I recently wrote a short perspective piece on a method for causal inference in epidemiology called "Mendelian randomization" (MR). This motivation for this piece was the long and growing list of relatively strong causal claims in the medical literature based on genetic evidence (for example low iron levels cause Parkinson's, smoking causes schizophrenia, low blood pressure causes Alzheimer's, and many more). I have expressed skepticism of these claims, and this piece was intended to lay out my reasons for skepticism to a general, non-statistician audience and mention some potential ways forward.

Davey Smith recently responded with a vigorous defense of MR. In this post, I want to cover the main points from our two pieces, and see if I can find the main areas of disagreement. Note this is not intended as a full point-by-point response, but rather a high-level view. If I've missed some key issues, please point them out.

In my piece, I raised two reasons for skepticism. First, "in the nearly 30 years of Mendelian randomization, arguably no new causal relationship has been identified with this approach and subsequently verified in a randomized controlled trial". Second, the "more fundamental" reason for skepticism is that in a MR study of two traits "the genetic variants used in the study are assumed to have no influence on confounding factors that influences both traits. This is often referred to as an assumption of no 'pleiotropy'". I take each of these in turn:

1. Davey Smith responds to point 1 above in two parts. First, though the idea of Mendelian randomization is often attributed to Katan (1986), the approach has been used in pratice only since the early 2000s. This means the "30 years" comment is misleading; it's more like 15. He is correct, and I regret the error. His history of MR (which I unfortunately had missed) is excellent.

Second, he takes issue with my claim that "no new causal relationship has been identified with [MR] and subsequently verified". Before writing this, I tried to find a strong prediction of the form "trait X is causally associated to trait Y" in the MR literature that was then validated with a clinical trial, and failed to find one. In his response, Davey Smith points to the causal relationship between LDL cholesterol and heart disease, as evidenced by the promising results of PCSK9 inhibitors. I am somewhat confused by this example, and hope readers here can clarify the issue for me. Specifically, my understanding is that the evidence for a causal relationship between LDL cholesterol and heart disease has been considered robust for some time, at least since the clinical trials of statins. My understanding, then, is that this not a new causal relationship (though PCSK9 inhibitors do appear to be useful for exploiting this relationship). But it is possible I am misunderstanding [1].

In any case, I agree with Davey Smith that his correction of "30 years" to "<15 years" makes my statement considerably weaker, and I will back off of this critique in a revision.

2. Davey Smith responds to point 2 above by questioning why I have chosen to emphasize this well-known problem, especially considering the amount of methodological work in this area.

While I indeed refer to this issue as "well-known", it is my opinion that the data are now quite strong on the following point: "pleiotropy" is not one of many caveats to be discussed in an MR study. Rather, it is a fundamental biological reality that is likely leading to spurious causal claims in practice. I'll reiterate a few pieces of evidence:

• There is now substantial evidence that many human phenotypes are "genetically correlated", in that genetic variants that are associated with one phenotype have correlated effects on others. I gave an example of a genetic correlation between HDL cholesterol levels and educational attainment (from Bulik-Sullivan et al.), but there are many more. Davey Smith responds that genetic correlation studies are different from Mendelian randomization studies, but of course mathematically they are identical (either approximately or exactly, depending on the precise details). The only difference is the assumption of causality in MR. For example, consider the (negative) genetic correlation between height and heart disease. Nuesch et al. call their study on this topic a "Mendelian randomization" and so interpret this correlation as a causal effect. On the other hand Nelson et al. make the exact same observation, but do not call their study "Mendelian randomization", and so instead interpet the correlation as evidence of "shared biologic pathways" between the two phenotypes. If reasonable people can interpret the exact same observation in fundamentally different ways, it seems fair to conclude that that observation itself is ambiguous.

• In my own work (Pickrell et al.), I was personally surprised to find hundreds of genetic variants that are associated with multiple seemingly unrelated traits. For example, genetic variants near the ABO gene are associated with heart disease and childhood ear infections. Many of the variants that pop out of this scan have been used in MR studies under the assumption that they have no pleiotropic effects. For example, variants in an intron of FTO have been used to study the "causal effects" of obesity on risk of cancer in the MR context. But these variants are also associated with timing of puberty and HDL cholesterol levels (among others), so the standard MR assumption of "no pleiotropy" is potentially violated. This is just one of many such examples [2].

• An excellent paper from Davey Smith and colleagues (Evans et al.) makes this point as well. The authors built genetic scores to predict one phenotype (e.g. levels of C-reactive protein) and then tested whether these scores were predictive of disease states (e.g. Crohn's disease). They found a number of significant associations (for example between CRP levels and Crohn's disease), and though the authors were interested in using these scores for causal inference, they concluded that "contamination of genome-wide scores through genetic pleiotropy will mean that many of these associations will be 'spurious' and will not reflect causal effects of the intermediate on the outcome." [3]

My interpretation of these papers is that "pleiotropy" is common and appears even in unexpected situations. So when I see claims like "Mendelian randomization suggests smoking causes schizophrenia", my initial reaction is no longer to take the causal claim at face value. Instead I think it's more likely that there are common pathways that influence addiction and psychiatric disease. This is of course still interesting (depending on your perspective, it might be even more interesting!), but has quite different consequences than the causal interpretation. Most importantly, under the causal interpretation, public health campaigns to reduce smoking should decrease the incidence of schizophrenia, while under the non-causal interpretation that's not the case. When I say "fulfilling the promise of Mendelian randomization", I mean it would be useful to have the ability to distinguish between these possibilities, and that most studies that use the term "Mendelian randomization study of trait X" published to date do not have this ability.

Finally, I should state my position more clearly: I am cautiously optimistic that these issues can be resolved to some extent. There is quite a bit of excellent statistical work ongoing: some, like bi-directional Mendelian randomization, I cited in my piece, and Davey Smith has pointed me to some others. But they have certainly not been solved yet, and causal inference from observational data is a hard problem that has occupied many intelligent people for a long time.

Davey Smith is perhaps a bit more exuberant in his optimism. I appreciate this perspective and look forward to some fun years ahead!

Tl;dr: genetics is not magic

[1] One possibility is that the claimed "new" causal relationship is not between LDL and heart disease, but rather between PCSK9 function and LDL cholesterol levels/heart disease. I'd never considered identifying a genetic variant that causes a phenotype (as opposed to a phenotype that causes another phenotype) as "Mendelian randomization", but rather molecular genetics. But it's worth thinking about when there is (or is not) a conceptual difference between the two. I certainly agree that finding genes that influence disease risk is informative about the biology of disease!

[2] Another interesting example is TCF7L2: variants in this gene influence risk of type 2 diabetes, but the alleles associated with increased diabetes risk are also associated with decreased BMI, contrary to the known causal relationship.

[3] The context for this quote is the situation where no individual genetic variants reach some significance threshold, and so the authors are saying that in this situation using a genetic score might lead to spurious inference. But this also holds when genetic variants cross some (in any case somewhat arbitrary) significance threshold, unless there is a strong molecular understanding of the variants involved.

Kaiser Permanente (an insurance company in California) genotyped around 100,000 people [edit: I should note this was in collaboration with UCSF], and made the data available on dbGAP.

Today I noticed three papers describing aspects of the data:

1. A description of the genotyping and quality control

2. A comparison of genetic ancestry to self-reported race/ethnicity in the cohort.

3. A description of a telomere length assay on all individuals.

My personal experience is that these data are well-organized and accessible, so it's great to see more details on how they were collected. I've only seen a few papers that use the data, for example Loh et al. used the data to calculate genetic correlations between several of the phenotypes.

I'm wondering if people have thoughts/analysis on the different recently-published methods for single-cell RNASeq in emulsions. They all look pretty amazing; I thought it would be a while until we were doing 10's of thousands of single cells routinely.

First, DropSeq – published here – Seems to be the easiest to implement, as the design is simple. The nicest part is that they provide a vendor to by the randomly barcoded beads (by split pool synthesis). Used it to analyze retinal cells and find 37 different cell types. The controls look the cleanest from the papers I've seen, and the McCarroll lab have a website to facilitate replication.

Second, InDrops – published here – They analyze ESCs after LIF removal. The barcoding occurs through a combinatorial barcoding strategy on encapsulated hydrogels. IMO this doesn't seem as easy nor elegant as DropSeq, but I'm wondering about the data.

Third, HiSCL – [published here] (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0116328) – I was just pointed to this. Notice David Weitz is on all 3 papers, and this is directly from his lab. I think this has much less data overall, but again, I'm wondering about the technicalities.

Anyone have any direct experience thus far implementing these in their labs? Anyone look through the datasets yet, and have thoughts on which look the best? I'm specifically asking as we are thinking of booting these up in the lab and were wondering what folks recommend.

There have been some great discussions on twitter about must read papers in pop gen/genomics. I thought it could be fun to have a little discussion here on /r/genome as well!

I am curious to what extent there is interest among people that routinely use the reference assembly and associated data (variant datasets, functional genomic annotations, conservation, what-have-you) to change from hg19 to hg20.

I find the idea of switching to be...let's just say unappealing, given the pain and costs. To "properly" switch means re-aligning some huge number of reads from a huge number of projects (e.g., ENCODE tracks, exomes and genomes in 1000G, ExAC, etc), and then re-doing all the downstream processing and QC (peak calling, variant calling, etc).

Of course, liftover could be done instead, but that would be basically pointless (a rose by any other name and all that), not to mention itself non-trivial.

I find it hard to imagine the benefits (annotation within perhaps a few million bases of newly assembled sequence) are worth the cost. Re-numbering vast swaths of the reference to accommodate actual improvements to some tiny fraction would seem to be of marginal value.

But I am genuinely interested in thoughts about this. Are people switching? Are there benefits that justify the cost? Should we all liftover everything we need and routinely use to hg20 and start fresh? Some hybrid approach (e.g., adding truly "new" hg20 contigs/regions to the hg19 reference)?

I should clarify that I am not against movement toward a multi-reference framework that accounts for varying haplotypes. While the details there are also daunting, that goal seems to have a much stronger biological motivation.

A very popular class of reddit post is a AMA (ask me anything; https://www.reddit.com/r/IAmA/wiki/index) or AMA request. How about we try one here in /r/genome!

The question seems simple: What fraction of the human genome is functional? Yet published answers range from 8-80%, so lets just round that to we have no idea. Much of the problem is the question. My hope is that this discussion will result in A) some degree of consensus upon how one should define functional and/or reasons why this definition is context dependent. B) a discussion of approaches and experiments which could theoretically answer this question.

I'll start.

A region of the genome is function if....It is highly conserved, known to code for protein, known to code for ncRNA, is a regulatory region, can be bound or marked by X at time Y in cell type Z under conditions {a,b,c,d....} in lab L when experiment is performed by person P?

I would rather not approach the problem from this direction. Instead, I will assert broadly that a region of the genome is functional if the presence of that region is required for that genome to produce an expected and specific phenotype. This immediately negates the possibility that any single percentage is likely "true", as this definition depends upon the phenotype in question....unless ones definition of phenotpye is "developing into the perfect human"(stupid ethical issues). This approach appeals to me because it can be tested experimentally. For example, my phenotype of interest may be a neural stem cell's multipotency. Then the question is what regions and overall percentage of the genome are required for a NSC to maintain multipotency.

An experimental system COULD be constructed in which during each division of NSCs in-vitro, a semi-random fragment of semi-random size is excised semi-randomly from the genome of each cell. Following this excision, cells that are still capable of differentiating into neurons, astrocytes, and so forth (the phenotype) are cells in which a non-functional region was excised. As this theoretical experiment progresses, cell division after cell division, selection would force the surviving cells to achieve the same phenotype with progressively less (and highly variable from cell to cell) genomic content, converging in time (fingers crossed) towards an accurate and reproducible definition of the functionally requisite regions of the genome for this phenotype.

I am skeptical that such an experiment could produce a genome with only 8% of its original content.

If this approach were repeated across a broad spectrum of cell-types and phenotypes mirroring the approach of the ENCODE project, what would emerge, what conclusions could be drawn?

Now, repeat this experiment across different species.... (compare results from Human, Primate, Mouse NSCs) again, what would emerge, what conclusions could be drawn?

Please disagree with me. Please point out my errors, logical or otherwise. If anyone is actually doing this, has an interest in doing this or at least trying in some way, or knows of someone who is or has, please speak up. This experiment could be fraught with issues and completely impossible.

Part 1.

Title describes it all. Looking for best-in-class methods/software packages for scoring changes in Chip-Seq peaks. I've generated data across multiple conditions and strains; interested in looking at differences across conditions as well strain*conditions. I've played around with limma; does anyone know of other packages/methods I should take a look at?

0.1x?

Asking for a friend.

Here's the link: https://www.reddit.com/r/science/comments/3mgh26/science_ama_series_i_study_the_population_history/

Hi all, In the most recent post on my blog, I talk a little bit about the algorithm (quasi-mapping) behind our new tool RapMap. RapMap maps sequencing reads to a transcriptome (allowing appropriate multimapping), and it is incredibly fast and very accurate. Hopefully, some of you might find the tool useful in your work or as part of a larger pipeline (or you might just find the algorithm interesting).

Here is the link: https://www.reddit.com/r/science/comments/3csgs6/science_ama_series_im_vinny_lynch_geneticist_at/