Much to the surprise of many scientists, a lot of the SNPs identified in genomewide association studies have not been in the parts of genes that encode the molecular machinery of a cell.

Instead, many SNPs have been found on the edges of genes, in regions of DNA that control when the genes get turned on or off, in parts of genes that get cut out before the final proteins are made, or even in so-called “gene deserts,” areas of DNA that don’t seem to contain any genes at all.

Rs6983267 is one of these gene desert SNPs.  People with two copies of the G version of this SNP have about 1.4 times the odds of developing colorectal cancer compared to people who have two Ts, but so far no one has been able to figure out why.  Two new reports show that, even though this SNP seems to be out in the middle of nowhere in the genome, it can interact with components of a signaling pathway known to be overactive in more than 90% of all colorectal cancers.

(23andMe customers can see their data for rs6983267, as well as two other SNPs associated with increased colorectal cancer risk, in Health and Traits.)

Results from Tuupanen et al. and Pomerantz et al., both published online this week in the journal Nature Genetics, show that the region of DNA containing rs6983267 is an “enhancer” that can turn up the amount of protein made from the MYC gene. The riskier G version of the SNP appears to make the enhancer stronger than the T version.

In colorectal cancer, increased MYC expression can often be traced to overactivity of a molecular signaling pathway known as Wnt.  Both groups of researchers found that the region of DNA containing rs6983267 was responsive to Wnt signaling, thus connecting this SNP to a well-established cancer mechanism.

Drugs that attack the Wnt pathway are attractive candidates for cancer therapies.  According to Tuupanen et al., the new results suggest that these same types of drugs might be useful for personalized cancer prevention treatments in people who carry the riskier version of rs6983267.

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Genomewide association studies have had some success in finding DNA variants associated with increased risk for bipolar disorder.  But researchers from the MRC Centre for Neuropsychiatric Genetics and Genomics at Cardiff University in England have taken these studies a step further by looking for common functional themes running through the GWAS data. Their results, published online this month in the American Journal of Human Genetics, implicate some of the most basic biological pathways in the genesis of bipolar disorder.

Starting with the idea that the genetic variations increasing risk for a disorder are probably not randomly distributed throughout the genome, but instead are in one or more sets of related genes, Holmans et al. re-analyzed the SNP data from four previous studies that included a total of 4,387 cases of bipolar disorder and 6,209 controls. Researchers at the Children’s Hospital of Philadelphia recently took a similar approach (although the technical details of the analysis differ) for a study of Crohn’s disease.

The bipolar disorder researchers found that biological pathways involved in broad control of cellular activity were overrepresented in the genetic variations association with bipolar disorder.  Two of the pathways, hormone activity and cellular self-digestion, are known to be impacted by lithium, the major medication used to treat bipolar disorder. As always, more research will be needed to substantiate these findings, as well as to understand how they can be used to help the more millions of people worldwide dealing with bipolar disorder.

(23andMe customers can learn more in the Bipolar Disorder Research Report and the Bipolar Disorder: Preliminary Research Report.)

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Each one of us carries in our cells the vital genetic data, compliments of our parents, that code for many of our traits and attributes.  Whether it’s our eye color, height or the ability to consume dairy products, the variations in our genes contribute to making us ‘one of a kind’.  Unfortunately, these variations can also lead to the onset of disorders that aren’t so unique.

Technology now allows scientists to tap into our DNA as they attempt to unlock the underlying genetic causes of diseases that afflict so many of us.  These studies, often called Genome-Wide Association Studies (GWAS) because of their comprehensive design, are producing some very compelling results. Under the present research model, individuals who are asked to consent to participating in these studies typically donate a blood or saliva sample and provide access to information about their particular disease (or drug response, in the case of pharmacogenetic studies) through their health records or through diagnostic interviews.  Scientists then look for genetic correlations that can help direct the development of diagnostics and therapeutics.

This model is fairly steeped in tradition and protocol.  Once your sample and information are collected, researchers go out of their way to break the link back to you, with the mindset that it’s a necessary measure to protect your privacy — and, frankly, minimize their liability to deliver and explain the data. The genetic information derived from your DNA is often “de-identified” or “anonymized” so that it can’t be traced back to you.  As a “human subject” in a study such as this, you are not offered access to this very personal data.  Yet it could be very important for you to know. Now that we have more knowledge about how our genes impact our lives, thanks to these very studies, shouldn’t you be given access to the data if you want it? Even if there’s little you can do to alter the course of your genetic predispositions — which are often not definitive — we’re seeing overwhelming evidence that a lot of people would like this information.

At 23andMe, we believe it’s time for a research revolution, where the people involved — let’s no longer call them human subjects — can play a more active role and contribute more directly to studies of most interest to them and their families.  And if any individual would like access to his or her data, he or she should be granted that request.

In this spirit, 23andMe is proud to support www.HealthDataRights.org and the Declaration of Health Data Rights.  We believe genetic data are an integral part of your health information, and you should have access if you so choose.

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Malaria is one of the leading causes of death in the developing world, claiming nearly a million victims each year. The great majority of them are African children below the age of five. The illness is caused by a single-celled parasite called Plasmodium that is transmitted by mosquito bites to humans. In a paper published today in Nature Genetics, a group of African and British doctors and scientists report on their study of the genetic roots of malaria susceptibility. They found no new smoking gun with this effort, but learned much about how to improve African genetic studies in the future.

The researchers gathered the SNP genotypes of 2,500 children, with the consent of their parents, from a small region in The Gambia. About 1,000 of the children had been admitted to the hospital with a case of severe malaria — the other 1,500 were newborns. In a genomewide association study, the researchers checked each of a half-million SNPs (single nucleotide polymorphisms) for sharp differences in genetic composition between the group of children suffering from malaria and the group of newborns, who served as an approximation of a malaria-free group. If one version of an individual SNP was seen at high frequency among the malaria victims, but at low frequency in the newborns, then the difference might be because the SNP tends to cause malaria or is nearby one that does.

Upon scanning their data, the researchers came up more or less empty-handed: by the usual standards of the field, none of the 500,000 SNPs would pass muster.

This deflating result stands at odds with what is known already about the genetics of malaria susceptibility. Most people who have taken a biology class learn that human populations in malarial regions have developed a natural immunity to malaria infection, not through their immune systems, but through a genetic modification of hemoglobin. Hemoglobin is a molecule charged with ferrying oxygen from your lungs (and the lungs of most life forms that have them) to all your cells, an essential task. Biologists have traced hemoglobin-based malaria resistance to a change at a single DNA base pair on chromosome 11 — wouldn’t we expect at least this SNP to light up as significant?

In truth, the failure wasn’t so surprising; it arises from the interplay of genetics with our species’ history. Humans first arose in Africa, so that’s where genetic variation has had the longest time to build up. Modern-day Asian, European, and Native American people descend from people who emigrated from Africa about 50,000 years ago. These migrants carried just a subset of the African gene pool with them, so non-African populations today have much less “well-mixed” genomes than African populations. The present study uses genotyping chips developed for use in European populations, and its failure to find the known hemoglobin SNP (which isn’t even genotyped by the chip) and other known genetic contributors to malaria resistance is essentially due to the fact that you’d need more like two million SNPs than half a million to do the job right.

The solution, you’d think, is just to make a chip with a lot of markers for specific use in Africa, and be done with it. But the authors show that African genomes appear to be mixed so well that no single such chip could be designed. Instead, they propose an alternative approach: use a good but inevitably suboptimal African SNP chip in your full study sample, then obtain full genome sequences from a small number of the members of that sample. Then, using a powerful statistical method called imputation, you use the full sequences of the smaller group to fill in the full genomes of the entire study sample based on their SNP genotypes. This approach, as the authors demonstrate convincingly in the case of hemoglobin-based malaria resistance, would provide a statistically powerful and economically viable means of tracking down the causes of some of the most challenging health problems of our time.

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Map of Mexico with sampled-from states highlighted.

Recent studies of genetic diversity among Europeans (blogged about here and here) show that DNA is a surprisingly good predictor of where a person lives; people from the same country tend to be more similar to one another than to those from other parts of the continent. This latest study, which was published earlier this week in the Proceedings of the National Academy of Sciences, shows a similar pattern in Mexico: Mexican Mestizos (people of mixed European and Native American ancestry) from the same state tend to group together genetically, and the groups themselves fall along a genetic continuum that corresponds roughly to their latitude. You can see for yourself in this plot from the paper (below), which we’ve modified a bit for clarity. This is the same kind of plot used in 23andMe’s Global Similarity: Advanced feature — each point in the plot represents a person. The closer two points appear in the plot, the closer those two individuals are to each other genetically. The 300 Mexican Mestizos fall into a line stretching from a group of Europeans at the upper right to a group of Amerindians¹ at the lower left.

PCA map of Mexican genotypes.

The people from Sonora, the northernmost state, appear closest to the European cluster, and the people from the sourthern states Guerrero, Veracruz, and Yucatan appear closest to the Amerind cluster. This makes you wonder whether this pattern might correlate with the proportion of European ancestry. The researchers wondered that too, so they investigated further by analyzing their dataset with a computer program that estimates the proportion of ancestry a person’s DNA derives from each of several reference populations. When they set the program loose, they found that the six states did vary widely in proportion of European ancestry, from an average of 65% in Sonora (fifth column from the left) to an average of 35% in Guerrero (second from the right):

Admixture proportion estimates for Mexican and HapMap samples.

The authors note that this pattern makes sense, since Amerindian population density declines as you head north. Also, you might note that there’s a green sliver of African ancestry in each of the Mestizo populations, which approaches 5% in the southern states of Veracruz and Guerrero. This also meshes well with the historical record, since it’s through these coastal states, among others, that African slaves entered Mexico during the Spanish colonization.

These analyses tell us much about Mexico’s history — could the same dataset serve to improve Mexico’s future? This paper also examines the prospects for conducting medical genetic studies in Mexico.

The most popular study design nowadays, and the one that the researchers focus on, is the genomewide association study, or GWAS, also called a case-control study. GWASes are the basis for essentially all the studies discussed in the Spittoon’s SNPWatch section, and the majority of the findings underlying 23andMe’s Health and Traits reports, and have recently been the subject of intense debate in the genetics community.

The core idea of a GWAS is to look for genetic markers (these days, usually common single-letter DNA variations known as SNPs) in a population that are at very different frequencies in a group of people with a particular disease, say type 2 diabetes, than in a group of people without that disease. In order to do that, you first have to identify a comprehensive set of the common genetic variations within a population, and then create a DNA array (commonly called a SNP chip) to probe all those variable locations in a large number of people with and without the condition you’re studying.

There’s the rub. In recent years, a project known as HapMap has created catalogs of common variations in European, African and East Asian populations, and chips have been produced based on it². But Mexico’s population is a mixture of two of those (European and African) and another population (Amerindian) that is related, but not identical to, the third. There is no Mexican SNP chip.

That’s why the authors of this paper are suggesting a project to characterize common genetic variation within the Mexican population itself. They estimated that a catalog of common genetic variation using any two of the Mestizo groups they analyzed would capture enough variation to fuel quality GWAS studies, and would require fewer markers to do so than the alternative, which would be to use all the common markers from HapMap itself. This would substantially lower the cost of genotyping, they argue, and the reduced cost of using a platform based on the Mexico-specific catalog would allow researchers to genotype many more people for their GWAS studies for the same number of pesos. Since sample size is often the limiting factor in the ability of the GWAS design to find disease genes, this could improve their ability to find the genetic causes of inherited disease.

Thanks to 23andMe Founding R&D Architect Brian Naughton for his assistance in the preparation of this blog post.

Notes

1. Amerindian, sometimes just Amerind, is short for “American Indian”, and it denotes a descendant of the indigenous peoples of the Americas; anthropologist-types use the word to avoid confusion with the Indians that live in South Asia.
2. Because full-genome sequencing is still a few years from being affordable, researchers cannot look at every single one of the 3 billion nucleotides in the genome to find the one (or combinations of more than one) that are directly linked to a particular condition. For now, they must make do with the half-million to million SNPs that the current crop of SNP-genotyping chips allow.At first blush, this sounds like a fool’s errand. How can you possibly say anything about 3 billion DNA nucleotides with a collection of just a million markers? What if the marker you have on your SNP chip is near to but not actually the disease-causing SNP? Won’t this be like ships passing in the night? In just the last decade or so, geneticists have learned that the human genome has a very peculiar property: the 22 numbered chromosomes, or autosomes, and the X, tend to do the bulk of their recombining in a very small fraction of the spans of those chromosomes. These highly-recombining locations are termed hotspots; it’s kind of like chromosomes are trains, with hotspots as the links between boxcars (although unlike boxcars, which are all the same length, there’s considerable variation in the distance between hotspots). This state of affairs is good news for GWASes, because it means that these boxcars — big chunks (mean length ~200,000 DNA base pairs) of chromosome that tend to be passed from parent to child as a unit — aid the task of marker selection greatly. If you sample a bunch of people, as these researchers have done in Mexico, then you can build up a catalog of the specific chromosome chunks that occur. The idea is that when a disease-causing allele occurs, it can’t help but sit on one of these chunks, so the problem of building a GWAS chip is transformed into choosing markers that reliably distinguish between the chunks. It’s like being able to assign one inspector to each boxcar, rather than each crate inside it. All you need to do, then, is to look at SNP that’s diagnostic for the chromosome chunk that your disease-causing SNP is sitting on, instead of having to find the causal SNP itself. The technical word for these chunks or boxcars is haplotype³, and such catalogs when built are called haplotype maps; it’s actually what was meant by “medical genetic reference database” in the title of this post. It’s for the same purpose that the International HapMap Project, which has built reference haplotype maps for African, Asian, and European populations, was conceived, and it’s the origin of the name HapMap.
3. Haplotype is short for haploid genotype. Haploid means that you’re concerned with one chromosome, so haplotype means a contiguous segment from a single chromosome. Diploid means two (paired) chromosomes; humans are a diploid organism, because our chromosomes come in pairs. The fun doesn’t stop there. Some organisms are tolerant of higher ploidy, so there are tetraploid, hexaploid, and even octoploid species. For example wheat is hexaploid, so it has six copies of each chromosome.

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As the director of Britain’s Wellcome Trust Center for Human Genetics, Peter Donnelly oversees research that provides vital raw material for the 23andMe Personal Genome Service™ — specifically, correlations between one-letter DNA variations, known as SNPs, and particular diseases.

Those correlations are made by research projects known as genome-wide association studies (GWAS), which scan the genomes of people with a disease and similar individuals who don’t, then look for statistically significant genetic differences between the two groups. The studies can be enormous — last year the Wellcome Trust published the results of a GWAS that analyzed data from 17,000 participants and found genetic associations with seven different diseases — coronary heart disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, Crohn’s disease, bipolar disorder and hypertension.

In a commentary published this week by Nature, Donnelly assessed what GWAS have been able to achieve so far, and argued that in most cases, people should be able to gain valuable information from having access to their own genomes.

Here’s Donnelly’s reasoning in his own words:

For a particular disease, most individuals will have inherited some sequence variants that confer risk and some variants that provide protection, and they will therefore have an overall risk around average. A small proportion of people, however, will have inherited mainly variants that confer risk of developing the disease. Using Crohn’s disease as an example … the top 5% of the UK population … have a 5—8-fold higher risk than average of developing the disease, whereas the top 1% have a 9—15-fold higher risk.

Using statistics from the US, that amounts to a risk increase from the poplation average of less than 0.5% to somewhere in the neighborhood of 4-7%. Performing the same calculations for type 2 diabetes, Donnelly concludes that 1% of the population would end up having a 4-fold risk increase, which amounts to about a 50/50 chance of developing the disease.

“Across 50 diseases,” Donnelly continues (23andMe provides complete estimates of lifetime genetic risk due to known SNPs for 10 common diseases),

making the simplifying assumption that susceptibility to each disease is independent of susceptibility to every other disease, almost everyone will be in the top 5% of risk for at least one disease, and nearly half of all people will be in the top 1% for at least one disease. … Given that it is possible to reduce the risk of developing certain diseases, I can see the value of knowing about the genetic risks now.

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