7^(th) International Imaging Genetics Conference

The Annual International Imaging Genetics Conferences focus on the advancement of the new discipline, Imaging Genetics, and the transdisciplinary fusion that is its foundation. The Conferences bring together national and international experts in neuroimaging, genetics, data-mining, visualization and statistics, to present their research and findings as they relate to the field of Imaging Genetics.

Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease

Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson’s disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease. Forty-eight Parkinson’s disease patients and 39 matched healthy controls underwent genotyping and 7 T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson’s disease diagnosis. We found that, in Parkinson’s disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein(SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson’s disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson’s disease.

inMTSCCA:An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics

Identifying genetic risk factors for Alzheimer's disease(AD)is an important research topic.To date,different endophenotypes,such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes,have shown the great value in uncovering risk genes compared to case-control studies.Biologically,a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis.However,existing methods mainly focus on the effect of endophenotypes alone;the effect of cross-endophenotype(CEP)associations remains largely unexploited.In this study,we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors,and proposed two integrated multi-task sparse canonical correlation analysis(inMTSCCA)methods,i.e.,pairwise endophenotype correlationguided MTSCCA(pcMTSCCA)and high-order endophenotype correlation-guided MTSCCA(hocMTSCCA).pcMTSCCA employed pairwise correlations between magnetic resonance imaging(MRI)-derived,plasma-derived,and cerebrospinal fluid(CSF)-derived endophenotypes as an additional penalty.hocMTSCCA used high-order correlations among these multi-omic data for regularization.To figure out genetic risk factors at individual and group levels,as well as altered endophenotypic markers,we introduced sparsity-inducing penalties for both models.We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real(consisting of neuroimaging data,proteomic analytes,and genetic data)datasets.The results showed that our methods obtained better or comparable canonical correlation coefficients(CCCs)and better feature subsets than benchmarks.Most importantly,the identified genetic loci and heterogeneous endophenotypic markers showed high relevance.Therefore,jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors.

maging genetics -towards discovery neuroscience

Imaging genetics is an emerging field aimed at identifying and characterizing genetic variants that influence measures derived from anatomical or functional brain images, which are in turn related to brain-related illnesses or fundamental cognitive, emotional and behavioral processes, and are affected by environmental factors. Here we review the recent evolution of statistical approaches and outstanding challenges in imaging genetics, with a focus on population-based imaging genetic association studies. We show the trend in imaging genetics from candidate approaches to pure discovery science, and from univariate to multivariate analyses. We also discuss future directions and prospects of imaging genetics for ultimately helping understand the genetic and environmental underpinnings of various neuropsychiatric disorders and turning basic science into clinical strategies.

The improved genetic algorithms for digital image correlation method

We present a global optimization method, called the genetic algorithms (GAs), for digital image/speckle correlation (DISC). The new algorithms do not involve reasonable initial guess of displacement and deformation gradient and the calculation of second-order spatial derivatives of the digital images, which are important challenges in practical implementation of DISC. The performance of a GA depends largely on the selection of the genetic operators. We test various operators and propose optimal operators. The algorithms are then verified using simulated images and experimental speckle images.

ZNF804A Variation May Affect Hippocampal-Prefrontal Resting-State Functional Connectivity in Schizophrenic and Healthy Individuals

The ZNF804 A variant rs1344706 has consistently been associated with schizophrenia and plays a role in hippocampal-prefrontal functional connectivity during working memory. Whether the effect exists in the resting state and in patients with schizophrenia remains unclear. In this study, we investigated the ZNF804 A polymorphism at rs1344706 in 92 schizophrenic patients and 99 healthy controls of Han Chinese descent, and used resting-state functional magnetic resonance imaging to explore the functional connectivity in the participants. We found a significant main effect of genotype on the resting-state functional connectivity（RSFC） between the hippocampus and the dorsolateral prefrontal cortex（DLPFC） in both schizophrenic patients and healthy controls. The homozygous ZNF804 A rs1344706 genotype（AA） conferred a high risk of schizophrenia, and also exhibited significantly decreased resting functional coupling between the left hippocampus and right DLPFC（F（2,165） = 13.43,P ／ 0.001）. The RSFC strength was also correlated with cognitive performance and the severity of psychosis in schizophrenia. The current findings identified the neural impact of the ZNF804 A rs1344706 on hippocampalprefrontal RSFC associated with schizophrenia.

How can we harness quantitative genetic variation in crop root systems for agricultural improvement?

Root systems are a black box obscuring a comprehensive understanding of plant function,from the ecosystem scale down to the individual. In particular,a lack of knowledge about the genetic mechanisms and environmental effects that condition root system growth hinders our ability to develop the next generation of crop plants for improved agricultural productivity and sustainability. We discuss how the methods and metrics we use to quantify root systems can affect our ability to understand them,how we can bridge knowledge gaps and accelerate the derivation of structurefunction relationships for roots,and why a detailed mechanistic understanding of root growth and function will be important for future agricultural gains.

Morphological changes in gray matter volume correlate with catechol-O-methyl transferase gene Val158Met polymorphism in first-episode treatment-nave patients with schizophrenia

The catechol-O-methyltransferase（COMT） gene is a schizophrenia susceptibility gene. A common functional polymorphism of this gene,Val158/158 Met,has been proposed to influence gray matter volume（GMV）. However,the effects of this polymorphism on cortical thickness/surface area in schizophrenic patients are less clear. In this study,we explored the relationship between the Val158 Met polymorphism of the COMT gene and the GMV/ cortical thickness/cortical surface area in 150 firstepisode treatment-nave patients with schizophrenia and 100 healthy controls. Main effects of diagnosis were found for GMV in the cerebellum and the visual,medial temporal,parietal,and middle frontal cortex. Patients with schizophrenia showed reduced GMVs in these regions. And main effects of genotype were detected for GMV in the left superior frontal gyrus. Moreover,a diagnosis × genotype interaction was found for the GMV of the left precuneus,and the effect of the COMT gene on GMV was due mainly to cortical thickness rather than cortical surface area. In addition,a pattern ofincreased GMV in the precuneus with increasing Met dose found in healthy controls was lost in patients with schizophrenia. These findings suggest that the COMTMet variant is associated with the disruption of dopaminergic influence on gray matter in schizophrenia,and the effect of the COMT gene on GMV in schizophrenia is mainly due to changes in cortical thickness rather than in cortical surface area.