For as long as humans have lived in complex communities, cities and civilizations, they have divided and classified their societies. Those divisions have been based on age, gender, appearance or – in many cases – occupation. In many traditional societies artisans would share the same social status; as would soldiers, priests and workers in any number of other occupations.

In antiquity, the status of a family rarely changed. If you were a farmer, your sons would be farmers, and so on. While today social status barriers are crumbling in many societies, in others they remain largely unchanged.

India’s complex social stratification, known as the caste system, has been one of the traditional cornerstones of society. Though urban Indians are shedding the caste labels of their parents and grandparents, many rural Indians – who make up 72% of the entire population – hold steadfast to the system. In small villages and towns, the Brahmin caste – consisting of scholars and priests – is still revered as one of the highest social strata. And members of the Dalit caste – formerly known as “Untouchables” – are still viewed as unclean and remain separated from others.

The rigidity of the system still present in rural India has made many wonder exactly how long castes have existed. Historical records are unclear, as early Hindu scriptures like the Bhagavad Gita are somewhat ambiguous when it comes to the topic. Some historians even propose that the caste system as we know it today is largely a construct of the English Colonial Era, arguing that the development of such a system could have been deemed necessary to instill order.

Genetic analysis has also proven inconclusive, as analysis of small segments of the human genome has yielded different results. But a new study by geneticist David Reich and colleagues, published in the September 24 issue of Nature, takes a new approach to understanding the genetic history of India.

The core difference between Reich’s genetic analysis and previous studies is in the sheer amount of genetic material analyzed. Reich’s team examined more than 550,000 points across all segments of the human genome. In doing so, they hoped to obtain a more complete picture of Indian genetic history.

The research team analyzed the DNA of 132 individuals from India and neighboring regions, dividing them into 25 distinct groups based on geography, caste and language. They calculated how genetically ‘closed’ each of these groups were. In the caste system it is rare to marry someone from another class, making caste societies very closed, or ‘endogamous.’ If this endogamy continues over many generations, it will leave a behind a genetic signature for scientists to discover.

Reich and his team found such a signature, indicating a long history of endogamy in several of the groups. In fact, the research team calculated that the DNA of six of the groups can be traced back to just a few individuals who lived anywhere from 30 to more than 100 generations ago. Assuming a generation time of 25 years, that establishes the existence of the caste system in the range of 750 to more than 2,500 years ago — long before the British colonial era.

In a second analysis, Reich and his team examined how ancient migrations could have influenced the formation of castes. First the researchers divided the Indian groups into language families: Indo-European and Dravidian. Dravidian tongues, like Tamil and Malayalam, are mainly spoken in southern India and are believed to be a remnant of languages spoken by some of the earliest inhabitants of the region. Indo-European languages, like Punjabi and Urdu, are more common in the north. They are believed to have arrived with a migration of farmers from southwestern Asia or the Near East about 9,000 years ago.

Reich and his colleagues then compared the genetics of each of the Dravidian and Indo-European groups to a sample of European DNA. The team reasoned that, if Indo-European groups were really descended from the farmers, they would show more genetic similarity to the Europeans than the Dravidians.

Not surprisingly, the authors’ hypothesis held true. The Indo-European speakers, like the Kashmiri Pandit and Vaish, were more genetically similar to Europeans. And because the majority of the upper castes speak Indo-European languages, while the lower ones tend to be Dravidian speakers, there could be a relationship between the arrival of Indo-European people and the formation of caste structure. Further evidence that an ancient caste system has permeated through India for thousands of years.

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The genetics of height is incredibly complex. Though height is strongly determined by genetics, no single gene appears to have much influence over how tall a person will be. Instead, there appear to be dozens of genetic variants involved, none of which account for more than a few millimeters’ height difference.

It will be no small feat for geneticists to identify all of the genetic contributors to height and out how they work together. But by analyzing growth data for a group of 3,538 people who were born in northern Finland in 1966, an international group of researchers has taken a step in that direction by figuring out when a few height-related genes exert their effects. They describe their research in the March issue of PLoS Genetics.

Humans tend to do most of their growing in two spurts, once during infancy and then again around the onset of puberty. Growth during infancy tends to be associated with how much nutrition a child absorbs, whereas the second growth spurt is governed more by hormonal signals. So it would not be surprising to find that different sets of genes are associated with growth during the two periods.

The researchers tested that theory by selecting 48 SNPs that have been associated with adult height in previous studies. Then they looked at growth data for each subject from birth to age 20 to determine whether any of those SNPs was associated with growth during infancy, around puberty or both.

Five SNPs were significantly associated with growth during infancy, and seven with the puberty-associated growth spurt. Only one was associated with growth during both periods.

The researchers were especially interested in two SNPs. One was in the gene SOCS2, which influences growth hormone signaling, and was associated with growth during puberty. The other was in the gene HHIP, which is involved in embryogenesis and development, and was associated with growth during infancy.

(23andMe customers can check their genotype for both of these SNPs using the Genome Browser. Each T version of the SOCS2 SNP rs11107116 was associated with faster growth during infancy, and 4.7 mm of adult height. Each A version of the HHIP SNP rs6854783 was associated with faster growth around the time of puberty, but was not significantly related to adult height.)

This research illustrates the value of collecting data over time in genetic association studies. Knowing not just whether, but when, a particular genetic variation exerts its effects can provide vital clues to how it works.

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A new genome-wide association study has yielded two more genes associated with height. But just like the amount of height difference the new genes explain, their discovery amounts to no more than an inch of progress in understanding the genetics of the trait.

Height is one of the most genetically complex traits scientists face. There are probably hundreds of different genes that influence stature, with each one accounting for only a tiny fraction of the difference in height from one person to the next. And though genes are by far the most important determinant of height, all other things being equal, all other things are by no means equal. Differences in childhood nutrition, prenatal care and infection history can cause even identical twins can to reach very different heights (more on that here).

Discerning the effect of a single genetic variation in such a noisy environment requires multiple studies involving large numbers of subjects. The latest study, published online last week by Human Molecular Genetics, looked at 379,319 SNPs in 1,000 people of European descent.

The study found statistically significant associations with height for 134 SNPs. The researchers focused on about two dozen that clustered around two genes that had not previously been associated with height, SBF2 and FLNB.

FLNB is plausible as a height-associated gene, because it is involved in skeletal development. Though SBF2 is not as clearly related to height, mutations in the gene are known to cause Charcot-Marie-Tooth disease, which causes muscle weakness and skeletal deformities in the limbs.

The effect of SBF2 was surprisingly large — subjects with the TT genotype at one SNP in the gene, rs1867138, were about an inch taller on average than those with the CC genotype. Though the effect of FLNB was less impressive, the study found that each G at the the SNP rs9834312 in that gene slightly increased a subject’s height.

23andMe customers can check their genotypes at two equivalent SNPs using the Browse Raw Data feature. Having a G at rs1867137 is the same as having a T at rs1867138. So the GG genotype at rs1867137 is worth about an inch compared to AA. In FLNB, rs939882 is equivalent to rs9834312. In both cases, having a G is correlated with slightly greater height.

When the researchers attempted to replicate their findings in Chinese subjects, they found that both genes were associated with height in that population as well — but not all of it. The SNP in FLNB was associated with height among 619 northern Chinese subjects, while the SNP in SBF2 was associated with height only in a sample of 2,953 southern Chinese.

But when the researchers tried to replicate previously discovered height associations using their data, their results were mixed. Out of 58 they looked for, only 13 showed up. That doesn’t necessarily mean the 45 associations that could not be replicated are invalid; but it does illustrate how difficult it will be to work out the genetic basis of height.

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“I feel like a fish with no water.”

That’s how the child in the public service announcement featuring a flopping, gasping goldfish describes what it feels like to have an asthma attack. The spot encourages parents of the close to nine million kids who suffer from asthma to take steps to decrease the number of attacks their kids have because, as the announcer says, “even one attack is one too many.”

Both environmental factors – things like mold, pollen, cold air and cigarette smoke — and genetic factors are thought to contribute to childhood asthma, a disease that causes more than 10 million missed days of school and leads to about two million trips to the emergency room each year.

Researchers are beginning to realize that asthma that strikes early in life may be a distinct disease from cases that set in later. In 2007, a genomewide association study linked several related SNPs in a region of chromosome 17 specifically to childhood asthma. New research published online today in the New England Journal of Medicine confirms these genetic findings and adds a new twist: the SNPs have an effect only in kids exposed to tobacco smoke.

(23andMe customers can check their data for one of the chromosome 17 SNPs associated with childhood asthma in Research Reports.)

Bouzigon and colleagues studied several hundred French families and found a number of SNPs linked with asthma on chromosome 17, just as in earlier studies. The SNPs were associated only with asthma that began at age four or younger.

Like previous researchers, Bouzigon et al could not determine exactly how the SNPs are related to childhood asthma risk; but they could clearly show that the variants’ effects are triggered by exposure to tobacco smoke. The researchers found that the overall risk of early-onset asthma was increased at least 1.7-fold for people with two risky copies of any of the SNPs compared to those with none or only one. But when they took smoking into account, they saw that children with two risky copies of any of the SNPs who were also exposed to tobacco in utero, during infancy or both actually had risk at least 2.3-fold higher than those with the more favorable genotypes. The SNPs had no statistically significant effect among children who were not exposed to tobacco smoke early in life.

In an accompanying editorial in NEJM, two researchers not involved in the study — John Holloway and Gerard Koppelman — say that the influence of early life events in the development of asthma is well supported, and that the interaction of genes and tobacco smoke seen by Bouzigon and colleagues “further highlights the detrimental effect of such early life exposure on respiratory health.”

But, say Holloway and Koppelman, the importance of the exact timing of exposure to tobacco smoke is not clear, as Bouzigon et al could not differentiate between prenatal and postnatal exposure.

Although more research will be needed to confirm the results in this new report, Holloway and Koppelman think that as more is learned about how chromosome 17 variants affect asthma risk, this knowledge may help physicians better treat the disease.

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For years I believed that every Mexican restaurant my family took me to had some kind of problem with their dishwashing machine. Why else would the food always taste like soap? No one around me seemed to notice, so I just assumed that everyone else liked the taste of dirty dishwater in their food.

Fast-forward several years to college: I’m at dinner with some friends, and a girl named Alice asks the waiter “Could you not put cilantro on that? It tastes like soap to me.”

I was so excited. Not only had I found someone who had experienced the soapy food phenomenon, I now had an answer for what was causing it: Cilantro.

It turns out that Alice and I aren’t so special. There are lots of people who hate cilantro. Some, like us, think it tastes like soap. Others think it tastes like doll hair, stinkbugs, or burnt rubber. (See ihatecilantro.com for a long and colorful list of descriptors, as well as stories from fellow cilantro-haters).

There’s some evidence that hating cilantro (or as I like to think of it, realizing how gross cilantro truly is) is genetic. One study found that identical twins (who share 100% of their genes) are more likely to have the same opinion of cilantro compared to fraternal twins.

Could there be a SNP that explains why I hate cilantro but my husband loves it, just as there are SNPs behind my wet earwax and ability to drink milk with no lactose intolerance problems? This is the kind of question we hope 23andWe can answer.

By taking 23andWe surveys, customers can help us advance scientific research. We’ve already asked about which hand and eye they use more, whether they’ve ever had any cavities, and of course, whether they can smell anything in their pee after they eat asparagus. In the coming months, there’ll be plenty more surveys. And as we move forward, we’ll also be asking about more serious traits related to health and disease risk.

23andWe is consumer-enabled research (CER™). So we want to ask you, our customers and potential customers: What do you want to know about? Do you have a particular preference that you suspect is determined by your genes? Have you always wondered if some distinctive family characteristic is due to nature or nurture? Leave us a comment!

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It turns out that the riskier version of this SNP increases the amount of the FGFR2 protein that is made by cells, which can stimulate cells to grow – and increases the chance that they will proliferate out of control and lead to cancer.

Though the number of genome wide association studies is growing every day, very few of these studies have been able to explain why the SNPs they find are associated with a particular disease. Understanding how SNPs impact the biology of cancer and other common diseases will be critical for developing new treatments.

The FGFR2 gene encodes a protein that is inserted through the surface of cells where it acts as a receptor for certain growth factors. A growth factor is a chemical signal in the body that can tell cells to divide, move around, or differentiate into more specialized types of cells.

While several genetic mutations have been found that confer a high risk for breast cancer (most famously BRCA1 and BRCA2), these known genetic changes are actually relatively rare. There are probably many more common genetic changes that on their own don’t pose much of a risk, but in certain combinations can cause disease. The SNP in FGFR2 was one of the first of these common variants to be found.

On average, a woman’s chances of developing breast cancer some time during her life are one in eight. Having two copies of the G version of SNP rs1219648 (instead of the most common genotype: AG) increase her chances to about one in six. Two copies of the A version of this SNP, on the other hand, reduce a woman’s chances to about one in 10. (These numbers apply only to women with European ancestry. 23andMe customers can learn more in My Gene Journal (now called Health and Traits).)
Meyer et al showed that the cells of women who had two copies of the G version of rs1219648 expressed significantly more FGFR2 than women with two copies of the A version (the study actually looked at another SNP that is thought to be universally linked to rs1219648).

It’s known that in 5 to 10% of breast cancers, the FGFR2 gene is turned on at unusually high levels. And laboratory experiments have shown expressing extra FGFR2 in cells can cause them to take on cancer-like traits.

When they investigated further, the researchers found that the G version of rs1219648 was associated with increased expression of FGFR2 because the versions of two SNPs (located very near rs1219648) that are inherited along with the G version of rs1219648 have a greater affinity for DNA-binding regulatory proteins that help turn on the FGFR2 gene.

Meyer et al believe that theirs is the first study to address the function of the SNPs associated with breast cancer.

“Our study demonstrates that SNPs identified by whole-genome scans can be used as valid starting points for studying the underlying biology of cancer,” the authors write.

Like many of the SNPs found in genome wide association studies, rs1219648 and other SNPs in FGFR2 that regulate the binding of regulatory proteins are in a non-coding part of the gene called an “intron”. Many other SNPs found in these types of studies are in non-coding regions of the genome located between genes.

The authors speculate that their finding that SNPs in non-coding parts of the DNA can regulate gene expression will be a common theme.

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The answer is, very certain. We typically claim that the genotyping technology we use reports the correct call for more than 99.9 percent of the approximately 580,000 single-letter DNA variations we report. Those variations, known as single-nucleotide polymorphisms, or SNPs, are the raw data upon which the 23andMe Personal Genome Service™ is built.

But apparently one of our customers decided to do more than ask. He designed a simple experiment to test the reproducibility of the SNP chips that 23andMe and other companies use to give people access to their genetic data.

Antonio C.B. Oliveira signed himself up for 23andMe and a similar service, then wrote a computer program to compare the genotype calls at the 560,299 SNPs where both companies reported data. He found a grand total of 23 discrepancies, or about 0.004% of the common reported SNPs.

“This error rate seems to me to be quite acceptable,” Oliveira concluded on Longa Vista, a blog he established to share his results.

Oliveira is actually the second customer to conduct such an experiment. When Ann Turner compared two datasets for the same person (not herself) back in January, she discovered 35 discrepancies.

And when Craig Venter and his colleagues published his full genome last October, they also found the discrepancies between their own sequencing results and two different SNP genotyping platforms to be negligible.

We aren’t surprised – these results confirm experiments that 23andMe performed before launching our Personal Genomics Service™ last year. But now 23andMe can say with confidence that independent testing has shown that the Illumina genotyping technology we use is extremely robust.

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A study published online yesterday in Science Express suggests that for schizophrenia at least, and perhaps other mental disorders, that approach might not be the way to go.

Schizophrenia is a debilitating psychiatric disorder that affects approximately one percent of the population. People with schizophrenia suffer from hallucinations, delusions, and disorganized thinking. The illness greatly impacts social and occupational functioning and has enormous public health costs.

The authors of the paper propose that a genetic predisposition to schizophrenia is caused by structural variations in the genome such as deletions, duplications, and re-arrangements of genetic material instead of variation at SNPs. Furthermore, they think that these structural variations might be different for different patients, meaning that it would be difficult to ever find DNA markers that are predictive for the disease.

The researchers used new technologies to look for structural variations in 150 people with schizophrenia or schizoaffective disorder and 268 healthy controls with no history of neurological or psychiatric illness. They found that individuals with schizophrenia were much more likely than controls to have structural variations that affected genes (as opposed to non-coding parts of the genome). The association was strongest in people who developed symptoms while 18 years old or younger.

Virtually every structural variant the researchers detected in the individuals with schizophrenia was unique, though sometimes patients had differing mutations in the same genes. Genes involved in brain development were the most affected.

The results of this study don’t prove that any one gene is associated with schizophrenia, but they do suggest that researchers who want to understand the genetics of this illness, and maybe other complex psychiatric disorders, should perhaps focus their efforts on structural variations instead of SNPs.

That doesn’t mean SNPs aren’t useful. Almost every day we’re learning something new about how subtle single-letter DNA variations between people may affect their health. But some questions won’t be answered until scientists have a better understanding of many other types of genetic variation as well – not to mention the contribution of other factors such as diet, personal habits and environmental exposures.

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