Networks of gene expression and brain function

Transcription

1 Networks of gene expression and brain function OHBM 25 June 2017 VILAS MENON HHMI JANELIA RESEARCH CAMPUS

2 Introduction to Imaging Genetics A brief history of imaging genetics JB Poline The modern day endophenotype Roberto Toro Networks of gene expression & brain function Vilas Menon Utilizing big datasets in imaging genetics Derrek Hibar Rare variant associations David Glahn Imaging Epigenetics Sylvane Desrivieres After the association Jason Stein

3 Outline Gene expression overview Relating gene expression to functional imaging networks Richiardi, Altmann et al. (2015) & Hawrylycz et al. (2016) Linking functional connectomes to gene co-expression Wang et al. (2015) Linking gene expression to the default mode network Summary and Interpretations

4 Gene expression overview Quantification of messenger RNA in a biological sample Genome Dictionary Genes Words Gene expression profile Story

5 Relating imaging and gene expression data Samples/Regions Imaging-based parcellation Genes Map gene expression samples to imaging-based coordinates Region Region Gene expression Region Imaging metric Gene expression Correlate quantified gene expression and imaging-derived metric Region Region

6 Relating gene expression to brain region inter-connectivity

7 Gene expression and imaging data sets Internally generated functional Imaging data set Gene expression: Allen Human Brain Atlas (brain-map.org) 6 brains, ~500 tissue samples/brain Human Connectome Project (hummanconnectome.org)

8 Aligning functional imaging and gene expression data Richiardi, Altmann, et al. (2015)

9 Identifying genes correlated with connectivity Strength fraction = Sum of gene expression correlations of samples within a functional network Sum of gene expression correlations of samples not in a functional network Generate consensus list of genes most strongly contributing to significant strength fraction Richiardi, Altmann, et. al (2015)

10 Examining consensus genes in other contexts Validation on IMAGEN data set, showing connectivity differences between 20 individuals with highest and lowest genetic score based on consensus list of genes Examination of mouse structural connection weights (Allen Mouse Connectivity Atlas) and gene expression using consensus list of gens Richiardi, Altmann, et al. (2015)

11 Examining genes with conserved expression across individuals Differentially stable genes segregate into network modules/clusters with region- and cell type-specific patterns PCDH8 shows stable differential expression pattern across 6 brains Hawrylycz et al. (2016)

12 Conserved genes tend to be more related to functional connectivity Hawrylycz et al. (2016)

13 Relating gene expression to the default mode network falff = fractional amplitude of low-frequency ( Hz) fluctuations Wang et al. (2015)

14 How is gene expression correlated with the DMN? Significant genes are enriched in neurons Wang et al. (2015) Genes downregulated in ASD (Voineagu et al. 2011) are also represented in this set

15 Main findings Significant correlations exist between gene expression and functional connectivity These genes tend to be ion channel/brain specific, and are often associated with disease Variation in these genes is associated with changes in functional connectivity Genes with conserved expression patterns across individuals tend to be correlated more strongly to connectivity Possible interpretations: 1. Gene products are responsible for establishing and maintaining functional networks 2. Network mechanisms may drive expression of these genes 3. Neurons participating in networks may express these genes

16 What have we learned? The field is wide open initial conclusions are very broad On the horizon: Re-analyzing relationships using new image-based parcellations Incorporating higher resolution measurements (single-cell gene expression, better imaging, etc.) Addressing individual and network/task-level variability Linking gene expression to brain endophenotypes Extending analysis to subcortical regions