Tag Archives: genetics
While it is clear that exercise is beneficial, how does one decide what to do to get and stay fit? When Laila Zemrani surveyed people at the gym, she found that a majority don’t decide at all. Sixty percent didn’t know why they were doing a particular exercise. And of those, 50% admitted to merely copying whatever their neighbor was doing.
Laila spoke recently at a QS meetup in Boston about how she tried to be more intentional in her choice in exercise. In reviewing the number of available exercises, she was able to put them into two buckets: strength and endurance. She decided to track the effectiveness of each training regimen by focusing on a single metric and watching its progress. For strength, she focused on body fat ratio. For endurance, she looked out how long it took her to run the same distance. She then alternated her training every three months or so, focusing on one or the other.
Here’s what she found. When she focused on strength training, her body fat ratio improved. For instance, in one three month period it went from 29% to 25%. This type of improvement repeated itself a number of times. However, when she focused on endurance, she did not see improvements in the time it took her to run a certain distance.
It’s hard to know what conclusion to draw from these results. Are these the right metrics for assessing performance? What does it mean to respond more to strength than endurance exercise? However, the question of why Laila seemingly responds better to strength-based exercises may be found in her genetics. She used a DNA test from 23andMe and the results suggested that she shows a propensity toward building fast-twitch fibers which allow for better performance at explosive activities, such as sprinting or weight-lifting. On the flip side, people who are more proficient at building slow-twitch fibers tend to do better at endurance-type activities such as running long distance. Everyone has a combination of the two types of muscle fiber, but the ratio seems to be correlated with performance, depending on the type of activity.
With these results, Laila decided it made sense for her to focus on strength-building exercises, since it seems that her body was built for that type of activity. Laila feels that having this information is allowing her to personalize her regimen and be more intentional about how she exercises, rather than be too influenced by the latest fads in fitness.
It can be debated whether it makes sense to focus on strength as opposed to endurance, depending on which one you see progress in. For Laila, the appearance of progress is important psychologically, in that it is easier to motivate herself if she sees improvement. There could be a downside to appearance of quick improvement, though. Ralph Pethica also uses genetic data to inform his training. He is the opposite of Laila in that his body is better suited for endurance exercise. What he finds, though, is that he improves and adapts too quickly and sees his performance plateau. To overcome this, he found that switching between steady-state training sessions and high-intensity intervals minimized the time he spent plateaued.
Training with knowledge of your genetic background is still a nascent practice. It’s still unclear how this information can and should be used. Useful ways to take advantage of this genetic information is still being tested and developed, but progress could be hastened if more people knew if they had more slow-twitch or fast-twitch muscle fiber. If this awareness is increased, it could lead to better strategies to get more out of exercise and reduce frustration and, hopefully, abandonment of the gym.
Fitbit Aria Wi-Fi Smart Scale
QS17 Tickets are Available
Our next conference is June 17-18 in lovely Amsterdam. It’s a perfect event for seeing the latest self-experiments, debating the most interesting topics in personal data, and meeting the most fascinating people in the Quantified Self community. There are only a few early-bird discount tickets left. We can’t wait to see you there.
The DIY Scientist, the Olympian, and the Mutated Gene by David Epstein. There are several surprising twists in this story of a non-professional scientist named Jill Viles, who made an important discovery about her own rare genetic disorder. What inspired me mostwas Viles’ tenacious reliance on her own capacity to reason, even in the face of skepticism from professionals who had less knowledge (though more confidence) than she did. Eventually, she connects with highly technical scientists whose research direction she influences with her ideas. Epstein got a fantasic quote from one of them when he asked the scientist if this has ever happened before. “In my life, no,” he says. “People from outside coming and giving me hope? New ideas? I have no other example of this kind of thing. You know, maybe it happens once in a scientific life.” I found myself wondering if this kind of thing will be less rare in the future. -Gary
A Drug to Cure Fear by Richard A. Friedman. This article intersects two of my interests that stem from my own self-experimentation. From my stress tracking I realized that many of my reactions in my day-to-day life are influenced by traumatic memories. From my spaced repetition practice I learned how memories can change over time through retrieval and consolidation. A study done in the Netherlands suggests that a memory can be decoupled from an associated fear response by using propranolol which blocks the effects of norepinephrine, a chemical that strengthens connections in the brain. The study has yet to be replicated, but hopefully it will increase our understanding of trauma. -Steven
Internet of Things security is so bad, there’s a search engine for sleeping kids by J.M. Porup. Ever since doing a research project on data flows for our first Quantified Self symposium we’ve had what you might describe as a below average level of confidence in the security and reliability of information traveling outside the immediate context of its collection, now that APIs connect to APIs connecting to yet other APIs. Still, even I was surprised by the recklessness and potential harm described in J.M. Porup’s brief account of a search engine that displays random pictures from internet connected consumer cameras around the world. -Gary
Algae bloom toxin linked to Alzheimer’s, other diseases by Amy Kraft. One consequence of the climate change and the depletion of fish stocks in ocean’s is the increase occurrence of algae blooms. Ethnobotanists found a correlation between algal blooms and neurodegenerative diseases among remote populations in the Pacific. New research suggests that cyanobacteria, the microorganism in these blooms, has a neurotoxin that can cause neurodegenerative precursors that develop. This neurotoxin enters the human food chain as it bioaccumulates in fish and shellfish. -Steven
Glass Half Full Succeeds in Unwinding Upsets by Paul LaFontaine. Most people have moments of irritation or worry throughout the day. Paul wanted to find out what worked better as a response to these moments. Option A was to step back and observe his emotions in a manner similar to that taught by some schools of meditation. Option B was to figure out the source of irritation or concern and think of a positive angle to the situation. What is great about this post is the very simple but illuminating experiment that he devised to explore this question. -Steven
Finding My Optimum Reading Speed by Kyrill Potapov
As an English teacher Kyrill Potapov spends a lot of time working with 12 year old kids who are trying to improve their reading, writing, comprehension, and analytical skills. In this talk, he explores a remarkable method of speed reading, called Spritz, that promises to let you “read Harry Potter in three hours” with full understanding and recall. Could such a promise possibly be true? -Gary
Heart Rate Variability, Body Metrics, and Cognitive Function by Justin Lawler. This is a great examination of how Justin’s HRV measurements correlate to all other personal data he has collected. -Steven
Using Spectrograms to Visualize Heart Rate Variability by Randy Sargent
Randy’s idea about using spectrograms, normally used for audio signals, to create a portrait of your own time series data, is completely novel as far as I know. -Gary
Spurious Correlations by Tyler Vigen. An entertaining collection of unrelated facts that can be correlated with a high degree of confidence. -Steven
Hackers Diet, FIRECalc and weight loss by u/Thebut_. This chart is a mess, but the idea behind it is fascinating. This reddit user was inspired by FIRECalc, a financial tool that “projects your future assets based on historical market data” and tried to apply it to his weight data. Instead of giving a single projection, the tool shows a range of possibilities. This is similar to how Baseball Prospectus’s PECOTA system uses a weighted range of possibilities (probability distribution) rather than a single guess (point estimate) for forecasting a prospect’s future performance. I would like to see more of this kind of thinking applied to personal data. -Steven
Darwin Tunes by Bob MacCallum, Armand Leroi, Matthias Mauch, Steve Welburn, and Carl Bussey. A fascinating project that treats pieces of music like organisms that can mate and reproduce based on listeners’ votes. These audio loops started off as random noise, but as the generations moved into the thousands, the presence of chords and higher order melodies emerged. At this point, there have been over 8700 generations. You can take part yourself! -Steven
In our Access Channel we’re trying to expose ideas, efforts, and insights about personal data access and it’s role in both generating personal and public insights. The last time we wrote about data donation we mentioned a few different projects that allowed you to collect and/or publish your self-tracking data for others to view and access. Today we’re going to showcase a few research-focused projects that collect personal data, but also allow participants to access the data they contribute. This seemingly minor addition, participant access to data, is actually a process not commonly employed by research studies. We’re very interested in new participatory models of research that respect participant’s rights to fully understand and access the data they contribute. If you know of others please get in touch and we’ll add them to the list.
Personal Genome Project: Harvard
Probably the most well-known of these research projects is the ongoing Personal Genomes Project based at Harvard University (PGP). Led by George Church and an outstanding team, the PGP is an ongoing research project recruiting participants to “share their genetic, health, and trait data in a public and non-anonymous manner. Participation is free.
Much like the project above, the American Gut project is an open call for participant to collect and share their data. In this case it is human microbiome data. Although enrollment is not free (they request donations starting at $99 to participate) data is returned to participants. (If you’re interested in participating in microbiome research, but live in Europe see the British Gut project)
Dynamics of the Human Microbiota
This new project, based out of Stanford, is also exploring the human microbiome. This study includes a variety of different perturbations and longitudinal data collection. Participants are compensated for their participation, their data is made accessible to them, and they have the opportunity to discuss their results with the study staff.
For those of you interested in research methods and ethics we recommend reading this brief article by Jeantine E. Lunshof, George M. Church, and Barbara Prainsack: Raw Personal Data: Providing Access
New sensors are peeking into previously invisible or hard to understand human behaviors and information. This has led to many researchers and organizations developing an interest in exploring and learning from the increasing amount of personal self-tracking data being produced by self-trackers. Even though individuals are producing more and more personal data that could possibly provide insights into health and wellness, access to that data remains a hurdle. Over the last few years a few different projects, companies, and research studies have launched to tackle this data access issue. As an introduction to this area, we’ve put together a short list of three interesting projects that involve donating personal data for broader use.
Developed and administed by the WikiLife foundation, the DataDonors platform allows individuals to upload and donate various forms of self-report and Quantified Self data. Data is currently available to the public at no cost in an aggregated format (JSON/CSV). Data types includes physical activity, diet, sleep, mood, and many others.
OpenSNP is an online community of over 1600 individuals who’ve chosen to upload and publicly share their direct-to-consumer genetic testing results ( 23andMe, deCODEme or FamilyTreeDNA) . Genotype and phenotype data is freely available to the public.
Open Paths is an Android and iOS geolocation data collection tool developed by the New York Times R&D Lab. It periodically collects, transmits, and stores your geolocation in a secure database. The data is available to users via an API and data export functions. Additionally, users can grant access to their data to researchers who have submitted projects.
We’ll be expanding this list in the coming weeks with additional companies, projects, and research studies that involve personal self-tracking data donation. If you have one to share comment here or get in touch.
One interesting aspect of personal data is how it can reveal what is unique about you. Nowhere is this more true than with genetic information coming from DNA testing kits. However, people are still at an early stage on how they apply that information to their lives. Ralph Pethica, who has a PhD in genetics, was interested in what his DNA could tell him about how to train more effectively. His findings were presented as an ignite talk at the 2014 QS Europe Conference.
What did Ralph do?
Ralph loves to surf. When it is the off-season, he trains so that his body will be in good condition for when the warm weather rolls back around. He used genetic research to inform how he designed his training plans.
How did Ralph do it?
Ralph used a 23andMe kit to find out his genetic profile. He researched those genes that have been found to have an impact on fitness to see his body should respond to exercise. For example, did he possess genes that gave him an advantage in building muscle with resistance training? He then modified his training routines to take advantage of this information and monitored his results (using the Polar watch and a Withings scale) to see whether his assumptions held up.
What did Ralph learn?
Ralph found out that he has genetic disadvantages when it came to strength training. This told him that progress in this area depended more on his lifestyle. In particular, he found that eating immediately after working out was important.
When it came to cardio exercise, he had a number of genetic advantages. The unexpected downside to this is that his body adapts quickly to any training regimen, resulting in a plateau. To get around this, he varied his training plan and monitored his results. On one day, he would cycle at a steady rate, while the next, he would use high-intensity intervals. His body seemed to respond to the varied training plan and he hit fewer plateaus. Without knowing which genes he possessed, and reading current research on those genes, it is unlikely that he would have discovered these effective customizations to his training plan.
Ralph has taken what he’s learned and built a tool called Genetrainer to help people use their genetic information to inform their fitness plains. You can check it out here.
Tools: Genetrainer, 23andMe, Polar RCX5, Withings Smart Body Analyzer
Rosane Oliveiria is a researcher and scholar that focuses on integrative medicine, genomics, and nutrition. She’s also an identical twin. In 2012 she was struck by the different patterns of weight fluctuations that she and her sister, Renata, had been experiencing. Using historical data and medical records she was able to go back in time and track their paired histories, dietary changes, and blood markers. Rosane and Renata started adding to there data-rich story by exploring genetic testing, additional biomarkers, and are looking to incorporate activity and microbiome data in the future. Watch her presentation, from the 2013 Quantified Self Global Conference, to learn more about this interesting quantified double self story.
At our Quantified Self conferences we focus our show&tell talks on personal, first person narratives of self-tracking and self-knowledge. But what if first person is actually two people instead of one? Well, that’s when things get interesting!
At the upcoming Quantified Self Global Conference we are excited to have Dr. Rosane Oliveira talking about her self-tracking experiments that she’s been conducting with her twin sister. This great talk will include explorations of genetic testing, metabolic biomarkers, gut microbiome and mobile monitoring of diet, weight, sleep, mood, and activity levels.
Lucky for us, we’ve already received a preview of sorts in the form of a wonderful talk Rosane presented at the Bay Area QS meetup group in March of 2012. Watch the video below and come prepared to learn more about what you Rosane was able to learn when she started tracking with her genetic double.
The Quantified Self Global Conference will be held in San Francisco on October 10th and 11th. Registration is now open. As with all our conferences our speakers are members of the community. We hope to see you there!
23andMe, a wonderful annual sponsor of the Quantified Self, has some exciting news to share with us. Check out this letter from 23andMe’s CEO Anne Wojcicki below!
Today we’re announcing some big news. I don’t want this moment to go by without a note of gratitude to our customers and those who have been advocates for 23andMe over the years.
We have come a long way together. Because of you we pioneered the use of personal genetics. You are helping us establish a new medical era that is defined by wellness, disease prevention, and personalized care. You also helped us create a novel research platform. By leveraging online tools, social networking and crowd sourcing, and combining them with genetics, we created a platform that has set the stage to transform the way pharma companies and academics do health and wellness research.
We want to do more.
23andMe has raised more than $50 million in new financing with the goal of reaching one million customers. To help us reach our goal, we are happy to announce today, that we are dropping our price to $99.
One million customers can be the tipping point that moves medicine into the molecular era. Hundreds of you have written to us about how genetic information changed your lives and, in some cases, saved your lives. We believe genetics should be an integral part of health care and we will work hard in the coming year to help genetics become part of everyone’s health and wellness.
A community of one million individuals will also benefit the world. A genetic data resource of this magnitude has enormous potential to address unanswered questions related to the contributions of genes, the environment and your health. Understanding these factors and their interactions could lead to major improvement in diagnostics, preventive medicine and therapeutics [Collins NATURE | VOL 429 | 27 MAY 2004].
This change is not just about a new price point for personal genetic testing. It is about an ambitious plan that could transform medicine for generations to come.
If you have questions about the new pricing please go to our FAQ or email your questions to customercare@23andMe.com.
We talk about very frequently here on the QS website about tools, methods, and systems that help us understand ourselves. When it comes to the self there may be nothing more fundamental to understanding our objective ourness than our basic genetic makeup. Many of you have probably undergone or have thought of using Direct-To-Consumer genetic testing to better understand your phenotypes, disease risk, or even your ancestry. That’s all great, and I’ve spent a lot of valuable time combing through my own genetic data, but like most data true power lies in large datasets that provide observations across many individuals. So how do you participate in that type of sharing and learning? Enter the team at the openSNP.org. Today we talk with Bastian Greshake, one the developers behind the openSNP project.
How do you describe openSNP? What is it?
The too long, didn’t read version: A open platform which allows people to share their genetic information and traits, which are suspected to be at least partially genetically predisposed, which also tries to annotate those genetic variants with primary scientific literature. The data can be exported from openSNP through the website or through APIs, making it easy to re-use the data.
A longer version: openSNP has basically two target groups and users may as well fit in both categories.
First there are customers of Direct-To-Consumer (DTC) genetic testing like 23andMe who want to share their genetic information with the public for various reasons. Those can use openSNP to release their genetic data into the public domain using the Creative Commons license which is applied to the data uploaded and entered in openSNP.
As genetic information is interesting but not very useful to analyze the effect of genetic variants on bodily traits those users can also enter information about traits which might genetically influenced and create new possible categories which all other users then can enter. Those traits range from the more obvious ones, like eye and hair color, to more exotic ones like political ideology. A few weeks ago we also created a method for users to also connect their Fitbit accounts to openSNP to make the collection of data easier and more standardized. The genetic effects on activity, sleep habits and weight loss/gain can more easily be analyzed in this fashion.
We also mine the databases of Mendeley, the Public Library of Science and the SNPedia to annotate the genetic variants users carry. This allows customers of DTC testing to find out what the recent scientific literature is able to tell them about their genetic variants. While the SNPedia is a crowd-curated Wiki, Mendeley and the Public Library of Science link back to primary literature, in the latter case even to Open Access literature which is full text available for everyone.
The second group of users who are interested in openSNP are scientists and citizen scientists who are interested in using the data for their own studies, be it to figure out what genetics can tell us about our ancestry or which effects single variants have on disease risks or other traits. The data can be downloaded from openSNP in bulk or more granularly accessed through a JSON-API and the Distributed Annotation System, a standard in Bioinformatics, which for example is used to visualize the data.
Both groups can profit from the commenting features which allows users to communicate about traits and individual genetic variants. The internal message system of openSNP also facilitates further communication, for example to share details about shared traits and diseases or to allow people who want to use the data to get back in touch with the people who uploaded the data. The latter one enables the direct exchange between those two user-groups in a bidirectional way: Researchers can ask questions about traits and people who have shared their data have a back channel as well and can get notified about the results researchers have made.
What’s the backstory? What led to it?
It more or less began with me getting my genetic information analyzed by 23andMe myself. After I received the results I published the data in a git repository on GitHub to make it available for others who might benefit of having more data. As I started to dig deeper into my own results and the raw data I wanted to have more data sets myself, to be able to compare the results. But unfortunately there wasn’t a single resource for such data. Some people also had published their data on GitHub, others on their own websites, collected publicly available data sets in a Google Spreadsheet or participated in projects like the Personal Genome Project.
This was quite frustrating: Finding the data was hard and it most often there was no additional data about traits attached. And more often than one would expect there was also no way to contact to people who made the data public. So the idea to create a platform to solve this problem grew and I contacted some friends to see if they were interested in doing such a platform, just for fun. We started out with the basic idea of creating a platform where people could upload their genetic data along with some traits they have. A couple of weeks after we started to work on the project we stumbled upon the APIs of Mendeley & the Public Library of Science and thought it might be cool to include additional data about the genetic variants as well. During the development we came up with more and more features, like the openSNP APIs. All in all the project is still growing and we’re working on adding and refining features.
What impact has it had? What have you heard from users?
We submitted the first release of openSNP to the 2011 PLOS/Mendeley Binary Battle, a competition interested in creative ways to use their APIs and won the first prize. We also secured a small grant from the German Wikimedia Foundation, which allowed us to genotype over 20 people, mainly from underrepresented groups, to diversify the available data. Those persons have now released their genetic data on openSNP as well. Right now we have over 250 genetic data sets on openSNP and just short of 600 registered users. Those numbers don’t sound to impressive in the age of one billion people on Facebook. But to put it into perspective: Genetic testing is still a niche thing and before openSNP was released there were about 40-50 of those data sets publicly available.
The feedback of our users has been very positive. Many users come up with new ideas for features they like to see added and we are really open to those suggestions and critiques. Many of the API methods, which are now implemented (and the whole Distributed Annotation System), are only in place because user let us know they wanted them. I know of users who are actively using openSNP to learn more about their test results and are in an active exchange with other users with similar traits. And while the amount of data we have so far doesn’t really allow scientifically sound studies there are already people using the data, for example there are users who run their self-written analysis-tools over the openSNP-data sets and report the results back to the users, which is amazing.
What makes it different, sets it apart?
Of course we’re not really the first to think of such an idea but are more or less a remix. For example 23andMe themselves do use the data of consenting customers for studies. They also provide questionaries about traits which users can take. But this data isn’t available to the public, due to (perfectly reasonable) concerns in terms of privacy, bio-ethics and liability. On the other hand there are projects like the Personal Genome Project, which publishes traits and genetic data of participants into the public domain. But due to similar reasons like with 23andMe the participation in the project isn’t open to everyone.
We feel that informed individuals should be in the position to share their data with the world, like they are already doing on their own websites, in an easy fashion. And of course we’re targeting a slightly different group: Probably over 150,000 people are customers of some DTC genetic testing, this is a huge potential data source which could be used to help us understand new and exciting things.
What are you doing next? How do you see openSNP evolving?
We’re still developing and refining openSNP. One of the biggest problems right now is the quality of the data for the additional traits. We have kept the process of adding data really open on purpose, to make it easy for people to provide additional information about themselves. Unfortunately this has the side-effect that the quality of the descriptions varies wildly. Those problems start of with regional idiosyncrasies: Is it “Eye Color” or “Eye Colour” and are you using the metric or the imperial system of units? And is your eye color blue or “Indeterminate brown-green with a subtle grey caste”? This granular data can be very useful, but for many applications it can be too specific. With the implementation of the Fitbit API we’ve taken a first step to keep the entering of data simple but unified at the same time. And we’re currently looking into other ways of how one could counter problems like this.
We’re also looking in more data sources to annotate the genetic variants listed in openSNP, to provide even more information for customers of DTC testing. And we’re also working on making our APIs more powerful. With the rOpenSci package there is already a great library which makes use of the APIs in the current state, but of course we would like to see more of those libraries.
And it’s hard to say in which direction openSNP will evolve as we are a bit dependent on the DTC genetic testing industry. More and more data, like Whole-Genome or Exome Sequencing, is generated and we are working on reflecting those changes on openSNP as well. And we’re open for any suggestions. So if you find that a feature is missing you should let us know, we will try to work out a way of how this might be usefully implemented.
Anything else you’d like to say?
First of all: We know, genetic information is sensitive and depending on where you are living there might not even be laws to protect you from discrimination based of your genes. Other countries, like the US with the Genetic Information Discrimination Act (GINA), have some mechanisms against this, but even those might not offer total protection in the end. And you should also keep in mind that your genetic information does not only give away details about yourself, but by design also about the next of kin. I think this is really important. If you are thinking about publishing your genetic data please keep those issues in mind. And if you come to the conclusion that this isn’t for you as you have to fear negative repercussions or just have a gut feeling of not really wanting to publish the data: Please don’t do it.
And what I also can’t stress enough is that openSNP is developed and run by a team of about four people and we are all doing this in our spare time as a fun project and as community service, without compensation. Some of us have day jobs, others are still studying and some even do both. So while we are doing our best to keep everything running it might sometimes take a while. But if you feel like contributing to the project please get in touch with us. We’d love to have more people in on this.
Authors note: Data sharing, especially genetic data, is a very sensitive topic in our community. I want to fully disclose my bias towards openness and sharing. I believe that our kindergarden teachers had it right when they taught us that sharing is one of the fundamental human traits we should all cultivate. To this end, I have participated in openSNP and you can view my genetic data here and my Fitbit data here.
This is the 18th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned? In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta or Ernesto Ramirez.