Week 1: LD Score regression distinguishes confounding from polygenicity in genome-wide association studies

https://www.nature.com/articles/ng.3211

Abstract:

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

Week 2: Disease Networks. Uncovering Disease-Disease Relationships Through the Incomplete Interactome

https://pubmed.ncbi.nlm.nih.gov/25700523/

Abstract:

According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes.

Week 3: Advances in microstructural diffusion neuroimaging for psychiatric disorders

https://www.sciencedirect.com/science/article/abs/pii/S1053811918303616

Abstract:

Understanding the neuropathological underpinnings of mental disorders such as schizophrenia, major depression, and bipolar disorder is an essential step towards the development of targeted treatments. Diffusion MRI studies utilizing the diffusion tensor imaging (DTI) model have been extremely successful to date in identifying microstructural brain abnormalities in individuals suffering from mental illness, especially in regions of white matter, although identified abnormalities have been biologically non-specific. Building on DTI's success, in recent years more advanced diffusion MRI methods have been developed and applied to the study of psychiatric populations, with the aim of offering increased sensitivity to subtle neurological abnormalities, as well as improved specificity to candidate pathologies such as demyelination and neuroinflammation. These advanced methods, however, usually come at the cost of prolonged imaging sequences or reduced signal to noise, and they are more difficult to evaluate compared with the more simplified approach taken by the now common DTI model. To date, a limited number of advanced diffusion MRI methods have been employed to study schizophrenia, major depression and bipolar disorder populations. In this review we survey these studies, compare findings across diverse methods, discuss the main benefits and limitations of the different methods, and assess the extent to which the application of more advanced diffusion imaging approaches has led to novel and transformative information with regards to our ability to better understand the etiology and pathology of mental disorders.

Week 4: Conditional and Joint multiple-SNP Analysis of GWAS Summary Statistics Identifies Additional Variants Influencing Complex Traits

https://pubmed.ncbi.nlm.nih.gov/22426310/

Abstract:

We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.

Week 5: Mapping cis-regulatory chromatin contacts in neural cells links neuropsychiatric disorder risk variants to target genes

https://www.nature.com/articles/s41588-019-0472-1

Abstract:

Mutations in gene regulatory elements have been associated with a wide range of complex neuropsychiatric disorders. However, due to their cell-type specificity and difficulties in characterizing their regulatory targets, the ability to identify causal genetic variants has remained limited. To address these constraints, we perform an integrative analysis of chromatin interactions, open chromatin regions and transcriptomes using promoter capture Hi-C, assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and RNA sequencing, respectively, in four functionally distinct neural cell types: induced pluripotent stem cell (iPSC)-induced excitatory neurons and lower motor neurons, iPSC-derived hippocampal dentate gyrus-like neurons and primary astrocytes. We identify hundreds of thousands of long-range cis-interactions between promoters and distal promoter-interacting regions, enabling us to link regulatory elements to their target genes and reveal putative processes that are dysregulated in disease. Finally, we validate several promoter-interacting regions by using clustered regularly interspaced short palindromic repeats (CRISPR) techniques in human excitatory neurons, demonstrating that CDK5RAP3, STRAP and DRD2 are transcriptionally regulated by physically linked enhancers.

Week 6: Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

https://pubmed.ncbi.nlm.nih.gov/26430803/

Abstract:

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

July 10th

Carolina OHBM conference recap

July 21st 2020: xgboost

• Example paper that use xgboost:

"Common brain disorders are associated with heritable patterns of apparent aging of the brain" https://www.nature.com/articles/s41593-019-0471-7

• A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE

https://www.frontiersin.org/articles/10.3389/fnagi.2018.00317/full

Presentation by Hao

Aug 4th 2020: Genetic Atlases of Human Cortex

Genetic topography of brain morphology

https://www.pnas.org/content/110/42/17089

Abstract:

Animal data show that cortical development is initially patterned by genetic gradients largely along three orthogonal axes. We previously reported differences in genetic influences on cortical surface area along an anterior-posterior axis using neuroimaging data of adult human twins. Here, we demonstrate differences in genetic influences on cortical thickness along a dorsal-ventral axis in the same cohort. The phenomenon of orthogonal gradations in cortical organization evident in different structural and functional properties may originate from genetic gradients. Another emerging theme of cortical patterning is that patterns of genetic influences recapitulate the spatial topography of the cortex within hemispheres. The genetic patterning of both cortical thickness and surface area corresponds to cortical functional specializations. Intriguingly, in contrast to broad similarities in genetic patterning, two sets of analyses distinguish cortical thickness and surface area genetically. First, genetic contributions to cortical thickness and surface area are largely distinct; there is very little genetic correlation (i.e., shared genetic influences) between them. Second, organizing principles among genetically defined regions differ between thickness and surface area. Examining the structure of the genetic similarity matrix among clusters revealed that, whereas surface area clusters showed great genetic proximity with clusters from the same lobe, thickness clusters appear to have close genetic relatedness with clusters that have similar maturational timing. The discrepancies are in line with evidence that the two traits follow different mechanisms in neurodevelopment. Our findings highlight the complexity of genetic influences on cortical morphology and provide a glimpse into emerging principles of genetic organization of the cortex.

Hierarchical genetic organization of human cortical surface area

https://pubmed.ncbi.nlm.nih.gov/22461613/

Abstract:

Surface area of the cerebral cortex is a highly heritable trait, yet little is known about genetic influences on regional cortical differentiation in humans. Using a data-driven, fuzzy clustering technique with magnetic resonance imaging data from 406 twins, we parceled cortical surface area into genetic subdivisions, creating a human brain atlas based solely on genetically informative data. Boundaries of the genetic divisions corresponded largely to meaningful structural and functional regions; however, the divisions represented previously undescribed phenotypes different from conventional (non-genetically based) parcellation systems. The genetic organization of cortical area was hierarchical, modular, and predominantly bilaterally symmetric across hemispheres. We also found that the results were consistent with human-specific regions being subdivisions of previously described, genetically based lobar regionalization patterns.

Presentation by Chi Hua

Aug 11th 2020: High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell

https://www.nature.com/articles/s41587-019-0290-0

Abstract:

Single-cell RNA sequencing can reveal the transcriptional state of cells, yet provides little insight into the upstream regulatory landscape associated with open or accessible chromatin regions. Joint profiling of accessible chromatin and RNA within the same cells would permit direct matching of transcriptional regulation to its outputs. Here, we describe droplet-based single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq), a method that can link a cell’s transcriptome with its accessible chromatin for sequencing at scale. Specifically, accessible sites are captured by Tn5 transposase in permeabilized nuclei to permit, within many droplets in parallel, DNA barcode tagging together with the mRNA molecules from the same cells. To demonstrate the utility of SNARE-seq, we generated joint profiles of 5,081 and 10,309 cells from neonatal and adult mouse cerebral cortices, respectively. We reconstructed the transcriptome and epigenetic landscapes of major and rare cell types, uncovered lineage-specific accessible sites, especially for low-abundance cells, and connected the dynamics of promoter accessibility with transcription level during neurogenesis.

Presentation by Jenny Weixiu Dong

Aug 19th 2020: Functional mapping and annotation of genetic associations with FUMA

https://www.nature.com/articles/s41467-017-01261-5.pdf

Abstract:

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

Presentation by Chi Hua Chen

Aug 21st 2020: Evo-Devo Genomics: What makes us human?

Not presenting on any specific paper in particular.

I will be presenting the evo-devo theory in the context of human brain development and evolution. I won't be focusing on one single paper but if you want to do a bit of reading beforehand, you can check out Sean Carroll's seminal paper on evo-devo here: https://www.cell.com/fulltext/S0092-8674(08)00817-9. Or you could just watch this youtube video by McGill alumnus and friend of mine: https://www.youtube.com/watch?v=ydqReeTV_vk&feature=emb_logo

Presentation by Carolina Makowski

July 26th 2021: Interactome project explained (July)

Presentation by Chi Hua Chen