Imaging genetics

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Imaging genetics refers to the use of anatomical or physiological imaging technologies as phenotypic assays to evaluate genetic variation. Scientists that first used the term imaging genetics were interested in how genes influence psychopathology and used functional neuroimaging to investigate genes that are expressed in the brain (neuroimaging genetics).[1]

Imaging genetics uses research approach in which genetic information and fMRI data in the same subjects are combined to define neuro-mechanisms linked to genetic variation.[2] With the images and genetic information, it can be determined how individual differences in single nucleotide polymorphisms, or SNPs, lead to differences in brain wiring structure, and intellectual function.[3] Imaging genetics allows the direct observation of the link between genes and brain activity in which the overall idea is that common variants in SNPs lead to common diseases.[4] A neuroimaging phenotype is attractive because it is closer to the biology of genetic function than illnesses or cognitive phenotypes.[5]

The University of California, Irvine hosts an annual conference in January called the International Imaging Genetics Conference. This international symposium is held to assess the state of the art in the various established fields of genetics and imaging, and to facilitate the transdisciplinary fusion needed to optimize the development of the emerging field of Imaging Genetics. Upcoming conference details and the archives of past conferences can be found at www.imaginggenetics.uci.edu.

Imaging Genetics in Alzheimer's[edit]

By combing the outputs of the polygenic and neuro-imaging within a linear model, it has been shown that genetic information provides additive value in the task of predicting Alzheimer’s Disease (AD).[6] AD traditionally has been considered a disease marked by neuronal cell loss and widespread gray matter atrophy and the apolipoprotein E allele (APOE4) is a widely confirmed genetic risk factor for late-onset AD.[7]

Another gene risk variant is associated with Alzheimer’s, which is known as the CLU gene risk variant. The CLU gene risk variant showed a distinct profile of lower white matter integrity that may increase vulnerability to developing AD later in life.[8] Each CLU-C allele was associated with lower FA in frontal, temporal, parietal, occipital, and subcortical white matter.[9] Brain regions with lower FA included corticocortical pathways previously demonstrated to have lower FA in AD patients and APOE4 carriers.[10] CLU-C related variability found here might create a local vulnerability important for disease onset. (Braskie et al. 2011). These effects are remarkable as they already exist early in life and are associated with a risk gene that is very prevalent (~36% of Caucasians carry two copies of the risk conferring genetic variant CLU-C.) [11] Quantitative mapping of structural brain differences in those at genetic risk of AD is crucial for evaluating treatment and prevention strategies. If the risk for AD is identified, appropriate changes in lifestyle may limit the apprehension of AD; exercise and body mass index-have an effect on brain structure and the level of brain atrophy [12]

Biomarkers and Alzheimer's Spectrum[13][edit]

If suitable biomarkers are found and applied in clinical use, we will be able to give the diagnosis of the AD spectrum at an even earlier stage. In the proposal, the AD spectrum is divided into the three stages (i) the preclinical stage; (ii) mild cognitive impairment; and (iii) clinical AD, (Biomarkers, 2011). In the preclinical stage, only changes in a specific biomarker are observed with neither cognitive impairment nor clinical signs of AD. The mild cognitive impairment stage may include those showing biomarker changes as well as mild cognitive decline abut no clinical signs and symptoms of AD. AD is diagnosed in patients with biomarker changes and clinical signs and symptoms of AD. This concept will promote understanding of the continuous transition from preclinical stage to AD via mild cognitive impairment, in which biomarkers can be utilized to discriminate and clearly define each stage of the AD spectrum. The new criteria will promote earlier detection of the subjects who will develop AD in later life, and also to initiate intervention aiming for the prevention of AD.

Future of Imaging Genetics[edit]

Imaging genetics must develop methods that will allow relating the effects of a large number of genetic variants on equally multi-dimensional neuroimaging phenotypes.[14]

Problems with Imaging Genetics[edit]

Medication, hospitalization history, or associated behaviors ranging such as smoking can affect imaging.[15]

Notes[edit]

  1. ^ Hariri, A. R., Drabant, E.M. & Weinberger, D. R. (May 2006). "Imaging genetics: Perspectives from studies of genetically driven variation in serotonin function and corticolimbic affective processing". Biological Psychiatry 59 (10): 888–897. doi:10.1016/j.biopsych.2005.11.005. PMID 16442081. 
  2. ^ Hariri; Weinberger. "Imaging Genomics". British Medical Bulletin 65: 259–270. 
  3. ^ Thompson (2012). Imaging Genetics http://www.loni.ucla.edu/~thompson/IG/IG.html |url= missing title (help). 
  4. ^ Chi (2009). "Hit or Miss?". Nature 461 (8): 712–714. 
  5. ^ Meyer-Lindenberg (2012). "The Future of fMRI and Genetics Research". NeuroImage 62: 1286–1292. doi:10.1016/j.neuroimage.2011.10.063. 
  6. ^ Filipovych; Gaonkar, Davatzikos. "A Composite Multivariate Polygenic and Neuroimaging Score for Prediciton of Conversion to Alzheimer's Disease". Biomedical Imaging Analysis: 105–108. 
  7. ^ Braski; Jahanshad, Stein, Barysheva, Toga, Thompson (2011). "Common Alzheimer's Disease Risk Variant within the CLU Gene Affects White Matter Microstructure in Young Adults". The Journal of Neuroscience 31 (18): 6764–6770. doi:10.1523/jneurosci.5794-10.2011. 
  8. ^ Braski; Jahanshad, Stein, Barysheva, Toga, Thompson (2011). "Common Alzheimer's Disease Risk Variant within the CLU Gene Affects White Matter Microstructure in Young Adults". The Journal of Neuroscience 31 (18): 6764–6770. doi:10.1523/jneurosci.5794-10.2011. 
  9. ^ Braski; Jahanshad, Stein, Barysheva, Toga, Thompson (2011). "Common Alzheimer's Disease Risk Variant within the CLU Gene Affects White Matter Microstructure in Young Adults". The Journal of Neuroscience 31 (18): 6764–6770. doi:10.1523/jneurosci.5794-10.2011. 
  10. ^ Braski; Jahanshad, Stein, Barysheva, Toga, Thompson (2011). "Common Alzheimer's Disease Risk Variant within the CLU Gene Affects White Matter Microstructure in Young Adults". The Journal of Neuroscience 31 (18): 6764–6770. doi:10.1523/jneurosci.5794-10.2011. 
  11. ^ Braski; Jahanshad, Stein, Barysheva, Toga, Thompson (2011). "Common Alzheimer's Disease Risk Variant within the CLU Gene Affects White Matter Microstructure in Young Adults". The Journal of Neuroscience 31 (18): 6764–6770. doi:10.1523/jneurosci.5794-10.2011. 
  12. ^ Braski; Jahanshad, Stein, Barysheva, Toga, Thompson (2011). "Common Alzheimer's Disease Risk Variant within the CLU Gene Affects White Matter Microstructure in Young Adults". The Journal of Neuroscience 31 (18): 6764–6770. doi:10.1523/jneurosci.5794-10.2011. 
  13. ^ "Biomarkers and Alzheimer's Spectrum". Psychiatry and Clinical Neurosciences 65: 115–120. 
  14. ^ Bedenbender; Paulus, Krach, Pyka, Sommer (2011). "Functional Connectivity Analyses in Imaging Genetics: considerations on Methods and Data Interpretation". PLoS One 6 (12). 
  15. ^ Bedenbender; Paulus, Krach, Pyka, Sommer (2011). "Functional Connectivity Analyses in Imaging Genetics: considerations on Methods and Data Interpretation". PLoS One 6 (12).