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1.  Genomewide Linkage Analysis in Costa Rican Families Implicates Chromosome 15q14 as a Candidate Region for OCD 
Human genetics  2011;130(6):795-805.
Obsessive compulsive disorder (OCD) has a complex etiology that encompasses both genetic and environmental factors. However, to date, despite the identification of several promising candidate genes and linkage regions, the genetic causes of OCD are largely unknown. The objective of this study was to conduct linkage studies of childhood-onset OCD, which is thought to have the strongest genetic etiology, in several OCD-affected families from the genetically isolated population of the Central Valley of Costa Rica (CVCR). The authors used parametric and non-parametric approaches to conduct genome-wide linkage analyses using 5786 single nucleotide repeat polymorphisms (SNPs) in three CVCR families with multiple childhood-onset OCD-affected individuals. We identified areas of suggestive linkage (LOD score ≥2) on chromosomes 1p21, 15q14, 16q24, and 17p12. The strongest evidence for linkage was on chromosome 15q14 (LOD=3.13), identified using parametric linkage analysis with a recessive model, and overlapping a region identified in a prior linkage study using a Caucasian population. Each CVCR family had a haplotype that co-segregated with OCD across a ~7Mbp interval within this region, which contains 18 identified brain expressed genes, several of which are potentially relevant to OCD. Exonic sequencing of the strongest candidate gene in this region, the ryanodine receptor 3 (RYR3), identified several genetic variants of potential interest, although none cosegregated with OCD in all three families. These findings provide evidence that chromosome 15q14 is linked to OCD in families from the CVCR, and supports previous findings to suggest that this region may contain one or more OCD susceptibility loci.
PMCID: PMC4442699  PMID: 21691774
Obsessive Compulsive Disorder; Genetic Linkage; Genetic Isolate; Genetic Loci; Humans; Genetic Predisposition to Disease
2.  A high resolution HLA and SNP haplotype map for disease association studies in the extended human MHC 
Nature genetics  2006;38(10):1166-1172.
The proteins encoded by the classical HLA class I and class II genes in the major histocompatibility complex (MHC) are highly polymorphic and play an essential role in self/non-self immune recognition. HLA variation is a crucial determinant of transplant rejection and susceptibility to a large number of infectious and autoimmune disease1. Yet identification of causal variants is problematic due to linkage disequilibrium (LD) that extends across multiple HLA and non-HLA genes in the MHC2,3. We therefore set out to characterize the LD patterns between the highly polymorphic HLA genes and background variation by typing the classical HLA genes and >7,500 common single nucleotide polymorphisms (SNPs) and deletion/insertion polymorphisms (DIPs) across four population samples. The analysis provides informative tag SNPs that capture some of the variation in the MHC region and that could be used in initial disease association studies, and provides new insight into the evolutionary dynamics and ancestral origins of the HLA loci and their haplotypes.
PMCID: PMC2670196  PMID: 16998491
3.  Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14 
BMC Genetics  2005;6(Suppl 1):S2.
The data provided to the Genetic Analysis Workshop 14 (GAW 14) was the result of a collaboration among several different groups, catalyzed by Elizabeth Pugh from The Center for Inherited Disease Research (CIDR) and the organizers of GAW 14, Jean MacCluer and Laura Almasy. The DNA, phenotypic characterization, and microsatellite genomic survey were provided by the Collaborative Study on the Genetics of Alcoholism (COGA), a nine-site national collaboration funded by the National Institute of Alcohol and Alcoholism (NIAAA) and the National Institute of Drug Abuse (NIDA) with the overarching goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. CIDR, Affymetrix, and Illumina provided single-nucleotide polymorphism genotyping of a large subset of the COGA subjects. This article briefly describes the dataset that was provided.
PMCID: PMC1866767  PMID: 16451628
4.  Evaluation of linkage disequilibrium and its effect on non-parametric multipoint linkage analysis using two high density single-nucleotide polymorphism mapping panels 
BMC Genetics  2005;6(Suppl 1):S85.
Genotype data from the Illumina Linkage III SNP panel (n = 4,720 SNPs) and the Affymetrix 10 k mapping array (n = 11,120 SNPs) were used to test the effects of linkage disequilibrium (LD) between SNPs in a linkage analysis in the Collaborative Study on the Genetics of Alcoholism pedigree collection (143 pedigrees; 1,614 individuals). The average r2 between adjacent markers across the genetic map was 0.099 ± 0.003 in the Illumina III panel and 0.17 ± 0.003 in the Affymetrix 10 k array. In order to determine the effect of LD between marker loci in a nonparametric multipoint linkage analysis, markers in strong LD with another marker (r2 > 0.40) were removed (n = 471 loci in the Illumina panel; n = 1,804 loci in the Affymetrix panel) and the linkage analysis results were compared to the results using the entire marker sets. In all analyses using the ALDX1 phenotype, 8 linkage regions on 5 chromosomes (2, 7, 10, 11, X) were detected (peak markers p < 0.01), and the Illumina panel detected an additional region on chromosome 6. Analysis of the same pedigree set and ALDX1 phenotype using short tandem repeat markers (STRs) resulted in 3 linkage regions on 3 chromosomes (peak markers p < 0.01). These results suggest that in this pedigree set, LD between loci with spacing similar to the SNP panels tested may not significantly affect the overall detection of linkage regions in a genome scan. Moreover, since the data quality and information content are greatly improved in the SNP panels over STR genotyping methods, new linkage regions may be identified due to higher information content and data quality in a dense SNP linkage panel.
PMCID: PMC1866695  PMID: 16451700

Results 1-4 (4)