localize the genetic determinants of these quantitative traits (expression phenotypes) in humans, we used microarrays to measure gene expression levels and performed genome-wide linkage analysis for expression levels of 3,554 genes in 14 large families. For approximately 1,000 expression phenotypes, there was significant evidence of linkage to specific chromosomal regions. Both cis- and trans-acting loci regulate variation in the expression levels of genes, although most act in trans. Many gene expression phenotypes are influenced by several genetic determinants. Furthermore, we found hotspots of transcriptional regulation where significant evidence of linkage for several expression phenotypes (up to 31) coincides, and expression levels of many genes that share the same regulatory region are significantly correlated. The combination of microarray techniques for phenotyping and linkage analysis for quantitative traits allows the genetic mapping of determinants that contribute to variation in human gene expression.