We often hear about the &#8220;nature versus nurture&#8221; debate, framing it as a battle between our genes and our environment. But in many cases it&#8217;s not a battle at all, it&#8217;s a partnership. The way our genes impact our health and traits may change depending on our environment.



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1024" height="684" src="http://35.88.189.111/wp-content/uploads/2022/09/GettyImages-981323210-1024x684.jpg" alt="" class="wp-image-30209" srcset="https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2022/09/GettyImages-981323210-1024x684.jpg 1024w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2022/09/GettyImages-981323210-300x200.jpg 300w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2022/09/GettyImages-981323210-768x513.jpg 768w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2022/09/GettyImages-981323210-880x587.jpg 880w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2022/09/GettyImages-981323210-1180x788.jpg 1180w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></figure>



Understanding both &#8220;nature&#8221; and &#8220;nurture&#8221; is especially important when we consider complex skills critical to success in life, like reading. Thanks to the contribution of over a million 23andMe® research participants, a <a href="https://pubmed.ncbi.nlm.nih.gov/40991152/" target="_blank" rel="noreferrer noopener">new study</a>, led by scientists from the University of Edinburgh, is helping to untangle how both genes and environment work to shape literacy.



<h2 class="wp-block-heading" id="h-the-reading-puzzle-nbsp">The Reading Puzzle&nbsp;</h2>



Literacy is a powerful predictor of life outcomes, from education level to income. For decades, researchers have tried to understand the roles genetics and environment play in why some people learn to read and spell more easily than others.



<figure class="wp-block-image alignleft size-full is-resized"><img loading="lazy" decoding="async" width="425" height="282" src="http://35.88.189.111/wp-content/uploads/2008/07/twins.jpg" alt="" class="wp-image-392" style="width:309px;height:auto" srcset="https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2008/07/twins.jpg 425w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2008/07/twins-300x199.jpg 300w" sizes="auto, (max-width: 425px) 100vw, 425px" /></figure>



Several <a href="https://pubmed.ncbi.nlm.nih.gov/23725549/" target="_blank" rel="noreferrer noopener">studies</a> of <a href="https://pubmed.ncbi.nlm.nih.gov/19076484/" target="_blank" rel="noreferrer noopener">twins</a> in the United States suggested an interaction between genes and environment. In these studies, how much influence genetics had on reading skills seemed to be higher in families with a higher socioeconomic status. In other words, genetics appeared to have a bigger impact on a child&#8217;s reading ability when they were in a more advantaged environment. This idea is called a gene-by-environment interaction.



However, the evidence of this gene-by-environment interaction was mixed in studies conducted in the <a href="https://pubmed.ncbi.nlm.nih.gov/19296213/" target="_blank" rel="noreferrer noopener">United Kingdom</a> and <a href="https://pubmed.ncbi.nlm.nih.gov/28832969/" target="_blank" rel="noreferrer noopener">Australia</a>. This puzzle led researchers to ask: can we use modern genetic data to test this theory in large, diverse populations?



<h2 class="wp-block-heading" id="h-a-new-approach">A New Approach</h2>



Researchers utilized summary statistics from a 2022 <a href="https://blog.23andme.com/articles/study-identifies-genetic-links-to-dyslexia" target="_blank" rel="noreferrer noopener">genome-wide association study (GWAS) on dyslexia</a> to construct a polygenic score—a way of estimating someone’s genetic tendency toward a trait by adding up the small effects of thousands of DNA differences.



The team then applied this polygenic score to two large groups of children and adolescents in Australia and the United Kingdom to see if the score&#8217;s effect on reading and spelling skills was moderated by their family&#8217;s socioeconomic status. Socioeconomic status was used as a broad measure of environmental advantage, capturing factors like parental education level and household income.&nbsp;



<h2 class="wp-block-heading" id="h-what-was-discovered-two-parallel-paths">What Was Discovered: Two Parallel Paths</h2>



Both the dyslexia polygenic score (nature) and socioeconomic status (nurture) were significant predictors of reading and spelling performance. Together, demographic, genetic, and socioeconomic factors explained between 8% and 11.6% of the variation in reading skills.



However, the study found no interaction effect. Nature and nurture were both important, but they worked in parallel.



This means the influence of a person&#8217;s genetic predisposition was the same, regardless of their family&#8217;s socioeconomic background. In these groups, a higher genetic predisposition for reading provided a similar boost to children from all socioeconomic backgrounds.&nbsp;



<h2 class="wp-block-heading" id="h-the-bigger-picture">The Bigger Picture</h2>



This finding is an important reminder to scientists to consider different environments when looking for gene-by-environment interactions. While some earlier US-based studies suggested an interaction, the different environments in these large UK and Australian groups showed a different outcome. Suggesting that both our genes and our environment make their own distinct contributions to the development of this fundamental skill.



Of course, every study has its limitations. The 23andMe research participants who contributed to the original GWAS are a self-selecting group and may not be perfectly representative of the general population. Additionally, only about 5% of 23andMe research participants said they had dyslexia, which is on the lower end of the <a href="https://pubmed.ncbi.nlm.nih.gov/18652545/" target="_blank" rel="noreferrer noopener">commonly cited 5–10% range</a>.



Even so, this large-scale study provides a crucial piece of the puzzle, emphasizing how vital it is to test scientific findings in diverse environments to get the full story.

Nature and Nurture: A New Look at the Genetics of Reading Ability

This week the American Society of Human Genetics (ASHG) is having its <a href="https://meetings.ashg.org/event/ASHG25/home/" target="_blank" rel="noreferrer noopener">annual meeting</a> in Boston. Attending the ASHG annual meeting is about more than just presenting our work, it&#8217;s one way we are working to ensure that the 23andMe® Research Institute contributes to a healthier future for everyone.



<ul class="wp-block-list">
<li>Amplifying impact: Sharing our discoveries with a global audience helps our work reach those who can build upon it, accelerating the path from discovery to clinic.</li>



<li>Fostering collaboration: It&#8217;s where we connect with the brightest minds in the field. These conversations spark new ideas and forge partnerships that are essential for tackling complex diseases.</li>



<li>Staying on the cutting edge: Exposure to the latest technologies and unpublished data keeps our research relevant and innovative.</li>
</ul>



<h2 class="wp-block-heading" id="h-23andme-research-institute-at-ashg">23andMe Research Institute at ASHG</h2>



For those attending, stop by our posters in the following sessions:



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WEDNESDAY, OCTOBER 15TH 2:30 &#8211; 4:30 PM (Posters; Board #2004W) Sustaining Research Ethics in Times of Change: A Framework from the 23andMe Research Program



presented by Emily DelloRusso, Principal Regulatory Science Manager
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THURSDAY, OCTOBER 16TH 2:30 &#8211; 4:30 PM (Posters; Board #2035T) Explore23: a privacy-forward, secure, extensible, and easy-to-use web application for exploration of a large genomic research cohort



presented by Julie Granka, Director, Statistical Genetics
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FRIDAY, OCTOBER 17TH 2:30 &#8211; 4:30 PM (Posters; Board #3014F) Learning about a genetic predisposition to depression is psychologically well tolerated among those with high polygenic risk scores



presented by Rebecca Berns, Sr. Product Scientist I, Genomic Health and Sciences
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Interested in reading more? 23andMe has contributed to <a href="https://research.23andme.com/publications/" target="_blank" rel="noreferrer noopener">more than 300 research publications</a>.

23andMe Research Institute Attending ASHG 2025

Cannabis is already one of the most widely used drugs globally, and its use will likely grow even more as it becomes more socially accepted and legally available. For many the recreational use of cannabis poses few problems, but for as many as a third of users, it becomes problematic developing into an addiction or what scientists call cannabis use disorder. Research suggests that genetics may explain why.



<h2 class="wp-block-heading" id="h-searching-the-genome-for-clues">Searching the Genome for Clues</h2>



In collaboration with 23andMe Research Institute, researchers at the University of California, San Diego <a href="https://www.nature.com/articles/s41380-025-03219-2" target="_blank" rel="noreferrer noopener">published a study</a> today that found genetic links to cannabis use as well as links to psychiatric, cognitive, and physical health. The researchers used data from more than 130,000 people who consented to participate in 23andMe research. Participants answered survey questions on whether they had ever used cannabis (“lifetime use”) and, for those who had, how often they used it during their heaviest 30-day period (“frequency of use”). The team then conducted genome-wide association studies searching for genetic variants linked to ever trying cannabis and how often people used the drug.



<h2 class="wp-block-heading" id="h-what-the-data-revealed">What the Data Revealed</h2>



Researchers found that having ever used cannabis was associated with two genetic regions: CADM2—a gene <a href="https://blog.23andme.com/articles/scientists-identify-genes-associated-with-cannabis-use" target="_blank" rel="noreferrer noopener">previously linked</a> to cannabis and other substance use, impulsivity, and risk-taking—and GRM3. While GRM3 has been tied to alcohol use, schizophrenia, and certain personality traits, this is the first time it’s been linked to cannabis use. How often someone used cannabis was associated with a different variant also near CADM2, strengthening this gene’s role in cannabis behaviors. Both having ever used cannabis and how frequently someone used cannabis were genetically linked to other substance use, pain, and psychiatric conditions like depression. Researchers also created polygenic scores—which tally the effects of many different genetic variants—to estimate both the likelihood of ever having used cannabis and the frequency of cannabis use. These polygenic scores were able to replicate associations between cannabis use and substance use and mood disorders in two other independent research datasets (All of Us and BioVU). The researchers also uncovered novel associations with anxiety, celiac disease, HIV, and viral hepatitis.



<h2 class="wp-block-heading" id="h-why-this-research-matters">Why This Research Matters</h2>



Most genetic research on cannabis has focused on two points in the spectrum: first use and cannabis use disorder. But the stages in between—like how often someone uses cannabis—are just as important. Studying these “pre-addiction” traits may help determine who is more likely to progress from a casual user to someone with cannabis use disorder. This study was one of the first genome wide association studies to look at traits that may lead to cannabis use disorder.&nbsp;



Traits like frequency of cannabis use can reveal genetic factors that shape vulnerability before addiction develops. Understanding these factors could eventually help predict risk for cannabis use disorder and related health conditions.



<h2 class="wp-block-heading" id="h-more-than-just-dna">More Than Just DNA</h2>



Like all research there are some important considerations to keep in mind for this study:



<ul class="wp-block-list">
<li>While this research focused on genetics, many non-genetic factors may also play a significant role in a person&#8217;s likelihood of using cannabis or developing cannabis use disorder.</li>



<li>The study was conducted in people of European descent (based on an analysis of their local genetic ancestry), so the findings may not fully apply to people of other ancestries.</li>



<li>The measure of frequency (use days in a 30-day window) of using cannabis doesn’t capture full patterns such as the duration of regular use or quantity consumed.</li>



<li>While GRM3 is a promising lead, more work is needed to understand how the genetic variant in this region affects cannabis use biology.</li>
</ul>



<h2 class="wp-block-heading" id="h-the-road-ahead">The Road Ahead</h2>



Future research will need to expand to larger and more diverse cohorts and include other cannabis-related traits such as craving, withdrawal, and response to cannabis. Combining genetic insights from multiple stages of cannabis use could:



<ul class="wp-block-list">
<li>Improve the discovery of new genetic variants</li>



<li>Help build predictive models for problematic use</li>



<li>Clarify how cannabis genetics intersect with physical and mental health</li>
</ul>



Takeaway: Studying genetic influences on early cannabis use behaviors could eventually help predict risk for cannabis use disorder and related health conditions, and may help shed light on who is most at risk for cannabis use disorder.&nbsp; These findings mark an important step toward understanding the genetics of cannabis use and its health consequences.

New Genetic Insights Into Cannabis Use

<h6 class="wp-block-heading" id="h-a-new-study-may-help-identify-and-more-quickly-help-the-60-of-people-with-major-depression-who-do-not-get-relief-from-their-initial-treatments">A new study may help identify and more quickly help the 60% of people with major depression who do not get relief from their initial treatments. </h6>



It is estimated that 60% of patients with major depressive disorder don&#8217;t find relief from their first medication, and as many as one-third develop <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC10503923/" target="_blank" rel="noreferrer noopener">treatment-resistant depression</a>, which is often defined as still experiencing symptoms after trying at least two antidepressants.



Researchers at Johnson &amp; Johnson and the 23andMe Research Institute used advanced machine learning techniques to accurately identify those at most risk for treatment-resistant depression, a massive breakthrough for those suffering with this condition.



The groundbreaking research, published this week in the journal <a href="https://pubmed.ncbi.nlm.nih.gov/41028019/" target="_blank" rel="noreferrer noopener">Scientific Reports</a>, used only self-reported information from about 23,000 people who consented to participate in 23andMe research. The model included a total of 101 predictive factors, including such things as age, symptoms that persist between episodes of depression, the duration of episodes of depression, stress levels, and suicidal thoughts. 



Using this predictive model, researchers were able to accurately predict those at the highest risk for treatment-resistant depression 78% of the time, according to the study.&nbsp;



The predictive model did not use either genetics or lab results, just questions that can be easily assessed by a clinician.



<h2 class="wp-block-heading">Why This Matters</h2>



<figure class="wp-block-image alignright size-full is-resized"><img loading="lazy" decoding="async" width="508" height="531" src="http://35.88.189.111/wp-content/uploads/2025/10/drug-bottle.png" alt="" class="wp-image-34304" style="width:161px;height:auto" srcset="https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2025/10/drug-bottle.png 508w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2025/10/drug-bottle-287x300.png 287w" sizes="auto, (max-width: 508px) 100vw, 508px" /></figure>



Typically, antidepressants used to treat major depression take a little time to work, up to a month or a month and a half. When a treatment does not work, clinicians often prescribe an alternative medication. When someone with depression fails to respond to at least two different medications, they are deemed to have treatment-resistant depression. In many cases, it can take around a year for someone to finally be diagnosed with treatment-resistant depression.



That long delay in finding effective treatment has a cost, not just in the time it takes to identify treatment but in the prolonged suffering for those in the throes of depression, unable to get relief.&nbsp;



The promise of the kind of predictive modeling described in this new study is that it would give both people with depression and clinicians who treat them a tool to flag those who might not respond to standard therapies.



Instead of waiting to see if initial treatments are effective, it may accelerate the process of finding alternatives. Additionally, this predictive model only utilizes patient-reported information; no expensive testing is required.&nbsp;



<h2 class="wp-block-heading">More About the Predictive Model</h2>



The model identified eight primary factors that predict treatment resistance, as reported by research participants. Each offers different dimensions of their depression.



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Participants reported on the different symptoms that persisted between the episodes of depression. They reported on how long each episode of depression lasted, and how many years they&#8217;d experienced these periodic episodes. Also included in the self-reported data were levels of stress, or whether they had recurring post-traumatic stress, and two critical factors that looked at more external stressors and life events. Also factored into the modeling was a participant&#8217;s age and whether they had suicidal thoughts. Finally, the model included the self-reported timing between episodes of depression, which was significant when the time between episodes was shorter.



Using these factors and advanced machine learning the researchers were able to accurately distinguish between those who were treatment-resistant and those who responded to treatment. An accurate way to identify if someone will or will not respond to treatment could translate into meaningful clinical benefits. It could mean the difference between waiting months for effective treatment instead of years.&nbsp;



<h2 class="wp-block-heading">More Personalized Depression Care</h2>



This research represents a crucial step toward more personalized mental health treatment and a move away from a &#8220;one-size-fits-all&#8221; approach to treatment of depression.&nbsp;



For the millions of people living with depression, this work represents a path toward precision care, shortening the time to effective treatment and improving quality of life.



The future of depression treatment lies not just in developing new medications, but in better understanding who needs what treatment when. This predictive model brings us measurably closer to that goal, promising a world where effective depression treatment is not a matter of luck, but of precision.



If you or someone you know is struggling with depression, please reach out to a mental health professional. The <a href="https://988lifeline.org/" target="_blank" rel="noreferrer noopener">National Suicide Prevention Lifeline</a> (988) provides 24/7 support for those in crisis.

Predicting Treatment-Resistant Depression Using the 23andMe Survey Data

<div id="attachment_25595" style="width: 294px" class="wp-caption alignright"><img loading="lazy" decoding="async" aria-describedby="caption-attachment-25595" src="https://blog-api.23andme.com/wp-content/uploads/2017/08/Fah-284x300.jpg" alt="" class="wp-image-25595 size-medium" width="284" height="300" srcset="https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2017/08/Fah-284x300.jpg 284w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2017/08/Fah.jpg 346w" sizes="auto, (max-width: 284px) 100vw, 284px" />Fah getting ready for his trek to Everest base camp. Everest can be seen in the background.</div>
On vacation trekking the Himalayas with his partner, 23andMe computational biologist Fah Sathirapongsasuti didn’t just take snapshots of surrounding peaks, he also did a little science at 17,000 feet.
As he hoofed along the trail to Everest base camp, Fah recorded the toll of hiking at altitude took on his body, monitoring his heart rate, the oxygen saturation in his blood, the number of calories he burned, and his disrupted sleep patterns.
“There were a few days that I felt pretty miserable,” he said.
Collecting data on vacation might not be on the top of your trip itinerary, but Fah saw an opportunity. He saw a chance to record in real time and with real data, the impact of altitude on the human body. And, he thought if enough people like him recorded their data and pooled it together, perhaps he could learn something more about why some of us do better than others at higher elevations. (You can see morea about how he tracked his data <a href="https://scholar.harvard.edu/fah/science-little-altitude">here</a>, or look at the sidebar to learn more about how you could track your own data if you’re planning a trip to higher elevations.)
“I don’t think you need to be a geek or a scientist to enjoy collecting data during your vacation,” said Fah. “Our ability to adapt to foreign environments such as high altitude is a testament to the wonder of the human body. Quantifying it will help you see and appreciate your own body more.”

<div style="float: right; text-align: justified; width: 350px; background-color: #eee; padding: 15px; margin-left: 10px;">
Fah&#8217;s DIY Altitude Science Field Guide 
I don’t think you need to be a geek or a scientist to enjoy collecting data during your vacation. Our ability to adapt to foreign environments such as high altitude is a testament to the wonder of the human body. Quantifying it will help you see and appreciate your body more. Plus, monitoring your blood oxygen level (SpO2) during high altitude trekking can literally save your life!
My data from Everest Base Camp Trekking can be found <a href="https://docs.google.com/presentation/d/1LQA3bKR-fcssExZcvDPMyKOgIzIgahPcjjP12krvqjY/edit?usp=sharing">here</a>. If you are planning a trip up the mountains, to elevations higher than 9,000 feet, I’ve outlined how you too can track this data.
What to track?
If you are traveling to a “very high altitude” — defined as anything higher than 11,500 ft (e.g. Mt Whitney, Machu Picchu, or Mount Kilimanjaro) — there are a few experiments you can try. 
I found that the oxygen saturation in my blood, now easily measured using a pulse oximeter that can be clipped to your finger and also tracks your pulse rate. 
The key here is to record at three consistent times each day:

<ol>
<li>On arrival at your resting point (e.g. campsite or , lodge)</li>
<li>Before bed</li>
<li>Morning, within the first hour after waking up</li>
</ol>
When recording the data, you should also make note of the elevation, and whether you have any symptoms of altitude sickness. For me, that amounted to nausea, dizziness, restless sleep and blurred vision.  Beyond those three key points of measuring your blood oxygen, you should also measure immediately after exertion, that could include hiking or climbing or skiing. Whatever you are doing at elevation that is physically taxing.  Record the exercise. Measure your heart rate and SpO2 immediate after exercise. Keep repeatedly measuring every 30 seconds after you’ve finished the “exercise” until your heart rate returns to normal. For me, this was instructive because I was able to see that I could recover even from the most strenuous exertions relatively quickly with a relatively  If you are going to a moderately high altitude location (4,900-11,500 ft), such as Aspen or Lake Tahoe, it is less likely that you will experience altitude sickness., But you may observe changes in your physiology especially during an exercise.  You might consider doing some strenuous exercise such as a sprint and measure your SpO2.  You might be surprised by how low it can get.
What You’ll Need 
Like any good experiment you’ll need a few things to ensure you can record good data. You&#8217;lld need a good pulse oximeter.  There are several brands.  The one I used is iHealth Air Pulse Oximeter available on <a href="https://www.amazon.com/iHealth-Pulse-Oximeter-Apple-Android/dp/B00D7MDXCU">Amazon</a>. In addition you&#8217;ll need a fitness tracker.  There are several acceptable, each with its own features and quirks.  I use Fitbit Charge HR, mostly for the balance between price and features.  Make sure it has continuous heart rate monitor and accelerometer (for steps and elevation gain estimate). 
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Although we can acclimatize to exertion at elevation, some people adapt to life at higher altitudes better than others. Sherpas, the ethnic group that has lived for thousands of years in the Himalayas, have a genetic advantage for life in the thin oxygen air of their homelands. So do Tibetans. Their genetic advantage is something they inherited from our ancient human cousins, the<a href="https://www.nature.com/nature/journal/v512/n7513/full/nature13408.html"> Denisovians</a>, more than 40,000 years ago. Variants in two genes EPAS1 and EGLN1 are prevalent in Tibetans. Among everyone else, it’s hard to predict who will be get sick at high elevations and who won’t. 
Most people know that climbing Everest is fraught with peril, but fewer know that just trekking to base camp can pose a danger due to altitude sickness. Although there aren&#8217;t accurate statistics on the number of trekkers hit by altitude sickness on the trail, a few die each year from what’s called Acute Mountain Sickness or underlying conditions that are exacerbated by exertion at altitude. But currently there is no good way to determine who might get sick and who won’t.
Fah and his partner were careful and prepared for their trek. Both of them are in shape, but even so they had days that were very difficult. Fah thought this was likely so he wanted to record the data to see how his body changed during the hike, and to see how it affected him differently than others.
“I know what I felt, but I wanted to see the data too,” he said.
There were some obvious data points. The higher Fah went, the lower the oxygen saturation in his blood.
“Duh,” he joked.
Although it wasn’t surprising, the data dramatically illustrated the impact of altitude. As Fah crossed the pass between Lobuche and Gorakshep at more than 16,000 feet, his oxygen saturation dropped below 80 percent, and his resting heart rate spiked upward. He got headaches, felt dizzy and his vision blurred. Fah slept less. None of that is atypical. In fact having your oxygen saturation drop below 80 percent for an extended period is very dangerous.
Over the week long trip, Fah slowly acclimatized to the higher elevations.  And his numbers got better, but in general, there was a direct correlation between how bad he felt and elevation gain. Beyond what he felt and what he was recording, Fah also knew that there are many studies <a href="https://www.nap.edu/read/5197/chapter/24"> on this</a>.
Out of curiosity Fah also added one other data point, he measured the oxygen saturation of three of the Sherpas helping to ferry some of the supplies for the trekkers. On average their oxygen levels were three to five percentage points higher than Fah, and they were much more comfortable.
As Andrew Murry, a physiologist at the University of Cambridge in the UK <a href="https://www.npr.org/sections/goatsandsoda/2017/05/28/530204187/the-science-behind-the-super-abilities-of-sherpas">recently told NPR</a>:
“You don&#8217;t need to spend very long in that part of the world to see that the people living there, particularly the Sherpas, perform extremely well at altitude — a lot better than we do.&#8221;
Over time many of us can acclimate ourselves to exertions at higher altitudes, improving our blood oxygen saturation levels, for instance. And Fah was able to see how, even in the short time he was in Nepal, his body acclimated itself to the higher elevations.
<img loading="lazy" decoding="async" src="https://blog-api.23andme.com/wp-content/uploads/2017/08/Graph-SpO2-1024x612.png" alt="" class="aligncenter wp-image-25596 size-large" width="1024" height="612" srcset="https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2017/08/Graph-SpO2-1024x612.png 1024w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2017/08/Graph-SpO2-300x179.png 300w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2017/08/Graph-SpO2-768x459.png 768w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2017/08/Graph-SpO2-880x526.png 880w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2017/08/Graph-SpO2-1180x705.png 1180w, https://blog.pub.us-west-2.prd.23andme.us/wp-content/uploads/2017/08/Graph-SpO2.png 1388w" sizes="auto, (max-width: 1024px) 100vw, 1024px" />
Oxygen saturation (SpO2, gray line) decreased as the elevation (blue line) increased. Symptoms of altitude sickness such as headaches, blurry vision, and sleeplessness are common when SpO2 dropped below 80, between Day 5 and Day 8.
But even acclimatizing won’t entirely relieve the symptoms and it’s still difficult to predict why some people get sick, and altitudes and others do not. So Fah decided to invite others with plans to trek at elevation — 11,000 feet or higher — to record their data.
“The lesson I learn as a scientist at 23andMe is that crowd-sourcing can give us the power to make a discovery at an unprecedented rate and scale. Together we might be able to discover a way to predict and prevent altitude sickness,” he said.

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