Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets

Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation

Impaired hepatic lipid synthesis from polyunsaturated fatty acids in TM6SF2 E167K variant carriers with NAFLD

Background: Carriers of the transmembrane 6 superfamily member 2 E167K gene variant (TM6SF2(EK/KK)) have decreased expression of the TM6SF2 gene and increased risk of NAFLD and NASH. Unlike common 'obese/metabolic' NAFLD, these subjects lack hypertriglyceridemia and have lower risk of cardiovascular disease. In animals, phosphatidylcholine (PC) deficiency results in a similar phenotype. PCs surround the core of VLDL consisting of triglycerides (TGs) and cholesteryl-esters (CEs). We determined the effect of the TM6SF2 E167K on these lipids in the human liver and serum and on hepatic gene expression and studied the effect of TM6SF2 knockdown on hepatocyte handling of these lipids. Methods: Liver biopsies were taken from subjects characterized with respect to the TM6SF2 genotype, serum and liver lipidome, gene expression and histology. In vitro, after TM6SF2 knockdown in HuH-7 cells, we compared incorporation of different fatty acids into TGs, CEs, and PCs. Results: The TM6SF2(EK/KK) and TM6SF2EE groups had similar age, gender, BMI and HOMA-IR. Liver TGs and CEs were higher and liver PCs lower in the TM6SF2(EK/KK) than the TM6SF2EE group (p Conclusions: Hepatic lipid synthesis from PUFAs is impaired and could contribute to deficiency in PCs and increased intrahepatic TG in TM6SF2 E167K variant carriers. (C) 2017 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.Peer reviewe

Uncovering the complex genetics of human character

Human personality is 30-60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic-phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory.Peer reviewe

Three genetic-environmental networks for human personality

Phylogenetic, developmental, and brain-imaging studies suggest that human personality is the integrated expression of three major systems of learning and memory that regulate (1) associative conditioning, (2) intentionality, and (3) self-awareness. We have uncovered largely disjoint sets of genes regulating these dissociable learning processes in different clusters of people with (1) unregulated temperament profiles (i.e., associatively conditioned habits and emotional reactivity), (2) organized character profiles (i.e., intentional self-control of emotional conflicts and goals), and (3) creative character profiles (i.e., self-aware appraisal of values and theories), respectively. However, little is known about how these temperament and character components of personality are jointly organized and develop in an integrated manner. In three large independent genome-wide association studies from Finland, Germany, and Korea, we used a data-driven machine learning method to uncover joint phenotypic networks of temperament and character and also the genetic networks with which they are associated. We found three clusters of similar numbers of people with distinct combinations of temperament and character profiles. Their associated genetic and environmental networks were largely disjoint, and differentially related to distinct forms of learning and memory. Of the 972 genes that mapped to the three phenotypic networks, 72% were unique to a single network. The findings in the Finnish discovery sample were blindly and independently replicated in samples of Germans and Koreans. We conclude that temperament and character are integrated within three disjoint networks that regulate healthy longevity and dissociable systems of learning and memory by nearly disjoint sets of genetic and environmental influences.Peer reviewe

Uncovering the complex genetics of human temperament

Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic-phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37-53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.Peer reviewe

Extra-Intestinal Manifestations of Familial Adenomatous Polyposis

Familial adenomatous polyposis (FAP) is an autosomal dominantly inherited disorder, which results from a germ line mutation in the APC (adenomatous polyposis coli) gene. FAP is characterized by the formation of hundreds to thousands of colorectal adenomatous polyps. Although the development of colorectal cancer stands out as the most prevalent complication, FAP is a multisystem disorder of growth. This means, it is comparable to other diseases such as the MEN syndromes, Von Hippel-Lindau disease and neurofibromatosis. However, the incidence of many of its clinical features is much lower. Therefore, a specialized multidisciplinary approach to optimize health care—common for other disorders—is not usually taken for FAP patients. Thus, clinicians that care for and counsel members of high-risk families should have familiarity with all the extra-intestinal manifestations of this syndrome. FAP-related complications, for which medical attention is essential, are not rare and their estimated lifetime risk presumably exceeds 30%. Affected individuals can develop thyroid and pancreatic cancer, hepatoblastomas, CNS tumors (especially medulloblastomas), and various benign tumors such as adrenal adenomas, osteomas, desmoid tumors and dental abnormalities. Due to improved longevity, as a result of better prevention of colorectal cancer, the risk of these clinical problems will further increase

Aerosol-optics model for the backscatter depolarisation ratio of mineral dust particles

The size-dependence of the linear depolarisation ratio of mineral dust aerosols is investigated. Laboratory measurements on 131 different aerosol samples with varying size distributions and mineralogical compositions are fitted with a homogeneous spheroid model. A minimum-bias and minimum-variance fit of the data is obtained for prolate model particles with a refractive index of 1.525+0.001i and an aspect ratio of 0.87. The model error is analysed by varying the input parameters to the light-scattering computations. It is found that the scattering of the measurements about the model can mainly be explained by variation of the morphology and dielectric properties, and to a much lesser extent by variation in the geometric standard deviation of the size distribution. The modelling of the data is extended by using size-shape distributions of spheroids. The results indicate that there is some freedom in choosing the best-fit weights of the shape-distribution of spheroids, which could potentially be useful when extending the model to multiple wavelengths, or to including additional optical parameters other than depolarisation. However, it is also found that the most reasonable fits of the data are obtained by mildly aspherical prolate and oblate spheroids, which limits the freedom of adjusting the best-fit weights. (C) 2020 The Authors. Published by Elsevier Ltd