Clinical Phenotype and Genetic Epidemiology of Schizophrenia in a Finnish Isolate

This study is part of the joint project "The Genetic Epidemiology and Molecular Genetics of schizophrenia in Finland" between the Departments of Mental Health and Alcohol Research, and Molecular Medicine at the National Public Health Institute. In the study, we utilized three nationwide health care registers: 1) the Hospital Discharge Register, 2) the Free Medication Register, and 3) the Disability Pension Register, plus the National Population Register, in order to identify all patients with schizophrenia born from 1940 to 1976 (N=33,731) in Finland, and their first degree-relatives. 658 patients with at least one parent born in a homogeneous isolate in northeastern Finland were identified, as well as 4904 familial schizophrenia patients with at least two affected siblings from the whole country. The comparison group was derived from the Health 2000 Study. We collected case records and reassessed the register diagnosis. Were contacted the isolate patients and a random sample of patients from the whole country to make diagnostic clinical interviews and to assess the negative and positive symptoms and signs of schizophrenia. In addition to these patients, we interviewed siblings who were initially healthy according to the Hospital Discharge Register. Of those with a register diagnosis of schizophrenia, schizoaffective or schizophreniform disorder, 69% received a record-based consensus diagnosis and 63% an interview-based diagnosis of schizophrenia. Patients with schizophrenia having first-degree relatives with psychotic disorder had more severe affective flattening and alogia than those who were the only affected individuals in their family. The novel findings were: 1) The prevalence of schizophrenia in the isolate was relatively high based on register (1.5%), case record (0.9-1.3%), and interview (0.7-1.2%) data. 2) Isolate patients, regardless of their familial loading for schizophrenia, had less delusions and hallucinations than the whole country familial patients, which may be related to the genetic homogeneity in the isolate. This phenotype encourages the use of endophenotypes in genetic analyses instead of diagnoses alone. 3) The absence of register diagnosis does not confirm that siblings are healthy, because 7.7% of siblings had psychotic symptoms already before the register diagnoses were identified in 1991. For genetic research, the register diagnosis should therefore be reassessed using either a structured interview or a best- estimate case note consensus diagnosis. Structured clinical interview methods need be considered also in clinical practice.

Intrahepatic cholestasis of pregnancy : genetic background, epidemiology and hepatobiliary consequences

Intrahepatic cholestasis of pregnancy (ICP) is the most common cholestatic liver disease during pregnancy. The reported incidence varies from 0.4 to 15% of full-term pregnancies. The etiology is heterogeneous but familial clustering is known to occur. Here we have studied the genetic background, epidemiology, and long-term hepatobiliary consequences of ICP. In a register-based nation-wide study (n=1 080 310) the incidence of ICP was 0.94% during 1987-2004. A slightly higher incidence, 1.3%, was found in a hospital-based series (n=5304) among women attending the University Hospital of Helsinki in 1992-1993. Of these 16% (11/69) were familial and showed a higher (92%) recurrence rate than the sporadic (40%) cases. In the register-based epidemiological study, advanced maternal age and, to a lesser degree, parity were identified as new risk factors for ICP. The risk was 3-fold higher in women >39 years of age compared to women <30 years. Multiple pregnancy also associated with an elevated risk. In a genetic study we found no association of ICP with the genes regulating bile salt transport (ABCB4, ABCB11 and ATP8B1). The livers of postmenopausal women with a history of ICP tolerated well the short-term exposure to oral and transdermal estradiol, although the doses used were higher than those in routine clinical use. The response of serum levels of sex hormone-binding globulin (SHBG) to oral estradiol was slightly reduced in the ICP group. Transdermal estradiol had no effect on C-reactive protein (CRP) or SHBG. A number of liver and biliary diseases were found to be associated with ICP. Women with a history of ICP showed elevated risks for non-alcoholic liver cirrhosis (8.2 CI 1.9-36), cholelithiasis and cholecystitis (3.7 CI 3.2-4.2), hepatitis C (3.5 CI 1.6-7.6) and non-alcoholic pancreatitis (3.2 CI 1.7-5.7). In conclusion, ICP complicates around 1% of all full-term pregnancies in Finland and its incidence has remained unchanged since 1987. It is familial in 16% of cases with a higher recurrence rate. Although the cause remains unknown, several risk factors, namely advanced maternal age, parity and multiple pregnancies, can be identified. Both oral and transdermal regimens of postmenopausal hormone therapy (HT) are safe for women with a history of ICP when liver function is considered. Some ICP patients are at risk of other liver and biliary diseases and, contrary to what has been thought, a follow-up is warranted.

Real-coded genetic algorithm for machining condition optimization

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

A real coded genetic algorithm for data partitioning and scheduling in networks with arbitrary processor release time

The problem of scheduling divisible loads in distributed computing systems, in presence of processor release time is considered. The objective is to find the optimal sequence of load distribution and the optimal load fractions assigned to each processor in the system such that the processing time of the entire processing load is a minimum. This is a difficult combinatorial optimization problem and hence genetic algorithms approach is presented for its solution.

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems. (C) 2004 Elsevier Ltd. All rights reserved.

Data integration from two microarray platforms identifies bi-allelic genetic inactivation of RIC8A in a breast cancer cell line

Background: Using array comparative genomic hybridization (aCGH), a large number of deleted genomic regions have been identified in human cancers. However, subsequent efforts to identify target genes selected for inactivation in these regions have often been challenging. Methods: We integrated here genome-wide copy number data with gene expression data and non-sense mediated mRNA decay rates in breast cancer cell lines to prioritize gene candidates that are likely to be tumour suppressor genes inactivated by bi-allelic genetic events. The candidates were sequenced to identify potential mutations. Results: This integrated genomic approach led to the identification of RIC8A at 11p15 as a putative candidate target gene for the genomic deletion in the ZR-75-1 breast cancer cell line. We identified a truncating mutation in this cell line, leading to loss of expression and rapid decay of the transcript. We screened 127 breast cancers for RIC8A mutations, but did not find any pathogenic mutations. No promoter hypermethylation in these tumours was detected either. However, analysis of gene expression data from breast tumours identified a small group of aggressive tumours that displayed low levels of RIC8A transcripts. qRT-PCR analysis of 38 breast tumours showed a strong association between low RIC8A expression and the presence of TP53 mutations (P = 0.006). Conclusion: We demonstrate a data integration strategy leading to the identification of RIC8A as a gene undergoing a classical double-hit genetic inactivation in a breast cancer cell line, as well as in vivo evidence of loss of RIC8A expression in a subgroup of aggressive TP53 mutant breast cancers.

Using IRAP markers for analysis of genetic variability in populations of resource and rare species of plants

Species specific LTR retrotransposons were first cloned in five rare relic species of drug plants located in the Perm’ region. Sequences of LTR retrotransposons were used for PCR analysis based on amplification of repeated sequences from LTR or other sites of retrotransposons (IRAP). Genetic diversity was studied in six populations of rare relic species of plants Adonis vernalis L. by means of the IRAP method; 125 polymorphic IRAP markers were analyzed. Parameters for DNA polymorphism and genetic diversity of A. vernalis populations were determined.