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1.  Selection of eating-disorder phenotypes for linkage analysis 
Vulnerability to anorexia nervosa (AN) and bulimia nervosa (BN) arise from the interplay of genetic and environmental factors. To explore the genetic contribution, we measured over 100 psychiatric, personality and temperament phenotypes of individuals with eating disorders from 154 multiplex families accessed through an AN proband (AN cohort) and 244 multiplex families accessed through a BN proband (BN cohort). To select a parsimonious subset of these attributes for linkage analysis, we subjected the variables to a multilayer decision process based on expert evaluation and statistical analysis. Criteria for trait choice included relevance to eating disorders pathology, published evidence for heritability, and results from our data. Based on these criteria, we chose six traits to analyze for linkage. Obsessionality, Age-at-Menarche, and a composite Anxiety measure displayed features of heritable quantitative traits, such as normal distribution and familial correlation, and thus appeared ideal for quantitative trait locus (QTL) linkage analysis. By contrast, some families showed highly concordant and extreme values for three variables — lifetime minimum Body Mass Index (lowest BMI attained during the course of illness), concern over mistakes, and food-related obsessions — whereas others did not. These distributions are consistent with a mixture of populations, and thus the variables were matched with covariate linkage analysis. Linkage results appear in a subsequent report. Our report lays out a systematic roadmap for utilizing a rich set of phenotypes for genetic analyses, including the selection of linkage methods paired to those phenotypes.
PMCID: PMC2560991  PMID: 16152575
Complex disease; endophenotype; liability; clinical judgment; covariate selection; mixture model; regression
2.  Identification of susceptibility loci for complex diseases in a case-control association study using the Genetic Analysis Workshop 14 dataset 
BMC Genetics  2005;6(Suppl 1):S102.
Although current methods in genetic epidemiology have been extremely successful in identifying genetic loci responsible for Mendelian traits, most common diseases do not follow simple Mendelian modes of inheritance. It is important to consider how our current methodologies function in the realm of complex diseases. The aim of this study was to determine the ability of conventional association methods to fine map a locus of interest. Six study populations were selected from 10 replicates (New York) from the Genetic Analysis Workshop 14 simulated dataset and analyzed for association between the disease trait and locus D2. Genotypes from 45 single-nucleotide polymorphisms in the telomeric region of chromosome 3 were analyzed by Pearson's chi-square tests for independence to test for association with the disease trait of interest. A significant association was detected within the region; however, it was found 3 cM from the documented location of the D2 disease locus. This result was most likely due to the method used for data simulation. In general, this study showed that conventional case-control association methods could detect disease loci responsible for the development of complex traits.
PMCID: PMC1866837  PMID: 16451558
3.  Linkage analysis of the GAW14 simulated dataset with microsatellite and single-nucleotide polymorphism markers in large pedigrees 
BMC Genetics  2005;6(Suppl 1):S14.
Recent studies have suggested that a high-density single nucleotide polymorphism (SNP) marker set could provide equivalent or even superior information compared with currently used microsatellite (STR) marker sets for gene mapping by linkage. The focus of this study was to compare results obtained from linkage analyses involving extended pedigrees with STR and single-nucleotide polymorphism (SNP) marker sets. We also wanted to compare the performance of current linkage programs in the presence of high marker density and extended pedigree structures. One replicate of the Genetic Analysis Workshop 14 (GAW14) simulated extended pedigrees (n = 50) from New York City was analyzed to identify the major gene D2. Four marker sets with varying information content and density on chromosome 3 (STR [7.5 cM]; SNP [3 cM, 1 cM, 0.3 cM]) were analyzed to detect two traits, the original affection status, and a redefined trait more closely correlated with D2. Multipoint parametric and nonparametric linkage analyses (NPL) were performed using programs GENEHUNTER, MERLIN, SIMWALK2, and S.A.G.E. SIBPAL. Our results suggested that the densest SNP map (0.3 cM) had the greatest power to detect linkage for the original trait (genetic heterogeneity), with the highest LOD score/NPL score and mapping precision. However, no significant improvement in linkage signals was observed with the densest SNP map compared with STR or SNP-1 cM maps for the redefined affection status (genetic homogeneity), possibly due to the extremely high information contents for all maps. Finally, our results suggested that each linkage program had limitations in handling the large, complex pedigrees as well as a high-density SNP marker set.
PMCID: PMC1866796  PMID: 16451599
4.  Effects of DNA mass on multiple displacement whole genome amplification and genotyping performance 
BMC Biotechnology  2005;5:24.
Whole genome amplification (WGA) promises to eliminate practical molecular genetic analysis limitations associated with genomic DNA (gDNA) quantity. We evaluated the performance of multiple displacement amplification (MDA) WGA using gDNA extracted from lymphoblastoid cell lines (N = 27) with a range of starting gDNA input of 1–200 ng into the WGA reaction. Yield and composition analysis of whole genome amplified DNA (wgaDNA) was performed using three DNA quantification methods (OD, PicoGreen® and RT-PCR). Two panels of N = 15 STR (using the AmpFlSTR® Identifiler® panel) and N = 49 SNP (TaqMan®) genotyping assays were performed on each gDNA and wgaDNA sample in duplicate. gDNA and wgaDNA masses of 1, 4 and 20 ng were used in the SNP assays to evaluate the effects of DNA mass on SNP genotyping assay performance. A total of N = 6,880 STR and N = 56,448 SNP genotype attempts provided adequate power to detect differences in STR and SNP genotyping performance between gDNA and wgaDNA, and among wgaDNA produced from a range of gDNA templates inputs.
The proportion of double-stranded wgaDNA and human-specific PCR amplifiable wgaDNA increased with increased gDNA input into the WGA reaction. Increased amounts of gDNA input into the WGA reaction improved wgaDNA genotyping performance. Genotype completion or genotype concordance rates of wgaDNA produced from all gDNA input levels were observed to be reduced compared to gDNA, although the reduction was not always statistically significant. Reduced wgaDNA genotyping performance was primarily due to the increased variance of allelic amplification, resulting in loss of heterozygosity or increased undetermined genotypes. MDA WGA produces wgaDNA from no template control samples; such samples exhibited substantial false-positive genotyping rates.
The amount of gDNA input into the MDA WGA reaction is a critical determinant of genotyping performance of wgaDNA. At least 10 ng of lymphoblastoid gDNA input into MDA WGA is required to obtain wgaDNA TaqMan® SNP assay genotyping performance equivalent to that of gDNA. Over 100 ng of lymphoblastoid gDNA input into MDA WGA is required to obtain optimal STR genotyping performance using the AmpFlSTR® Identifiler® panel from wgaDNA equivalent to that of gDNA.
PMCID: PMC1249558  PMID: 16168060
5.  Linkage analysis of anorexia and bulimia nervosa cohorts using selected behavioral phenotypes as quantitative traits or covariates 
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.
PMCID: PMC2590774  PMID: 16152574
Complex disease; endophenotype; liability; mixture model; regression

Results 1-5 (5)