Genetic analysis of structural brain connectivity using DICCCOL models of diffusion MRI in 522 twins

Genetic and environmental factors affect white matter connectivity in the normal brain, and they also influence diseases in which brain connectivity is altered. Little is known about genetic influences on brain connectivity, despite wide variations in the brain's neural pathways. Here we applied the 'DICCCOL' framework to analyze structural connectivity, in 261 twin pairs (522 participants, mean age: 21.8 y ± 2.7SD). We encoded connectivity patterns by projecting the white matter (WM) bundles of all 'DICCCOLs' as a tracemap (TM). Next we fitted an A/C/E structural equation model to estimate additive genetic (A), common environmental (C), and unique environmental/error (E) components of the observed variations in brain connectivity. We found 44 'heritable DICCCOLs' whose connectivity was genetically influenced (α2>1%); half of them showed significant heritability (α2>20%). Our analysis of genetic influences on WM structural connectivity suggests high heritability for some WM projection patterns, yielding new targets for genome-wide association studies.

Genetic programming metamodel for rotating beams

This paper investigates the use of Genetic Programming (GP) to create an approximate model for the non-linear relationship between flexural stiffness, length, mass per unit length and rotation speed associated with rotating beams and their natural frequencies. GP, a relatively new form of artificial intelligence, is derived from the Darwinian concept of evolution and genetics and it creates computer programs to solve problems by manipulating their tree structures. GP predicts the size and structural complexity of the empirical model by minimizing the mean square error at the specified points of input-output relationship dataset. This dataset is generated using a finite element model. The validity of the GP-generated model is tested by comparing the natural frequencies at training and at additional input data points. It is found that by using a non-dimensional stiffness, it is possible to get simple and accurate function approximation for the natural frequency. This function approximation model is then used to study the relationships between natural frequency and various influencing parameters for uniform and tapered beams. The relations obtained with GP model agree well with FEM results and can be used for preliminary design and structural optimization studies.

A Haplotype at the UCP1 Gene Locus Contributes to Genetic Risk for Type 2 Diabetes in Asian Indians (CURES-72)

Background: The gene encoding for uncoupling protein-1 (UCP1) is considered to be a candidate gene for type 2 diabetes because of its role in thermogenesis and energy expenditure. The objective of the study was to examine whether genetic variations in the UCP1 gene are associated with type 2 diabetes and its related traits in Asian Indians. Methods: The study subjects, 810 type 2 diabetic subjects and 990 normal glucose tolerant (NGT) subjects, were chosen from the Chennai Urban Rural Epidemiological Study (CURES), an ongoing population-based study in southern India. The polymorphisms were genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Linkage disequilibrium (LD) was estimated from the estimates of haplotypic frequencies. Results: The three polymorphisms, namely -3826A -> G, an A -> C transition in the 5'-untranslated region (UTR) and Met229Leu, were not associated with type 2 diabetes. However, the frequency of the A-C-Met (-3826A -> G-5'UTR A -> C-Met229Leu) haplotype was significantly higher among the type 2 diabetic subjects (2.67%) compared with the NGT subjects (1.45%, P < 0.01). The odds ratio for type 2 diabetes for the individuals carrying the haplotype A-C-Met was 1.82 (95% confidence interval, 1.29-2.78, P = 0.009). Conclusions: The haplotype, A-C-Met, in the UCP1 gene is significantly associated with the increased genetic risk for developing type 2 diabetes in Asian Indians.

Molecular Genetic Studies of Melanocortin Receptors in Morbid Obesity

The prevalence of obesity is increasing at an alarming rate in all age groups worldwide. Obesity is a serious health problem due to increased risk of morbidity and mortality. Although environmental factors play a major role in the development of obesity, the identification of rare monogenic defects in human genes have confirmed that obesity has a strong genetic component. Mutations have been identified in genes encoding proteins of the leptin-melanocortin signaling system, which has an important role in the regulation of appetite and energy balance. The present study aimed at identifying mutations and genetic variations in the melanocortin receptors 2-5 and other genes active on the same signaling pathway accounting for severe early-onset obesity in children and morbid obesity in adults. The main achievement of this thesis was the identification of melanocortin-4 receptor (MC4R) mutations in Finnish patients. Six pathogenic MC4R mutations (308delT, P299H, two S127L and two -439delGC mutations) were identified, corresponding to a prevalence of 3% in severe early-onset obesity. No obesity causing MC4R mutations were found among patients with adult-onset morbid obesity. The MC4R 308delT deletion is predicted to result in a grossly truncated nonfunctional receptor of only 107 amino acids. The C-terminal residues, which are important in MC4R cell surface targeting, are totally absent from the mutant 308delT receptor. In vitro functional studies supported a pathogenic role for the S127L mutation since agonist induced signaling of the receptor was impaired. Cell membrane localization of the S127L receptor did not differ from that of the wild-type receptor, confirming that impaired function of the S127L receptor was due to reduced signaling properties. The P299H mutation leads to intracellular retention of the receptor. The -439delGC deletion is situated at a potential nescient helix-loop-helix 2 (NHLH2) -binding site in the MC4R promoter. It was demonstrated that the transcription factor NHLH2 binds to the consensus sequence at the -439delGC site in vitro, possibly resulting in altered promoter activity. Several genetic variants were identified in the melanocortin-3 receptor (MC3R) and pro-opiomelanocortin (POMC) genes. These polymorphisms do not explain morbid obesity, but the results indicate that some of these genetic variations may be modifying factors in obesity, resulting in subtle changes in obesity-related traits. A risk haplotype for obesity was identified in the ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) gene through a candidate gene single nucleotide polymorphism (SNP) genotyping approach. An ENPP1 haplotype, composed of SNPs rs1800949 and rs943003, was shown to be significantly associated with morbid obesity in adults. Accordingly, the MC3R, POMC and ENPP1 genes represent examples of susceptibility genes in which genetic variants predispose to obesity. In conclusion, pathogenic mutations in the MC4R gene were shown to account for 3% of cases with severe early-onset obesity in Finland. This is in line with results from other populations demonstrating that mutations in the MC4R gene underlie 1-6% of morbid obesity worldwide. MC4R deficiency thus represents the most common monogenic defect causing human obesity reported so far. The severity of the MC4-receptor defect appears to be associated with time of onset and the degree of obesity. Classification of MC4R mutations may provide a useful tool when predicting the outcome of the disease. In addition, several other genetic variants conferring susceptibility to obesity were detected in the MC3R, MC4R, POMC and ENPP1 genes.

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).