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Biomedical subjects

Julia N Bailey

Publications and source records attributed to Julia N Bailey.

13 recordsLinked to original sources
PubMed2007

The association of DRD4 and novelty seeking is found in a nonhuman primate model.

OBJECTIVE: The association of novelty seeking with a repeat polymorphism in the coding region of the dopamine D4 receptor gene (DRD4) has been demonstrated in several human populations, but not in others. The objective of this study was to test the generality of the association in a captive nonhuman primate population of known history, using objective methods for assessing novelty seeking and a pedigree-based association design. METHODS: Four hundred and fifty two socially-living vervet monkeys (Cercopithecus aethiops) from a large multigenerational pedigree at the UCLA-VA Vervet Research Colony were studied. Two variants in the 48 base pair repeat in exon III of the DRD4 gene have been found in this population, a six-repeat (92%) and a less common five-repeat (8%). Novelty seeking was measured by the latency to approach a large and potentially threatening novel object placed in the home enclosure. Heritability of novelty seeking and the association of novelty seeking with the DRD4 polymorphism were assessed using variance component modeling as implemented in Sequential Oligogenic Linkage Analysis Routines. RESULTS: The variance component analysis indicated that the DRD4 variant explained a significant portion of the total variance in novelty seeking. The final model included a significant effect of the DRD4 polymorphism (P=0.03), which explained 13% of the phenotypic variance, and a significant remaining genetic effect (h=0. 467+/-0.095, P<0.0001). CONCLUSIONS: The association of DRD4 with novelty seeking has now been replicated in a nonhuman primate species, the vervet monkey.

Analysis of Variance↗
PubMed2006

Mutation analyses of genes on 6p12-p11 in patients with juvenile myoclonic epilepsy.

Juvenile myoclonic epilepsy (JME) is a distinct form of idiopathic generalized epilepsy (IGE). One of the candidate regions for human JME has been mapped on chromosome band 6p11-p12 by linkage analyses and is termed EJM1 (MIM 254770). Recently, we reported the reduction of the EJM1 region to 3.5cM that contains 18 genes, the exclusion of three genes (LRRC1, GCLC, KIAA0057) by mutation analyses, and the identification of Myoclonin1/EFHC1 as the EJM1 gene. Here, we describe detailed physical and transcriptome maps of the 3.5cM EJM1 region, and detailed results of mutation analyses for the remained 14 genes (HELO1, GCMA, KIAA0936, FBXO9, GSTA3, GSTA4, PTD011, KIAA0576, LMPB1, IL17F, MCM3, PKHD1, KIAA0105, TFAP2B) in patients with JME. We identified 49 single nucleotide changes in eight genes. Twelve amino acid substitutions occurred in two genes, 11 silent mutations in seven genes, and 26 in the non-coding or intronic regions of seven genes. Twelve amino acid substitutions in the two genes (IL17F, PKHD1) were also observed in healthy control individuals or did not co-segregate with the disease phenotypes in other family members. Thus, the absence of significant and potentially functional mutations in the remaining 14 genes further supports the concept that Myoclonin1/EFHC1 is the EJM1 gene in chromosome 6p12.

Chromosomes, Human, Pair 6↗
PubMed2006

Juvenile myoclonic epilepsy subsyndromes: family studies and long-term follow-up.

The 2001 classification subcommittee of the International League Against Epilepsy (ILAE) proposed to 'group JME, juvenile absence epilepsy, and epilepsy with tonic clonic seizures only under the sole heading of idiopathic generalized epilepsies (IGE) with variable phenotype'. The implication is that juvenile myoclonic epilepsy (JME) does not exist as the sole phenotype of family members and that it should no longer be classified by itself or considered a distinct disease entity. Although recognized as a common form of epilepsy and presumed to be a lifelong trait, a long-term follow-up of JME has not been performed. To address these two issues, we studied 257 prospectively ascertained JME patients and encountered four groups: (i) classic JME (72%), (ii) CAE (childhood absence epilepsy) evolving to JME (18%), (iii) JME with adolescent absence (7%), and (iv) JME with astatic seizures (3%). We examined clinical and EEG phenotypes of family members and assessed clinical course over a mean of 11 +/- 6 years and as long as 52 years. Forty per cent of JME families had JME as their sole clinical phenotype. Amongst relatives of classic JME families, JME was most common (40%) followed by grand mal (GM) only (35%). In contrast, 66% of families with CAE evolving to JME expressed the various phenotypes of IGE in family members. Absence seizures were more common in family members of CAE evolving to JME than in those of classic JME families (P < 0.001). Female preponderance, maternal transmission and poor response to treatment further characterized CAE evolving to JME. Only 7% of those with CAE evolving to JME were seizure-free compared with 58% of those with classic JME (P < 0.001), 56% with JME plus adolescent pyknoleptic absence and 62% with JME plus astatic seizures. Long-term follow-up (1-40 years for classic JME; 5-52 years for CAE evolving to JME, 5-26 years for JME with adolescent absence and 3-18 years for JME with astatic seizures) indicates that all subsyndromes are chronic and perhaps lifelong. Seven chromosome loci, three epilepsy-causing mutations and two genes with single nucleotide polymorphisms (SNPs) associating with JME reported in literature provide further evidence for JME as a distinct group of diseases.

Adolescent↗
PubMed2005

Including endophenotypes as covariates in variance component heritability and linkage analysis.

The purpose of these analyses was to determine if incorporating or adjusting for covariates in genetic analyses helped or hindered in genetic analyses, specifically heritability and linkage analyses. To study this question, two types of covariate models were used in the simulated Genetic Analysis Workshop 14 dataset in which the true gene locations are known. All four populations of one replicate were combined for the analyses. The first model included typical covariates of sex and cohort (population) and the second included the typical covariates and also those related endophenotypes that are thought to be associated with the trait (phenotypes A, B, C, D, E, F, G, H, I, J, K, and L). A final best fit model produced in the heritability analyses was used for linkage. Linkage for disease genes D1, D3, and D4 were localized using models with and without the covariates. The use of inclusion of covariates did not appear to have any consistent advantage or disadvantage for the different phenotypes in regards to gene localization or false positive rate.

Genetic Linkage↗
PubMed2005

Dissection of heterogeneous phenotypes for quantitative trait mapping.

We discuss analyses of Genetic Analysis Workshop 14 data from the Collaborative Study on the Genetics of Alcoholism (COGA) as well as from a simulated complex disease, Kofendrerd personality disorder (KPD), with both genetic and phenotypic heterogeneity. Both data sets included numerous related phenotypes in addition to disease definitions. All analyses either chose from the given selection of phenotypes or defined new ones, including traits that may not have been related to alcoholism or KPD. Some contributors evaluated the genetic components of the trait. Many investigated genome-wide linkage and/or association, using microsatellites and/or single-nucleotide polymorphism (SNP) chip data. Here we will focus on methodological issues that the investigators faced. Their results depended on phenotype selection, whether continuous or discrete, the covariates included, and ethnicity of the study population. For SNP chip data, members of our group detected no difference in results for Affymetrix or Illumina chips, although higher marker density for association studies appeared to be advantageous. Overall, there were some observations that different chromosomal segments, i.e., physical locations on the p-arm, q-arm, or middle segment, might lead to possible differences in type I error rates. This finding and others highlight the importance of empirical determination of P-values to determine significance.

Alcoholism↗
PubMed2005

Seizures of idiopathic generalized epilepsies.

Idiopathic generalized epilepsies (IGEs) comprise at least 40% of epilepsies in the United States, 20% in Mexico, and 8% in Central America. Here, we review seizure phenotypes across IGE syndromes, their response to treatment and advances in molecular genetics that influence nosology. Our review included the Medline database from 1945 to 2005 and our prospectively collected Genetic Epilepsy Studies (GENESS) Consortium database. Generalized seizures occur with different and similar semiologies, frequencies, and patterns, ages at onset, and outcomes in different IGEs, suggesting common neuroanatomical pathways for seizure phenotypes. However, the same seizure phenotypes respond differently to the same treatments in different IGEs, suggesting different molecular defects across syndromes. De novo mutations in SCN1A in sporadic Dravet syndrome and germline mutations in SCN1A, SCN1B, and SCN2A in generalized epilepsies with febrile seizures plus have unraveled the heterogenous myoclonic epilepsies of infancy and early childhood. Mutations in GABRA1, GABRG2, and GABRB3 are associated with absence seizures, while mutations in CLCN2 and myoclonin/EFHC1 substantiate juvenile myoclonic epilepsy as a clinical entity. Refined understanding of seizure phenotypes, their semiology, frequencies, and patterns together with the identification of molecular lesions in IGEs continue to accelerate the development of molecular epileptology.

Adolescent↗
PubMed2004

Mutations in EFHC1 cause juvenile myoclonic epilepsy.

Juvenile myoclonic epilepsy (JME) is the most frequent cause of hereditary grand mal seizures. We previously mapped and narrowed a region associated with JME on chromosome 6p12-p11 (EJM1). Here, we describe a new gene in this region, EFHC1, which encodes a protein with an EF-hand motif. Mutation analyses identified five missense mutations in EFHC1 that cosegregated with epilepsy or EEG polyspike wave in affected members of six unrelated families with JME and did not occur in 382 control individuals. Overexpression of EFHC1 in mouse hippocampal primary culture neurons induced apoptosis that was significantly lowered by the mutations. Apoptosis was specifically suppressed by SNX-482, an antagonist of R-type voltage-dependent Ca(2+) channel (Ca(v)2.3). EFHC1 and Ca(v)2.3 immunomaterials overlapped in mouse brain, and EFHC1 coimmunoprecipitated with the Ca(v)2.3 C terminus. In patch-clamp analysis, EFHC1 specifically increased R-type Ca(2+) currents that were reversed by the mutations associated with JME.

Animals↗
PubMed2004

Genetic contributions to social impulsivity and aggressiveness in vervet monkeys.

BACKGROUND: Impulsivity contributes to multiple psychiatric disorders and sociobehavioral problems, and the more serious consequences of impulsivity are typically manifest in social situations. This study assessed the genetic contribution to impulsivity and aggressiveness in a social context using a nonhuman primate model. METHODS: Subjects were 352 adolescent and adult vervet monkeys from an extended multigenerational pedigree. Behavior was assessed in the Intruder Challenge Test, a standardized test that measures impulsivity and aggressiveness toward a stranger. Genetic and maternal contributions to variation in the Social Impulsivity Index and its two subscales, impulsive approach and aggression, were estimated using variance components analyses. RESULTS: The results found significant genetic contributions to social impulsivity (h2 =.35 +/-.11) and to each of the subscales, with no significant influence of maternal environment. There was a high genetic correlation between the impulsive approach and aggression subscales (rho =.78 +/-.12). CONCLUSIONS: This is the first study to demonstrate heritability of social impulsivity in adolescents and adults for any nonhuman primate species. The high genetic correlation suggests the same genes may influence variation in both impulsive approach and aggression. These results provide a promising basis for identification of susceptibility loci for impulsivity and aggressiveness.

Age Factors↗
PubMed2002

Juvenile myoclonic epilepsy: linkage to chromosome 6p12 in Mexico families.

Juvenile myoclonic epilepsy is a common subtype of idiopathic epilepsy accounting for 4-11% of all epilepsies. We reported previously significant evidence of linkage between chromosome 6p12-11 microsatellites and the clinical epilepsy and EEG traits of JME families from Belize and Los Angeles. To narrow the JME region, we ascertained and genotyped 31 new JME families from Mexico using a later generation of Généthon microsatellites. Two point linkage analyses obtained significant Z(max) values of 3.70 for D6S1573 and 2.65 for D6S1714 at theta(m = f) = 0.10, and 3.49 for D6S465, 2.11 for D6S1960 at theta(m = f) = 0.05 assuming autosomal dominant inheritance with 70% age-dependent penetrance. Multipoint LOD score curve peaked at 4.21 for D6S1573. Haplotype and recombination analysis reduced the JME region to 3.5 cM flanked by D6S272 and D6S1573. These results provide confirmatory evidence that a major susceptibility gene for JME exists in chromosome 6p12 in Spanish-Amerinds of Mexico.

Chromosome Mapping↗
PubMed2002

Identification and mutational analysis of candidate genes for juvenile myoclonic epilepsy on 6p11-p12: LRRC1, GCLC, KIAA0057 and CLIC5.

Juvenile myoclonic epilepsy (JME) is one of the most frequent hereditary epilepsies characterized by myoclonic and tonic-clonic convulsions beginning at 8-20 years of age. Genetic studies have revealed four major chromosomal loci on 6p21.3, 6p11-12, 6q24, and 15q14 as candidate regions harboring genes responsible for JME. Previously we reported the region on 6p11-p12 (EJM1), and here we report the identification and mutational analysis of candidate genes for EJM1. One of those is a leucine-rich repeat-containing 1 (LRRC1) gene that is composed of 14 exons and codes for 524 amino acid residues. In Northern analysis, 7 kb transcripts of LRRC1 gene were detected in multiple tissues, most strongly, in heart, lung, and kidney. Mutation analysis of LRRC1 gene in 20 JME patients from ten families revealed one nucleotide substitution that lead to amino acid exchange (c.577 A>G; Ile193Val). This variation, however, did not co-segregate with the disease phenotype. We further performed mutational analyses of CLIC5, KIAA0057 and GCLC genes in or flank to the EJM1 region. These analyses did not provide any evidences that these genes are responsible for the JME phenotype, and suggested that these may not be the EJM1 gene.

Base Sequence↗