Showing posts with label SNP. Show all posts
Showing posts with label SNP. Show all posts

Friday, August 10, 2018

Does Intimate Knowledge of Your Personal Genome - Really Help?





As an offshoot of my previous post - I think this is an obvious question.  I am speaking from a medical context and not from the standpoint of genealogy.  DNA studies of human origins that I posted here in the past are also a valuable use of genomic material.  The latter are common reasons why people send their DNA off for analysis.  Another reason is to learn if they have certain disease susceptibilities and that is where the real problems come in.

The genetics of many major diseases causing significant mortality and morbidity were worked out before the genomic era. They are the typical heritable disorders and inborn errors of metabolism that are flagged in popular sites like 23 & Me.  I can't imagine that there are many surprises when subscribers find out that they do not have a fatal error of metabolism as an adult. Most of the concern is about complex polygenic disorders that may or may not have a significant environmental factor and that also lead to significant morbidity and mortality.  From a philosophical standpoint this is also an interesting group of illnesses because there are clear parallels between psychiatric disorders and what are typically considered usual medical problems like hypertension, coronary artery disease, asthma, and diabetes mellitus.  For the past decade, the standard genomic approach to study these disorders has been to scan large groups of genomes looking for mutations associated with these disorders.  Those analyses are complex.

I thought I would continue with some clear cut examples form my own genome to illustrate the polygenes behind both heritability of complex disorders as well as the polygenes behind my current chronic conditions.

The at risk condition is bipolar disorder.  My mother had severe bipolar 1 disorder.  That is why nobody in my family ever doubts that bipolar disorder or "diagnoses" exist in psychiatry.  Bipolar disorder is not a subtle condition and it is currently fairly easy to diagnosis.  I credit DSM technology with making this an easy to recognize diagnosis. That was not the case 2 generations ago.  The treatment in those days was less clear and there were very few resources to treat people in their communities.  Despite my mother's diagnosis none of my siblings or their offspring has been diagnosed with bipolar disorder or any mood disorder.  One of the inquiries for my genome is whether or not there are any polygenes associated with a bipolar disorder diagnosis.

The methodology I used for this post was to export all of my 23&Me data to Promethease, a search and cataloging software that arranges specific SNPs by disease, medication, genes, and several additional classification parameters.  Before this software was available - I was stuck looking up every rsID in PubMed. The processing time of my entire genome in Promethease was 133 seconds.  All of the correlates posted below are from that data.  The first graphic is for bipolar disorder risk (click to enlarge).



Ten SNPs associated with bipolar disorder were identified.  The risk is modest 1.39-2x. A more interesting feature is the facts that some of the identified SNPs were protective against bipolar disorder.  In some cases, the SNP identified had to be in association with another gene in order to create the risk. Ethnic groups are also noted in association with some of these SNPs to increase and decrease risk.  Standard approaches to this in the literature are to construct equations with risk terms from identified SNPs to determine which of those equations is the best predictor of risk.  A second approach that I will discuss in a subsequent post is to use neural networks to determine associations between the SNPs and quantitative estimates to determine risk.  At a macro level,  the lesson is that a person with a non-bipolar disorder phenotype can carry multiple SNPs that may confer risk for bipolar disorder.

What about actual disease phenotypes?  I am fortunate enough to have several for analysis.  The first and in many cases the most illustrative is asthma.  I have had asthma since childhood with various diagnoses along the way.  The first diagnosis was a misdiagnosis and that it was a psychosomatic condition and not really asthma.  Then it was diagnosed as allergic asthma.  Then it was exercise induced asthma.  I have received just about every conceivable treatment for asthma including some that have been determined to not be effective.  There was also the famous disproven mechanism of action (increased intracellular cAMP) that was used to explain the mechanism of action for theophylline. I had a long quiescent period of about 20 years where I did not require any medical treatment at all.  That ended about 6 years ago when I developed an upper respiratory infection and I have had to take medications ever since.  My experience with available treatments has generally been disappointing.  The variable course of the illness seems to have a more significant effect.  That is probably why most treated asthmatics are symptomatic and the clinical markers of illness are mostly subjective.  I have several posts on asthma on this blog discussing why it is an ideal comparison disease to polygenic psychiatry disorders.  What does my genome say? (click to enlarge)



I have one sibling and one offspring of a sibling with asthma.  In this case the situation is more complicated - 47 SNPs with varying risk and qualifiers based on numerous contexts such as ethnicity, smoking status of the parents, exposure to allergens, medication responsiveness  and others. I highlighted a couple of SNPs that show a very high risk compared to what was seen in the bipolar disorder - specifically an odds ratio of 7.84 and a 3-fold to 39-fold increase in risk.  Like the bipolar disorder case - some of the polygenes decrease risk as well. There is no available level of data integration beyond that and no clear guidance in terms of therapy.

UpToDate  has a brief chapter (1 ) on the genetics of asthma.  The authors point out that it is a complex polygenic illness that in some cases depends on environmental interactions.  Like pre-genomic twin and family studies in psychiatric disorders there is a range of heritability.  The authors recommend genetic testing in patients with asthma only to exclude monogenic obstructive lung diseases that can be misdiagnosed as asthma, such as cystic fibrosis, primary ciliary dyskinesia, and alpha-1 antitrypsin deficiency.  They see other genetic testing as useful at the heuristic but not at a clinical level.   They point out that the study of asthma genetics is complicated by the lack of a gold standard test and the uneven application of clinical diagnostic criteria.  That has led to a study of a number of asthma traits.

From a pharmacogenomics standpoint up to half of asthma patients do not respond well to initial pharmacotherapy and even then the response is quite variable.  They review the strategies used for genetic analyses, including SNPs as mentioned here but do not comment on any specific SNPs.  I had a previous post on genes and GWAS studies of asthma. They do name several genes.  After reading this chapter it is clear that the parallels between asthma and major psychiatric disorders is clearer than ever.  All of the features of complex psychiatric illness including polygenic inheritance, complex heritability, lack of a gold standard medical test, and a lack of or incomplete response to medication that also occurs with severe psychiatric disorders.

 The final chronic condition is atrial fibrillation.  I had an onset about 8 years ago and as long as I take flecainide - I have no atrial fibrillation.  I had one grandparent with atrial fibrillation.  The SNPs identified follow and it is similar to asthma except fewer identified SNPs.  Multiple SNPs with associated conditions (embolic or ischemic stroke) and qualifiers.


Fifteen SNPs noted in my genome for atrial fibrillation.  Some of these genes have bare bones information (GWAS = genome wide association study, OMIM = online Mendelian Inheritance in Man).  There are complementary approaches that involve using other databases like the GWAS database.  Searching atrial fibrillation in that database. identifies a number of genotypes that were not found in my genome (rs247617, rs2129977, rs2220427, and rs6843082) and one that was - rs6843082.

Clearly, the approach I have outlined about is an improvement over searching Medline and my genome for SNP correlates but below any threshold for being able to use this information for precision medicine.  That means it is below any standard required to look at diagnosis, prevention, or treatment.

There is probably a lot of information even at this level that is sitting there under analyzed.  From my own genome, the involvement of the cytokine system (interleukins are cytokines) with multiple SNPs affecting those genes is a case in point.  Many asthmatics have multiple allergic conditions including atopy, eczema, urticaria, and episodic anaphylaxis.  These same individuals will see allergists, get tested and learn that they are allergic to everything. Those associated conditions are currently treated as medical mysteries or symptomatically as they flare up occasionally.  Are there deeper patterns in the immune system that have not been realized at this time?  Give the complexity of this system, I think that there are.

One of the key questions is whether the identified genes are producing identifiable products.  At that level the short answer is that current detailed genomic information is interesting from an academic perspective - but like genomic testing we are years away from clinical applications.  I could see the shadows of some serious family illnesses in my DNA like systemic lupus erythematosus and diabetes mellitus.  The reasons why my relatives developed these diseases and I did not is not clear at this time. I think most people might come to that same conclusion if they compare their personal genome with SNP markers of diseases.

With a few exceptions, it takes more than correlating mutations in your own DNA to what is known about those mutations across a much larger population and coming up with a diagnosis.  It probably takes more than knowing the mutations exist.  Multiple omics approaches might provide better information and I hope to post one one of those experiments soon and the result of that experiment in the case of selective serotonin reuptake inhibitor (SSRI) antidepressants will be shocking.


George Dawson, MD, DFAPA   



References:

1:  Author: Barnes KC.  Section Editors: Barnes, PJ; Raby BA, Deputy Editor: Hollingsworth H.  Genetics of asthma.  In: UpToDate  Accessed on August 14, 2018.





Friday, February 10, 2017

Cannabis and Causation





Cannabis use is highly politicized in the US at this time largely due to legalization rhetoric that has spilled over into scientific research on the topic.  Despite the broad movement to legalize cannabis across the US, only a minority of the population are regular cannabis users.  More widespread use will undoubtedly lead to increased problems associated with wider exposure, especially wider exposure in populations with vulnerabilities to the toxic effects of cannabis.  The toxic effects of interest include addiction and psychosis.  It is common in clinical practice to encounter daily cannabis smokers who stopped using the substance after several years because they started to get panic attacks, paranoia, or both.  The people I see have all moved on to something else, but there are also a substantial number of chronic smokers who are addicted.  That number is about 9% of users, and that is comparable to the amount of people who have problems from drinking alcohol.  Inpatient psychiatrists commonly see people with florid psychotic episodes from smoking significant quantities of cannabis.  They also see repeat admissions from people who are either detoxified or treated for these psychotic episodes, are discharged and smoke more cannabis to the point of a repeat psychotic episode.  The longstanding controversy among people who are not doing the work and just speculating is whether any good observational studies can be done to show that cannabis does cause psychosis or if this is an artifact of observational methodology.  In other words,  could a reverse causality bias exist that makes people who are prone to schizophrenia or psychotic episodes more likely to smoke cannabis.  In my opinion, there have been excellent observational studies showing the association between cannabis use and psychosis, but as long as that is the technology these studies will always contain the old association is not causation qualifier.

A recent paper (1) in Molecular Psychiatry may have just illustrated the causation that psychiatrists have been experiencing firsthand for decades.  The authors use a novel genetic appraoch to look at the issue of causation.  The main assumption of this study is that using specific genotypes as the independent variable rather than observed individuals gets rid of the confounding demographic and environmental variables that could be casual.  They point out that any actual clinical trial looking at the issue of whether cannabis causes psychosis would be unethical, but that a model that looks at whether causation can be established by looking at single nucleotide polymorphisms (SNPs) from a Genome Wide Association Study (GWAS) looking at the any cannabis use phenotype.  They looked at the top 10 SNPs from that data that were used to calculate gene-exposure (SNP-cannabis) estimates.  SNP-risk of schizophrenia exposure estimates were calculated from available data from the Psychiatric Genomics Consortium.  Instrumental variable estimates were made by dividing the risk of schizophrenia/risk of any cannabis use.  The instrument variable analyses were pooled across SNPs and analyzed with fixed effect meta-analysis.  The authors provide a detailed discussion and rationale for their statistical calculation in the full text of the article and supplementary material.  At this point I am going to post their main graphics.  Click on any graphic to enlarge it.

The first graphic looks at prospective observational studies.  The authors were interested in determining whether their genetically based analysis was in the same direction of this meta-analysis. Ever use cannabis use was associated with a 43% increase in schizophrenia or psychosis.

Figure 1. Meta-analysis of prospective observational studies reporting an association between use of cannabis and risk of schizophrenia or related disorders. Meta-analysis uses a random-effects model. Studies are sorted by type of outcome (schizophrenia only vs schizophrenia and related outcomes). Odds ratios (ORs) and 95% confidence intervals (CIs) express the risk of schizophrenia or psychotic symptoms for ever use of cannabis (compared with never use). For additional information on each study, see Supplementary Table S1. Dunedin, Dunedin Multidisciplinary Health & Development Study; ECA, Epidemiologic Catchment Area; EDSP, Early Developmental Stages of Psychopathology Study; NEMESIS, Netherlands Mental Health Survey and Incidence Study; SC, Swedish Cohort.



Figure 2 looks at the Mendelian Randomization analysis of 34,241 cases of schizophrenia and 45,604 cases of ever use cannabis.  This shows a 37% risk of cannabis users versus non-users for schizophrenia/psychosis risk.  The authors did a sensitivity analysis of this same data by removing each SNP from the analysis to calculate a summary causal effect of 1.33 across all 10 SNPs or 1.88 when restricted to 2 functional SNPs.





Figure 4 is included here to illustrate the authors' sensitivity analysis showing a summary casual effect of about 1.37 (red line).



All things considered this may be a compelling story for causation.  I qualify that of course in a couple of domains.  First. there are a lot of statistical models and calculations operating here.  In my experience mapping complex statistical estimates onto the most complex object in the universe has not worked out very well.  My first hand experience was statistical modeling of quantitative EEG and claims that is was predictive of psychiatric diagnosis.  Those compelling calculations published in Science (4) did not pan out at all in the long run.  It will be interesting to see if the authors applications are more widely applied to other SNPs to determine disease causation from other risk factors.  The second potential problem is a slight variation on that theme and that is the overall imprecision of meta-analysis.  The known  approximate prediction/concordance rates of meta-analyses for clinical trials (2.3) suggests that it may not be good predictor of a reproducible result.  The authors themselves suggest that the potential limitations of their study start with the fact that none of the chosen SNPs met conventional genome wide significance thresholds.  The specific dose effect of cannabis could not be investigated in the study.  The age at exposure is may be a developmental variable of interest and that was unknown.  The Mendelian Randomization techniques may have not been powerful enough to detect pleiotropic (one gene affecting more than one trait) effects, but they discuss how an alternate analysis applies in this situation.

The other question I had was about epigenetic effects on this model.  The authors were certainly aware of smoking as a confounding variable.  The known epigenetic effects of nicotine on brain chromatin would seem to cloud SNPs as pure genetic risk factors.  But this is nonetheless one of the more interesting models and concepts I have seen in a while.

They conclude that their study is "the closest approximation to a randomized trial on the effect of ever use of cannabis and risk of schizophrenia" when such a clinical trial is unethical.   That is an interesting take on their method and causation.  Hopefully it will open up the way for other studies of causation using these techniques.  If that is the case, it is a good idea to study this paper and the supplementary material (26 pages) and have a good idea about its difference from observational/association studies.  The supplementary material is also very useful for the calculations used in the study, a Venn diagram of the overlap between the schizophrenia-GWAS group (N=79,845) and the ever-use cannabis GWAS group (N=37,957), and their review methods of the best observational studies of cannabis use and  schizophrenia/psychosis.  



George Dawson, MD, DFAPA



References:

1: Vaucher J, Keating BJ, Lasserre AM, Gan W, Lyall DM, Ward J, Smith DJ, Pell JP, Sattar N, Paré G, Holmes MV. Cannabis use and risk of schizophrenia: a Mendelian randomization study. Mol Psychiatry. 2017 Jan 24. doi: 10.1038/mp.2016.252. [Epub ahead of print] PubMed PMID: 28115737.

2: LeLorier J, Grégoire G, Benhaddad A, Lapierre J, Derderian F. Discrepancies between meta-analyses and subsequent large randomized, controlled trials. N Engl J Med. 1997 Aug 21;337(8):536-42. PubMed PMID: 9262498.

3: Ioannidis JPA, Cappelleri JC, Lau J. Issues in Comparisons Between Meta-analyses and Large Trials. JAMA. 1998;279(14):1089-1093. doi:10.1001/jama.279.14.1089

4:  John ER, Prichep LS, Fridman J, Easton P. Neurometrics: computer-assisted differential diagnosis of brain dysfunctions. Science. 1988 Jan 8;239(4836):162-9. PubMed PMID: 3336779.

"The standard for psychiatric diagnosis and categorization in the United States and Canada is now DSM-III and soon will be DSMIIIR. The categories defined therein have often been criticized as nothing more than a compilation of symptoms. The results obtained with neurometrics have shown that at least the categories studied are much more than arbitrary groupings of symptoms. ............. Validity-the great deficiency of psychiatric nosology - is beginning to emerge and, thus far, to reveal an impressive concordance with biology." p. 169

5: Smith GD, Ebrahim S. Mendelian Randomization: Genetic Variants as Instruments for Strengthening Causal Inference in Observational Studies. In: National Research Council (US) Committee on Advances in Collecting and Utilizing Biological Indicators and Genetic Information in Social Science Surveys; Weinstein M, Vaupel JW, Wachter KW, editors. Biosocial Surveys. Washington (DC): National Academies Press (US); 2008. 16. Available from: https://www.ncbi.nlm.nih.gov/books/NBK62433/

Selected References on Mendelian Randomization



Attributions:  All graphics except my home-made one at the top are from reference 1 per a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.




Supplementary 1:

With today's publicly available genetic technology it is possible for a person to search their own DNA for the SNPs found in this study.  When I do that using a database where my DNA analysis resides I found the following SNPs from this study from chromosomes 15, 4, and 12 respectively.  I have linked them  to the dbSNP database at NLM:

rs4984460

rs7675351

rs2099149

It is interesting to speculate on what it means to have 3/10 genetic markers for schizophrenia/psychosis susceptibility if any cannabis exposure.


Supplementary 2:  Click on my homemade graphic to see how beautiful it is.  Blogger does not do it justice.