Identifying the genetic basis and molecular mechanisms underlying phenotypic correlation between complex human traits using a gene-based approach

  1. Jialiang Gu
  2. Chris Fuller
  3. Peter Carbonetto
  4. Xin He
  5. Jiashun Zheng
  6. Hao Li

  • Curated by eLife

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    eLife Assessment

    This valuable study characterizes the emergence of the membrane-associated periodic cytoskeleton (MPS) in the axons of human motor neurons derived from induced pluripotent stem cells. Super-resolution imaging of beta-II spectrin provides convincing evidence for the patterned assembly of spectrin-poor gaps and spectrin-rich MPS in the medial region of the axons and its enhancement by the kinase inhibitor staurosporine. The data advocates against gap formation by cytoskeleton disassembly in a continuous MPS. Instead, a continuous MPS may result from nascent MPS patches and their maturation, a model that would benefit from live imaging for validation.

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Abstract

Phenotypic correlations between complex human traits have long been observed based on epidemiological studies. However, the genetic basis and underlying mechanisms are largely unknown. Here we developed a gene-based approach to measure genetic overlap between a pair of traits and to delineate the shared genes/pathways, through three steps: 1) translating SNP-phenotype association profile to gene-phenotype association profile by integrating GWAS with eQTL data using a newly developed algorithm called Sherlock-II; 2) measuring the genetic overlap between a pair of traits by a normalized distance and the associated p value between the two gene-phenotype association profiles; 3) delineating genes/pathways involved. Application of this approach to a set of GWAS data covering 59 human traits detected significant overlap between many known and unexpected pairs of traits; a significant fraction of them are not detectable by SNP based genetic similarity measures. Examples include Cancer and Alzheimer’s Disease (AD), Rheumatoid Arthritis and Crohn’s disease, and Longevity and Fasting glucose. Functional analysis revealed specific genes/pathways shared by these pairs. For example, Cancer and AD are co-associated with genes involved in hypoxia response and P53/apoptosis pathways, suggesting specific mechanisms underlying the inverse correlation between them. Our approach can detect yet unknown relationships between complex traits and generate mechanistic hypotheses and has the potential to improve diagnosis and treatment by transferring knowledge from one disease to another.

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  1. eLife

    eLife Assessment

    This valuable study characterizes the emergence of the membrane-associated periodic cytoskeleton (MPS) in the axons of human motor neurons derived from induced pluripotent stem cells. Super-resolution imaging of beta-II spectrin provides convincing evidence for the patterned assembly of spectrin-poor gaps and spectrin-rich MPS in the medial region of the axons and its enhancement by the kinase inhibitor staurosporine. The data advocates against gap formation by cytoskeleton disassembly in a continuous MPS. Instead, a continuous MPS may result from nascent MPS patches and their maturation, a model that would benefit from live imaging for validation.

  2. eLife

    Reviewer #1 (Public review):

    Summary:

    Ever since the surprising discovery of the membrane-associated Periodic Skeleton (MPS) in axons, a significant body of published work has been aimed at trying to understand its assembly mechanism and function. Despite this, we still lack a mechanistic understanding of how this amazing structure is assembled in neuronal cells. In this article, the authors report a "gap-and-patch" pattern of labelled spectrin in iPSC-derived human motor neurons grown in culture. The mid-sections of these axons exhibit patches with reasonably well-organized MPS that are separated by gaps lacking any detectable MPS and having low spectrin content. Further, they report that the intensity modulation of spectrin is correlated with intensity modulations of tubulin as well. However, neurofilament fluorescence does not show any correlation. Using DIC imaging, the authors show that often the axonal diameter remains uniform across segments, showing a patch-gap pattern. Gaps are seen more abundantly in the midsection of the axon, with the proximal section showing continuous MPS and the distal segment showing continuous spectrin fluorescence but no organized MPS. The authors show that spectrin degradation by caspase/calpain is not responsible for gap formation, and the patches are nascent MPS domains. The gap and patch pattern increases with days in culture and can be enhanced by treating the cells using the general kinase inhibitor staurosporine. Treatment with the actin depolymerizing agent Latrunculin A reduces gap formation. The reasons for the last two observations are not well understood/explained.

    Strengths:

    The claims made in the paper are supported by extensive imaging work and quantification of MPS. Overall, the paper is well written and the findings are interesting. Although much of the reported data are from axons treated with staurosporine, this may be a convenient system to investigate the dynamics of MPS assembly, which is still an open question.

    Weaknesses:

    Much of the analysis is on staurosporine-treated cells, and the effects of this treatment can be broad. The increase in patch-gap pattern with days in culture is intriguing, and the reason for this needs to be checked carefully. It would have been nice to have live cell data on the evolution of the patch and gap pattern using a GFP tag on spectrin. The evolution of individual patches and possible coalescence of patches can be observed even with confocal microscopy if live cell super-resolution observation is difficult.

    Some more comments:

    (1) Axons can undergo transient beading or regularly spaced varicosity formation during media change if changes in osmolarity or chemical composition occur. Such shape modulations can induce cytoskeletal modulations as well (the authors report modulations in microtubule fluorescence). The authors mention axonal enlargements in some instances. Although they present DIC images to argue that the axons showing gaps are often tubular, possible beading artefacts need to be checked. Beading can be transient and can be checked by doing media changes while observing the axons on a microscope.

    (2) Why do microtubules appear patchy? One would imagine the microtubule lengths to be greater than the patch size and hence to be more uniform.

    (3) Why do axons with gaps increase with days in culture? If patches are nascent MPS that progressively grow, one would have expected fewer gaps with increasing days in culture. Is this indicative of some sort of degeneration of axons?

    (4) It is surprising that Latrunculin A reduces gap formation induced by staurosporine (also seems to increase MPS correlation) while it decreases actin filament content. How can this be understood? If the idea is to block actin dynamics, have the authors tried using Jasplakinolide to stabilize the filaments?

    (5) The authors speculate that the patches are formed by the condensation of free spectrins, which then leaves the immediate neighborhood depleted of these proteins. This is an interesting hypothesis, and exploring this in live cells using spectrin-GFP constructs will greatly strengthen the article. Will the patch-gap regions evolve into continuous MPS? If so, do these patches expand with time as new spectrin and actin are recruited and merge with neighboring patches, or can the entire patch "diffuse" and coalesce with neighboring patches, thus expanding the MPS region?

  3. eLife

    Reviewer #2 (Public review):

    Summary:

    In this manuscript, Gazal et al. describe the presence of unique gaps and patches of BetaII-spectrin in medial sections of long human motor neuron axons. BII-spectrin, along with Alpha-spectrin, forms horizontal linkers between 180nm spaced F-actin rings in axons. These F-actin rings, along with the spectrin linkers, form membrane periodic structures (MPS) which are critical for the maintenance of the integrity, size, and function of axons. The primary goal of the authors was to address whether long motor axons, particularly those carrying familial mutations associated with the neurodegenerative disorder ALS, show defects in gaps and patches of BetaII-spectrin, ultimately leading to degradation of these neurons.

    Strengths:

    The experiments are well-designed, and the authors have used the right methods and cutting-edge techniques to address the questions in this manuscript. The use of human motor neurons and the use of motor neurons with different familial ALS mutations is a strength. The use of isogenic controls is a positive. The induction of gaps and patches by the kinase inhibitor staurosporine and their rescue by Latrunculin A is novel and well-executed. The use of biochemical assays to explore the role of calpains is appropriate and well-designed. The use of STED imaging to define the periodicity of MPS in the gaps and patches of spectrin is a strength.

    Weaknesses:

    The primary weakness is the lack of rigorous evaluation to validate the proposed model of spectrin capture from the gaps into adjacent patches by the use of photobleaching and live imaging. Another point is the lack of investigation into how gaps and patches change in axons carrying the familial ALS mutations as they age, since 2 weeks is not a time point when neurodegeneration is expected to start.

  4. eLife

    Author response:

    Reviewer #1 (Public review):

    The authors tried to quantify the difference between human complex traits by calculating genetic overlap scores between a pair of traits. Sherlock-II was devised to integrate GWAS with eQTL signals. The authors claim that Sherlock-II is superior to the previous version (robustness, accuracy, etc). It appears that their framework provides a reasonable solution to this important question, although the study needs further clarification and improvements.

    (1) Sherlock-II incorporates GWAS and eQTL signals to better quantify genetic signals for a given complex trait. However, this approach is based on the hypothesis that "all GWAS signals confer association to complex trait via eQTL", which is not true (PMID: 37857933). This should be acknowledged (through mentioning in the text) and incorporated into the current setup (through differential analysis - for example, with or without eQTL signals, or with strong colocalization only).

    The reviewer is correct that in this version of the tool, we focused on SNPs with effect on gene expression, as the majority of the SNPs identified by GWASs are non-coding SNPs. In the future improvement, we should also include coding SNPs that change the amino acid sequence of genes. We will discuss this point more in the revised manuscript.

    (2) When incorporating eQTL, why did the authors use the top p-value tissues for eQTL? This approach seems simpler and probably more robust. But many eQTLs are tissue-specific. Therefore, it would also be important to know if eQTLS from appropriate tissues were incorporated instead.

    This is a simple scheme to incorporate eQTL data from multiple tissues, assuming that the tissue that gives the strongest association is most relevant, or mainly mediates the effect from the SNP to the phenotype. This is a reasonable approach given that the tissues of origin for most of the phenotypes are unknown. In the future improvement, we should incorporate eQTL data from the appropriate tissue(s) if that is known.

    (3) One of the main examples is the novel association between Alzheimer's disease and breast cancer. Although the authors provided a molecular clue underlying the association, it is still hard to comprehend the association easily, as the two diseases are generally known to be exclusive to each other. This is probably because breast cancer GWAS is performed for germline variants and does not consider the contribution of somatic variants.

    This is due to one of the limitations of the current algorithm: no direction of association is predicted explicitly. It could be that increasing the expression of a gene reduced the risk of one disease but increase the risk of another. Currently we have to analyze the details of the SNPs to infer direction once overlapping genes are found. This needs improvement in the future.

    (4) It would help readers understand the story better if a summary figure of the entire process were provided. The current Figure 1 does not fulfil that role.

    We plan to incorporate reviewer's suggestion in the revised manuscript.

    (5) Figure 2 is not very informative. The readers would want to know more quantitative information rather than a heatmap-style display. Is there directionality to the relationship, or is it always unidirectional?

    We will consider a different presentation in the revised manuscript.

    (6) In Figure 3, readers may want to know more specific information. For example, what gene signals are really driving the hypoxia signal in Alzheimer's disease vs breast cancer? And what SNP signals are driving these gene-level signals?

    We will add these information in the revised manuscript.

    Reviewer #2 (Public review):

    Summary:

    The authors introduce a gene-level framework to detect shared genetic architecture between complex traits by integrating GWAS summary statistics with eQTL data via a new algorithm, Sherlock-II, which aggregates signals from multiple (cis/trans) eSNPs to produce gene-phenotype p-values. Shared pathways are identified with Partial-Pearson-Correlation Analysis (PPCA).

    Strengths:

    The authors show the gene-based approach is complementary and often more sensitive than SNP-level methods, and discuss limitations (in terms of no directionality, dependence on eQTL coverage).

    Weaknesses:

    (1) How do the authors explain data where missing tissues or sparse eQTL mapping are available? Would that bias as to which genes/traits can be linked and may produce false negatives or tissue-specific false positives?

    Missing tissues or sparse eQTL certainly can produce false negatives as the signals linking the two phenotypes are simply not captured in the data. It is less likely to produce false positives as long as the statistical test is well controlled.

    (2) Aggregating SNP-level signals into gene scores can be confounded by LD; for example, a nearby causal variant for a different gene or non-expression mechanism may drive a gene's score, producing spurious gene-trait links. How do the authors prevent this?

    When there are multiple SNPs in LD with multiple genes nearby, it is generally difficult to map the causal SNP and the causal gene it affected, and thus there will be spurious gene-trait links. When we calculate the global similarity based on the gene-trait association profiles, we tried to control this by simulating with random GWASs that have the same power as the real GWAS and preserve the LD structure, as the spurious links will also be present in the simulated data (but may appear in different loci) that are used to calibrate the statistical significance.

    (3) How the SNPs are assigned to genes would affect results, this is because different choices can change which genes appear shared between traits. The authors can expand on these.

    We assign SNPs to genes based on their strongest eQTL association from the available data. Improvement can be made if the relevant tissues for a trait are known (see response to Reviewer 1 above).

    (4) Many reported novel trait links remain speculative without functional or orthogonal validation (e.g., colocalization, perturbation data). Thus, the manuscript's claims are inconclusive and speculative.

    We agree with the reviewer that the reported trait links are speculative, and they should be treated as hypotheses generated from the computational analyses. To truly validate some of these proposed relationships, deeper functional analyses and experimental tests are needed.

    (5) It would be best to run LD-aware colocalization and power-matched simulations to check for robustness.

    We agree more control on LD and power-matched simulations will be important for testing the robustness of the predictions.

  5. Version published to 10.1101/2021.02.09.430368 on bioRxiv