FTO, cohort of birth and body mass index

In a recently accepted article in PNAS, entitled "Cohort of birth modifies the association between FTO genotype and BMI," the association of FTO variant rs993609 with body mass index is described as having essentially zero influence for study participants born before 1942 and increasing influence on this obesity phenotype as participants were born in increasingly more recent years. That long-range enhancers within the FTO region recapitulate aspects of IRX3 expression implies that the obesity-associated interval serves to regulate IRX3. Consistent with this, obesity-associated SNPs are associated with expression of IRX3, but not FTO, in human brains. Nonetheless, this is an important obesity locus, be it FTO or IRX3 as the functional unit. 
 
The authors rightly suggest that gene-environment interactions (GxEs) coupled with changes to the environment of the participants could alter the FTO-BMI association. 

FTO is subject to exercise-induced changes in DNA methylation. See, for example, table 5 (and reference 3) of  Rönn, Volkov, et al. We have cataloged a large number of genetic variants that show the type of GxEs suggested by the recent PNAS article. That catalog shows that some nine different studies observed modulating effects of physical activity on the FTO-BMI association. (In most populations of European ancestry, for example, in which many of these studies were conducted, the variants analyzed are in relatively strong to very strong linkage disequilibrium.) Other lifestyle choices also modulated the effects of FTO variants, including macronutrient intakes of carbohydrate, and fatty acids such as saturated fat, MUFA (mono-unsaturated fatty acid) and PUFAs (poly-unsaturated fatty acids). Whether time spent engaged in physical activity shrank as the birth cohorts became more recent, or diet changed, or some combination of this, is difficult to ascertain. But a list of known FTO-BMI GxEs would be a good place to begin such an analysis.
 

APOE, memory impairment, diet and N-3 PUFAs

APOE is a curious gene. It has roles in both lipid/cholesterol homeostasis and memory impairment with its associations with Alzheimer disease. For example, see this entry in OMIM and the section titled "Role of APOE in Abnormalities of Blood Lipids and in Cardiovascular Disease." If you read through that long section, over 2600 words, you'll learn that APOE is an important contributor to the management of low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL). If LDL and VLDL levels are not in homeostasis, triglyceride levels can become elevated, which increases risk of atherosclerosis.

A recent report in Nature Medicine by Mapstone, et al. entitled "Plasma phospholipids identify antecedent memory impairment in older adults" identified a panel of ten blood-based lipid biomarkers for "detecting preclinical
Alzheimer's disease in a group of cognitively normal older adults." Those ten lipids include two acylcarnitines and eight
phosphatidylcholines (PC), specifically:

propionylacylcarnitine
3-OH-hexadecenoylcarnitine (C16:1-OH)
phosphatidylcholine diacyl C36:6 (PC aa C36:6) *
lysophosphatidylcholine acyl C18:2 (lysoPC a C18:2)
phosphatidylcholine diacyl C38:0 (PC aa C38:0) *
phosphatidylcholine diacyl C38:6 (PC aa C38:6) *
phosphatidylcholine diacyl C40:1 (PC aa C40:1)
phosphatidylcholine diacyl C40:2 (PC aa C40:2)
phosphatidylcholine diacyl C40:6 (PC aa C40:6) *
phosphatidylcholine acyl-alkyl C40:6 (PC ae C40:6) *

These were noted by the study to be lower in the group of cases compared to controls.

Curiously, this group did not reference the findings from a 2013 study by Rudowska, et al., that characterized the transcriptomic and metabolomic signatures of adding N-3 polyunsaturated fatty acid (N-3 PUFA) to the diet in a Caucasian population. Of their findings, it is most notable that five of the eight above-listed PCs were increased after the six-week N-3 PUFA intervention. These are noted with an asterisk above.

Whether a diet rich in N-3 PUFAs could decrease risk of memory impairment or Alzheimer disease (AD) is a matter for further investigation. Nonetheless, that five of these eight PCs show opposite changes when comparing an N-3 PUFA intervention with the group of cases in the Mapstone, et al. study is highly interesting. Consider also for the moment gene by diet or gene by environment (GxE) interactions. A GxE interaction is an association between a genetic marker and a phenotype that is modified by an environmental factor such as the diet, macronutrient (ie, fat, protein or carbohydrate) intake, physical activity or any of many other lifestyle choices. The risk allele will not show itself as risk until the environmental factor passes a given threshold, say too much saturated fat and now the risk is elevated.

The overlap of the five PCs highlighted here, coupled with the large number of gene-environment interactions we see for the common AD/blood lipid variants of APOE - SNPs rs429358 and rs7412 - strengthen my personal view that lifestyle has a significant role in cognitive decline.

The WHO's report on noncommunicable diseases

The World Health Organization of the United Nations has released a report titled "Global status report on noncommunicable diseases." Access to the report and its individual chapters is at this link. I was particularly interested in Chapter 1 and the major contributing factors to noncommunicable diseases (NCD).

According to the above report and others from the WHO, the four primary contributors to global increases in NCDs, such as type 2 diabetes, cancer, and cardiovascular diseases, are:

tobacco

harmful use of alcohol

unhealthy diet

physical inactivity

While such a list is really not surprising, what I do take from this, with respect to my own research on the genetic basis for the differential response to the diet as it pertains to metabolic diseases, is these are our key environmental factors used to assess gene by environment, or GxE, interactions. In other words, while these factors are strong contributors to NCD onset and progression, genetic differences exert different influences on the disease risk, onset and progression in different individuals. That influence could be negative - increasing risk - or positive - being more protective.

Thus, the importance of GxE identification cannot be overlooked, and ought really to be emphasized in genetic association studies.

Genetics of coronary heart disease

Note: This is a guest-post, authored by geneticist and molecular biologist Dr. Chao-Qiang Lai; with edits added by LP.

Last week, Nature Genetics published three letters reporting results from genome-wide association studies (GWAS) for coronary heart disease (CAD). The studies reported a number of markers that reached the threshold of statistical significance for association to CAD with concomitant association to traditional biomarkers of disease risk, such as elevated LDL-cholesterol (LDL-C), elevated total cholesterol, decreased HDL-cholesterol (HDL-C), hypertension, obesity (as measured by elevated body mass index), or type 2 diabetes. However, the two larger and more highly powered GWAS (C4D Genetics Consortium, Schunkert, et al.) also identified CAD-associated variants that are not associated with traditional biomarkers. The third study is of interest because it examines CAD in Chinese populations, but beginning with a discovery set of 130 cases and 130 controls leaves it a bit under-powered. They report a unique association between a SNP in C6orf105 and CAD, which is not found in European or south Asian populations. Curiously, this gene has also been implicated in non-syndromic oral cleft.

There are many sources of CAD. Blood lipids are most commonly thought of as the prime source, but blood pressure in the form of hypertension is also a source. Traditional biomarkers such as LDL-C, HDL-C, triglycerides, and hypertension have been used almost as the sole surrogates for measuring the devolvement and progression of CAD over the course of some 50 years. Meta-analyses of GWAS based on over 100,000 subjects (22,233 cases and 64,762 controls from 14 GWAS) thus far have identified 23 genetic variants associating with CAD. The eye-opening aspect to this is these variants account for about 10% of CAD cases with the shocking observation that 17 of 23 confirmed loci appear to have no association with traditional markers. This observation then suggests two possible explanations.

One possibility is when we assume that the remainder of the CAD cases (90%) contribute to risk associated with traditional markers, such genetic factors cannot be detected based on current GWAS methodology. This is likely to be true because of to the effect sizes of these variants are too small, or their effects are camouflaged by gene-gene (GxG) and gene-environment (GxE) interactions or by epigenetic mechanisms.

This second possibility rests on the fundamental premise that all markers associating with CAD have more or less equal chance to be detected. It then follows that a majority of genetic factors that contribute to CAD has nothing to do with traditional markers. If this is indeed the case, it opens a new avenue to identify the new mechanism(s) and new biomarkers that lead to CAD. In fact, this possibility is supported by many observations. For example, 50% of those individuals who have CAD have low LDL-C (Braunwald & Shattuck; Ridker).

These genes – for example, ADAMTS7, PDGFD, ABO and PPAP2B – point to new mechanisms. While GxG, GxE and epigenetic interactions remain as viable contributors to CAD risk, the path to better understanding of the other component(s) to CAD risk will likely transit through metabolic profiling to identify the compounds that distinguish elevated from nominal risk. Furthermore, research will need to be conducted in model organisms based on these newly discovered genes, perhaps in pig as this is a good model for heart function and disease in human.

A water flea's phenotypic plasticity and HDL-cholesterol in humans

This week marked the announcement of the completion of the genome sequence of the water flea Daphnia pulex. I remember peering through a microscope in my first biology classes amazed at the activity and diversity of structures of these creatures. Now, the 200-megabase genome has been deduced. One of the startling discoveries is the small D. pulex genome is packed full with more than 30000 genes, far exceeding the number in the human genome. Some 13000 genes were identified in the paper by Colbourne, et al. as paralogs - arising from gene duplication.

Here is part A of figure 1 from the paper illustrating major differences in gene numbers between D. pulex and other animal genomes.

So, why all these paralogous genes? Well, the upshot here is one of likely gene duplication as a means to build an inventory of possibilities for a wide range of phenotypes. This scenario is spelled out rather nicely by Dieter Ebert in an accompanying overview. The water flea is remarkably able to sense its predators in a very precise manner and in turn activate any of a number of genes that direct expression of defense mechanisms. Some of these are structural features such as protective helmets, tail spines and neck teeth. Herein is the water flea's phenotypic plasticity - different environments induce expression of different subsets of the vast genome for the purpose of evading the predator. A gene for each bad guy swimming nearby.

Now, let's consider humans and their environment. In particular, I'd like to offer the example of diet, for most this is high in fat and sugar, and the important blood lipid of HDL-cholesterol, so-called "good cholesterol." Regular readers of this blog know that our research expends a good deal of effort in describing gene-environment interactions (GxEs). This is a situation where one allele of a genetic variant like a SNP associates with disease risk only when a given environmental factor passes a certain threshold. We have compiled a series of these GxEs for phenotypes pertinent to metabolic syndrome - phenotypes such as body weight, BMI, blood lipids, blood pressure, glucose and insulin levels, as well as heart disease and type 2 diabetes risk. Those data are available here. If you mine those data, you'll notice that by far there are more GxEs reported in the literature for HDL-cholesterol than any other commonly measured phenotype.

Thus, it seems to me that the water flea has a lot of very similar genes, mostly in paralogous pairs to cope with slight changes in its environment. Humans do not. Eating a sub-optimal diet will likely drive HDL levels down (unhealthy). There are also age-related, natural declines in HDL. At the same time, there are a number of variants in our genomes that show an environmental sensitivity with respect to HDL - there are many ways to activate a program of increased risk (by lowering HDL levels). And similar cases can be presented for LDL, triglycerides, total cholesterol, blood pressure, waist circumference, body weight, etc. So, while it may take years of indulging in a sub-optimal diet before an adverse event such as diabetes of atherosclerosis is diagnosed, perhaps our (relatively) small number of genes, each with a collection of variants, that sets us up for sensitivity to what we put in our mouths. If we can't eat right, then perhaps more genes would be the answer to a better defense against a poor diet.

One size does not fit all

On January 11th of this year, 23andMe, one of several companies offering direct-to-consumer genotyping (or genetic testing), put out a press release entitled, "23andMe Presents Top Ten Most Interesting Genetic Findings of 2010."

I found number 5 on that list to be quite appealing. It reads, in part:

One size doesn’t fit all — personalizing treatment

The old adage, “take two aspirin and call me in the morning,” doesn’t work as well as we might think. It turns out that one size doesn’t fit all when it comes to drug response, and for some people, certain drugs might be more effective, not work at all, or even produce serious side effects. The growing body of pharmacogenomics research has helped us understand that, at least in part, genetics play a role in how well some drugs work for different people. The 23andMe Drug Response reports link customers’ genetics to the way they might respond to certain drugs and medications. The results range from whether you’re likely to benefit from a drug, need a different dose due to sensitivity, experience toxic or adverse effects, or even have increased risk for other conditions. 23andMe cautions that its Drug Response reports should not be used to independently establish, abolish, or adjust medical treatment and medications but should be discussed with your physician. Only a medical professional can determine whether a particular drug or dose is appropriate for you.

The piece goes on to describe, briefly, two genes, CYP2C9 and VKORC1 and the role of variants of these genes in warfarin dosing.

OK, so this is all neat but really only represents the tip of the tip of the iceberg. There are many more examples of one size not fitting all and reaching far beyond pharmaceuticals. We and many others have reported on many such interactions between certain genetic variants and diet which affect disease risk. On this blog, I have listed some examples pertaining to HDL-cholesterol. And those variants that show interactions with physical activity as modifiers of disease risk are really interesting. And not to forget other environmental factors or lifestyle choices of sleep, latitude and altitude of residence (how much seasonality you experience, oxygen tension), use of alcohol, use of tobacco, and so forth.

For scientists, medical professionals and the general public alike, clearly a greater understanding of elements at the basis of "one size does not fit all" would be welcome. Stay tuned, it's happening - more of these elements, the gene-environment interactors, are being described and collated into databases.

Gene-HDL associations modified by physical activity

A brief post here. I am simply listing a few genes/SNPs that associate with HDL-cholesterol in a manner modified by physical activity.



One can see from the above table (click for a larger view) that results of physical activity modifying the effects of APOE alleles is not consistent across populations. There are different risk alleles in the different studies. The EUROSPAN study under PubMed ID 20066028 did not give specifics of levels of physical activity nor identify the risk alleles.

If there is something that interests you in terms of measures of metabolic health (along the lines of heart disease, diabetes, blood lipids), just ask and I'll see what I can provide.