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PLINK

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Long time readers know that I spend a lot of time with Plink, developed by Shaun Purcell. That being said, even with the modest data sets I play with I’ve had to make recourse to to writing shell scripts to perform various Plink manipulations serially and let them run overnight. Well, perhaps no more. Here’s the description for WDIST genomic analysis toolset:

WDIST is an experimental rewrite of the popular PLINK command-line tool, with vastly improved performance and scaling across a wide range of calculations. (And a thorough command-line help facility, in case you’re as forgetful as we can be.) Some new functionality is present as well, such as a memory-efficient and Mac/Windows-compatible implementation of GCTA 1.1′s multithreaded relationship matrix calculator.

It complements the PLINK/SEQ library, which focuses on developer-friendliness and support for more complex data formats.

It is developed by Christopher Chang, with support from the NIH-NIDDK’s Laboratory of Biological Modeling and others. (Credits.) We estimate the first complete version will be ready in early 2014, and with permission, we will then prepare a stable build suitable for universal distribution as PLINK v1.50.

Update: Also, PLATO: PLatform for the Analysis, Translation, and Organization of large-scale data.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Genomics, PLINK, Translation 
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Earlier editions:

Using your 23andMe data: exploring with MDS
Using your 23andMe data in Plink

From Reconstructing Indian Population History:

We hypothesize that founder effects are responsible for an even higher burden of recessive diseases in India than consanguinity. To test this hypothesis, we used our data to estimate the probability that two alleles from a group share a common ancestor more recently than that group’s divergence from other Indians, and compared this to the probability that an individual’s two alleles share an ancestor in the last few generations due to consanguinity…Nine of the 15 Indian groups for which we could make this assessment had a higher probability of recessive disease due to founder events than to consanguinity, including all the Indo-European speaking groups (Table 2). It is important to systematically survey Indian groups to identify those with the strongest founder effects, and to prioritize them for studies to identify recessive diseases and map genes.

South Asian populations exhibit a lot of between population genetic distance, and not simply as a function of geography. With more markers and an expansive data set Dan MacArthur will be able to assess exactly which South Asian caste his ancestry is from.

But this is an issue where I have fancied myself an outlier. My own background is moderately heterogeneous, and I’ve always explained to people that I’m not inbred like most South Asians, only half in jest (from what I can tell Muslims in the subcontinent have castes too, though they may somewhat different terminology). I know that my paternal grandmother came from a Brahmin family (clear by the customs preserved in the family even in her generation), while my maternal grandfather was almost certainly from a group with a Kayastha origin (going by surname, and who my mother actually clusters with). My maternal grandmother had considerable non-Bengali ancestry, which does show up in Middle Eastern signatures in my mother.

But this is talk. Am I truly not as inbred as the average brown? Leveraging methods which I discussed earlier (see posts above) I can very quickly check this.


First, you need to prune your data set to a reasonably homogeneous reference population which resembles your own ethnic makeup. The way you infer the extent of inbreeding is simply to look at the distribution of genetic variants, and see how shifted away from the population norm you are. Since different populations have different background distributions putting yourself within the wrong reference set leads to absurdity. Compared to a Bushmen reference every non-African would come out as inbred. The computation is not faulty, but it’s not giving you useful information.

In the .fam file of PHYLO I picked out every single non-Pakistani South Asian as my reference, mostly Gujarati, but with some South Indians as well. By looking at the expected genotypes by pooling this population together I want to get a sense of my own place. Additionally I’ll add my daughter and my 1/4 Filipino friend as controls, in that they should be way less “inbred” than everyone else since they are the products of recent admixture.

After using the – -keep function of Plink I merged the file with my own, my daughter’s, and friend’s. There were north of 90,000 SNPs, more than sufficient for the simple computation I wanted to do. I’ll output the F-statistic with the – -het function like so:

plink – -noweb – -bfile DATASET – -het

The output is in plink.het. You’ll see the labels in the leftmost column, and the statistic you want in the rightmost column. In the results below are sorted from most to least inbred, at least using the F-statistic as a measure of that (this isn’t really totally accurate because the population isn’t really a homogeneous random mating set, but I think it gets the intuition out there):

The reason that my daughter and my friend have negative values is that they have way fewer homozygotes than you should get my chance. But they’re recent admixtures, so question of inbreeding is near not-even-wrong for them. The Plink documentation says that negative F values are noise (they are not contamination in this case), but I think I’ll chalk it up to a not-totally-homogeneous population. My position this list is not as low as I’d like, but I’ll take it. I believe I can still claim I’m less inbred than the average brown.

(Republished from Discover/GNXP by permission of author or representative)
 
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With the recent $99 price point for 23andMe many of my friends have purchased kits (finally!). 23andMe’s interpretive results are pretty rich now, but there are still things missing. There are plenty of third party tools you can use, but I know some people might want to do their own data analysis. There are many ways you could go about this, but I want to put up some posts on DIY genomic data analysis to making the learning curve a little less steep, and get people started. Motivation to actually begin going down this road is a big issue, but I think once you get over the hump it gets a lot easier.

First, you need Plink. It is really preferable that you work on a Mac or in Linux to engage in heavy duty analysis, but in this post I’ll assume you are working on the Windows platform. Again, the point here is to make this accessible. Download Plink if you don’t have it, and extract it where ever you like.


Plink is a command line tool, which means that you need to into the folder with the old MS-DOS interface. So use the cd command to get into that folder. Here is a screenshot of my shell:

The selection “plink –noweb –bfile PhyloF –genome” is a command that I entered. It is not part of the directory structure. If you don’t know about the cd command, please see the Wikipedia entry. It’s really just a simple way to step through the directory structure of your files and folders.

Now you have Plink. We need to put your 23andMe data into pedigree format. Additionally it would be convenient to have other reference data sets . Go to here. You now need to click the ZIP option. That will download a 74 MB zip containing all the files you see listed to the left. Most of that is in the two zip files, which are pedigree file data sets that I have provided for your future use. More on that later. First you need to use “CONVERT_23AME_PED.pl” This a Perl script which takes the 23andMe text file, and converts it to pedigree format which Plink can use. You need to have Perl to use this script.

If you are on Windows you need to get ActivePerl. Download it. Again you have to open the command prompt and go into the appropriate folder. On my computer (this is the first time I’m using Perl on Windows in 10 years, the sacrifices I make for the readership of this weblog!) it is in the C: directory, so you probably have to move “up” the directory tree twice by typing “cd ..” (if you do this you’ll see what I mean). Once you are in the Perl directory you need to go into the bin directory. Remember to move the Perl script into the Perl directory. Here is a screen of what I get when I try and run the Perl script without any parameters:

Basically there needs to be a file for the script to process. You should have a 23andMe text file, your raw data. It will start like so: “genome_”. If you don’t have it, go into your account, and click “Browse Raw Data.” On this page there will be options to download various peoples’ data if you have multiple accounts. It will download whoever is selected in your profile (for most it will be just one person of course).

Now you need to just select the button and enter your password. An 8 MB zip file will come down from the server. Put it into your Perl/bin folder by extracting it. Do not try and process the zip file! Once in there you now add it as your first parameter. I’d rename it something short and sweet since you’ll be typing it in. You don’t need to put a unique id parameter in, but I would if I were you. Try “me.” And “me” for the family id. At some point you’ll do more sophisticated things and need less silly ids, but not right now.

Here’s a screenshot of me running the Perl script with my own data (I renamed the text file). If the file name isn’t recognized make sure that you didn’t add the file extension within Windows, that might confuse it (e.g., for razibdata.txt if that’s what you see in the directory, you’d have to enter in razibdata.txt.txt in the parameter value since the extension is hidden):

There are two output files. In my case they are razibdata.ped and razibdata.map. As you can see they are named from your original file. You need to move both into the Plink directory. The .ped file has your individual data, the first half a dozen columns being the same as the parameters you may, or may not, have entered above. But it is very large because the whole line is filled out with your 23andMe genotype. The .map file basically has the information about the SNPs. These are both text files, and unwieldy. You need to make it into a binary file. At the end of this there are three new files of the same name with extensions .bed, .bim, .fam:

You can see a lot of information. Most of it is not relevant to you, but note the number of SNPs. So now you have a pedigree file! Great. What do you do with it? Lots of stuff. You can look at the Plink documentation. Because the .bed file is a binary, never open it. The .bim has SNP info. You shouldn’t open this. On the other hand when you merge data sets .fam is useful. It’s a text file with all your individual and family id information. In this case with one file it isn’t informative, though you could change the id by editing the .fam file.

One thing you can do with just one individual is look for runs of homozygosity. The command is:

plink –bfile mydata –homozyg

You enter your binary pedigree file name, without the extension. Observe that now we are use –bfile instead of –file. Many commands will be bCommand instead of just Command if you are using binary files instead of the conventional ones. Binary files are smaller and the commands finish much faster, so use them! The output files, unless you use the –out command at the end to define them, usually begin with plink. So above you have plink.hom. It has some interesting information about the runs of homozygosity, but it is probably not too illuminating unless you suspect you are inbred!

Ultimately what I want you do by the end of this is compute an MDS with your own data against a reference set. That’s PHYLO in the data I’ve provided. It has 99,000 SNPs that overlap with 23andMe, and 1,500 individuals. I’ve altered the .fam file so that all the family ids are recognizable as populations. This will make analysis of the output easier for you. First you need to merge the files. It will be useful for you to prune your data set down, since you have a lot of extra SNPs.

Assuming you’ve extracted PHYLO out of the zip downloaded here is my command writing out the list of SNPs within PHYLO:

You can see from reading this that this data set has ~99,000 SNPs. I pruned it so that it ran quicker for phylogenetic analysis. This is more than a sufficient number for most analysis. What you want to next is create a copy of your own data which doesn’t have so many SNPs, so you can merge them well. Because I created this data set I can tell you that all of the above SNPs are probably in your 23andMe file. With the commands above there is a file, plink.snplist, which you will use to filter your data set.

Here’s how to do it:

Now we’ve got it ready to merge. I will warn you that this takes forever on Windows! No idea why. Also, Windows tends to do strange things with the file extensions. If Plink tells you that a .fam does not exist, look to the file extension. If you label something as something.fam, it might actually be something.fam.fam. In any case, here’s how you merge:

This is going to give you lots of warnings. Often this won’t matter, but sometimes it will tell you that you might need to “flip” one of the files. Try flipping it. If it still doesn’t work I would remove the SNPs causing problems. Something like this:

Honestly you might have to do a lot of things to get data sets to merge. But this particular combination of 23andMe genotype and PHYLO shouldn’t be too bad. Let’s assume that your merge worked. What do you want to do? One thing that might be interesting is an MDS plot (it’s like a PCA plot).

First you run the genome command, which takes forever to finish. It might be best if you did this before you go to sleep, and just check in in the morning. The genome command will produce an output that you’ll use next.

Notice the input file. That was generated in the previous step. The value 6 is a parameter that defines how many dimensions you want to output. My experience with this is that it doesn’t take too long, so I go for 6 at least. The final result of this is that you have an plink.mds file with an ordered list of family and individual ids, along with positions for 6 dimensions. It should be straightforward to import this into Excel, and then plot your MDS, emphasizing your own position. Since I can no longer use Excel I couldn’t be bothered to figure out how to plot my own position, but the distribution should be familiar.

That’s about it for now. I’ll put up another post focusing less on phylogenetics, using the HapMap data set that I provided. I don’t know if I can continue to do this in Windows, but hopefully this illustrated how easy (if tedious) most of this is.

(Republished from Discover/GNXP by permission of author or representative)
 
• Category: Science • Tags: Genomics, PLINK 
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Over the past few months I was hoping more people would start doing what Zack Ajmal, Dienekes, and David, have been doing. There are public data sets, and open source software, so that anyone with nerdy inclination can explore their own questions out of curiosity. That way you can see the power and the limitations of genomics on your own desktop. I wonder if one of the biggest reasons that more people haven’t started doing this is formatting. It can be a pain to convert matrix formatted files into pedigree format, for example. But the data gusher isn’t ending, look at what’s coming out (and has come out) in the 1000 Genomes project!

I’ve been thinking I need to write up a post which is a “soft landing” for people so that we can reduce the “activation energy” for this sort of thing…once you get hooked, you only go deeper. Luckily an anonymous tipster has sent me the link to a URL with a huge data set which has been merged, already pedigree formatted. Here are the populations:

!Kung Buryats Hausa Mada Punjabi Arain Totonac
Adygei Cambodian Hazara Makrani Pygmy Tu
African Americans Chinese Hema Malayan Romanians Tujia
Algeria Chinese Americans Hezhen Mandenka Russian Tunisia
Altaians Chukchis Hungarians Maya Sahara Occ Turks
Alur Chuvashs Iban Mbuti Sakilli Tuscans
Ap Brahmin Cochin Jews Igbo Melanesian Samaritians Tuvinians
Ap Madiga Colombian Iranian Jews Mexicans Samoan Urkarah
Ap Mala Cypriots Iranians Miao San Utahn Whites
Armenians Dai Iraq Jews Mongola San Nb Uygur
Armenians B Daur Irula Mongolians Sandawe Uzbekistan Jews
Ashkenazy Jews Dogon Italian Moroccans Sardinian Uzbeks
Azerbaijan Jews Dolgans Japanese Morocco Jews Saudis Vietnamese
Balochi Druze Jordanians Morocco N Selkups Greenlanders
Bambaran Greenlanders Kaba Morocco S Sephardic Jews Xhosa
Bamoun Egypt Kalash Mozabite She Xibo
Bantukenya Egyptans Karitiana N European Sindhi Yakut
South Africa Ethiopian Jews Kets Naxi Singapore Chinese Yemen Jews
Basque Ethiopians Khmer Nepalese Singapore Indians Yemenese
Bedouin Evenkis Kongo Nganassans Singapore Malay Yi
Beijing Chinese Fang Koryaks Nguni Slovenian Yoruba
Belorussian French Kurd North Kannadi Sotho/Tswana Yukaghirs
Biaka Fulani Kyrgyzstani Orcadian Spaniards
Bnei Menashe Georgia Jews Lahu Oroqen Stalskoe
Bolivian Georgians Lebanese Palestinian Surui
Brahui Gujaratis Lezgins Paniya Syrians
Brong Gujaratis B Libya Papuan Thai
Bulala Hadza Lithuanians Pathan Tamil Brahmin
Burusho Han Luhya Pedi Tamil Dalit
Buryat Han Nchina Maasai Pima Tongan

The data set has ~4,000 individuals, and ~30,000 markers. The binary file is ~25 MB. The download has four files. The .bed, .bim, and .fam, are pedigree formatted. The .csv is a “master list” of the information on each individual (population, region, etc., tied to a specific identification number). This is important because once you have some output files…you need to figure out what it means, and visualize it, and that’s only informative if you have a master list with more than just family and individual information.

Here is the link to the file to download with all the above populations. I’ve pulled it down and run it, so I know it’s not malware.

So what now? The post will be divided into three portions.

1) Running this data in ADMIXTURE

2) Visualizing it in R

3) Manipulating this data in Plink

#1 is not contingent on #2 and #3, so I’ll do that first. You don’t need to read #2 and #3. In fact some of you might be really good at manipulating spreadsheet formatted data, so it might not be needful to go to #2. But in the R section I’ll also have a easier spreadsheet output for you, so even if you don’t care for R’s visualization, you’ll at least have a better to manage set of .csvs. #3 matters if you want to constrain your data set, and also add your own 23andMe file to the end of it.

#1 Running the data in ADMIXTURE

First, you need Linux or MacOS. If you are on Windows, the Wubi application allows you have to have a dual boot. It runs Ubuntu Linux next to Windows, and you can uninstall it as if it is a Windows application.

I am doing this on Ubuntu Linux, for your information. Assuming you have the right operating system, now you need ADMIXTURE. You can put the folder anywhere.

You need to use the terminal to go to the folder where you have ADMIXTURE. The image to the left shows me doing so. You need to click the terminal application, and ender the “cd” command to get to the appropriate folder. My ADMIXTURE program is on the Desktop, within the “GA” folder, and the “admix2″ subfolder. So I typed what you see. The “cd” command moves you around the folders, up and down. Google it if it confuses you, though without knowing what it does it should be fine if you just extract ADMIXTURE to the Desktop, and you type “cd Desktop”. This will clutter up your desktop in the future…but if you need to get some stuff done ASAP without knowing how to navigate in Linux, that should work.

So now you have ADMIXTURE, and the files which ADMIXTURE is going to analyze. What do you do? You need to make sure that ADMIXTURE and your files are in the same folder/location. So if ADMIXTURE is on the Desktop, just extract the files to the Desktop. Now you need to run a command. You see a screenshot of me running ADMIXTURE. You may need to omit the ./ (i.e., “admixture” vs. “./admixture”). You see the file name. The option -j2 is due to the fact that I have two cores. If you don’t know what that means, just omit it. It speeds up the run though. The last number is the K. So this is for K = 4.

Now the program will run. How long depends on the size of the file, and the number of K’s. I often run the program overnight for larger K’s. If you want to get fancy and do stuff like cross-validation, it will take even longer. Be warned. The screenshot to the left is typical of what you’ll run in to as ADMIXTURE does its thing. No worries, the algorithm is running. If you watch long enough you’ll get a sense of what values on the screen point to a high likelihood that it’s almost done, and you can start anticipating the output files from which you can make inferences.

Completion! To the right is what you’ll see when ADMIXTURE is done. As noted, there are output files. This is what is really interesting & useful, but even on this screen there’s goodness. The primitive matrix shows you Fst distances between putative ancestral populations. Fst is measuring the proportion of variance within the data set which can be attributed to between population variance. The smaller the value, the less the magnitude of differences between two populations. On this screen you see four populations, since I set K = 4. The Fst is generated from ancestral allele frequencies, which are within the output files. Remember, these are distances between abstract populations, not real ones.

The original files were euraocean.bed, euraocean.bim, and euraocean.fam. So the output files are like so:

euraocean.4.Q
euraocean.4.F

The 4 represents the K. The first file has a list of the proportions for putative ancestral populations for each individual in the data set, the individuals being on separate lines. The second file has all the allele frequencies for the ancestral populations, generated by the parameter K.

What do you do with this? euraocean.4.Q is related to euraocean.fam, which has family and individual IDs line by line. I don’t know how to use spreadsheets in anything but a primitive way, so I assume there are ways to merge the files and get each line to have ancestry proportions as well as more detailed IDs. Generating mean values for populations also seems essential.

But I use R to do this dirty work.

#2 Visualizing the output with R

If you don’t have R, you need to install it. If you don’t know how to start, control-f sudo. That should yank it down for you. Once R is installed, make sure to be in the folder where you have ADMIXTURE. Then type “R” (no quotes when you type a command!). Now you are in R, what do you do? Here are the specifics of what you need to do:

1) Take the Q file, pump it into a data frame

2) Take the master list, pump it into a data frame

3) Take the .fam file, pump it into a data frame

4) Mix & match

5) Calculate mean proportions, output populations, etc.

6) Visualize!

If you needed to know how to install R, you probably don’t know how to do this. When I first started playing around with ADMIXTURE output files I wrote a quick & dirty script. I barely remember what I am doing with this script now, as I don’t care about the details. But it is now at your service. Still, first you need to do one thing: use a master list which is formatted slightly differently from the one that you downloaded. Here is the revised master list.

Put it in the same folder as ADMIXTURE. Then start R, again, by typing “R.” Run the command you see above. This creates an “HGDPMaster” data frame. That’s necessary for the script I’m giving you to run.

The script is here. If it doesn’t download, copy & paste, and create a file “Rstuff.R”, in the same folder as ADMIXTURE. There are a few variables which you have to manipulate. Here is the relevant section:

###############
# change these
###########
### outputfiles
fileName<-"euraocean"
fileType<-"Q"

#### sets the number of populations to through
#lowest K
Start_K<-12
#highest K
End_K<-12

You need to change the file name to the one you have output. If you did do any manipulation, it should be ref.2.Q for K = 2, so the name is “ref.” You also need to put in the number of K’s. I often run many simultaneously, which I have output files for in the morning. So I often start with 2 and end with 12. If you just want to output one, for example, 2, change Start_K to 2, and End_K to 2. These are the only variables you need to change. But there is a lot more you could do. R “comments” with #, so there is a section which I commented out where you can limit the output to particular populations to make the bar plot less busy. You’ll see what I mean if you look at the script, just remove all the #’s, and reedit as to your taste. Please note that casing matters, so make sure to keep it lower case when possible (if you looked at the master list, you understand). The script does have a string to upper case function, but that’s only for the output. There’s also a small section where you can reedit the names to your taste.

To run the script, do like so:

source("Rstuff.R")

It should output out bar plots, as well as generating some spreadsheet files. There’s a lot more you can do…but if you can do a lot more, you wouldn’t be reading this post. Let’s move to the next issue. So now you wonder: is there any way I can change the data file, or add myself to it? Read on….

#3 Using Plink to manipulate the data file

Now you need Plink. I usually put it within the same larger folder as a subfolder parallel with ADMIXTURE. You run the Plink command like so: “./plink” or, “plink.” Depends on the environment (remember, the quotes are only for the post!). There are many things you can do with Plink. I will show you how to do two things.

#1 remove individuals from the data set

#2 add yourself (or someone whose 23andMe file you have) to the data set

#1 is important because the plots get busy with too much variance. Additionally, Africans, and genetic isolates which have gone through population bottlenecks, tend to overwhelm ADMIXTURE. You probably want to remove them. To do this you need to use the remove option. You need to remove individuals.

Here’s one option with the file you’ve got:

./plink --bfile ref --remove removelist.txt --make-bed --out refRemoved

What’s going on above? You’re using a binary pedigree file, so you have the –bfile option on. You do the deed with –remove, and then you create a second binary pedigree file, refRemoved. So you’ll have refRemoved.bed, refRemoved.bim, and refRemoved.fam. Obviously removelist.txt has what you want to remove. Each line has a family ID and individual ID, separated by a space, of those who you want to remove. The easiest way is probably to open up the master list. For the one I gave you above the last column is the family ID and the first is the individual ID. Cut & paste the first column after the last, delete the other columns, and save. I usually get rid of quotations and tabs, change it to a .txt file, and there you have it.

But what about your 23andMe file? You need to convert it to pedigree. I have created a quick & dirty perl script to do so. You can find it here. Download or cut & paste it. You need to remove the comments at the top of the 23andMe file. That is, you need to remove everything before the first SNP. Assuming that’s done, do this at the command line within the folder where you put the script (you get to that folder with “cd” recall):

perl convert.pl "YourFileName" "001" "001"

The script fires, gets the file name from the first parameter, and outputs two files, YourFileName.ped and YourFileName.map. What about the two other parameters? They’re generating your family ID and individual ID. They’d be FAM001 and ID001 in this case. You need to enter these into the master list! Otherwise you won’t come out on the bar plots. Also enter your ethnicity, etc. Or, just your name if you want to be your own slice of the bar plot.

Note that you have .ped, not .bed, files. These are big. Now you need to convert the text to binary pedigree. Move the YourName files to the plink folder. Make binary:

./plink --file YourFileName --make-bed --out YourFileName

Now you have YourFileName.bed YourFileName.bim YourFileName.fam. It is best to limit your SNPs to the same as those in the reference data set. So get those from the reference:

./plink --bfile ref --write-snplist --out SNPs

You should have a file, SNPs.snplist. Use them to filter your 23andMe file.

./plink --bfile YourFileName --extract SNPs.snplist --make-bed --out YourFileNameFiltered

Now you want to merge:

./plink --bfile ref --bmerge YourFileNameFiltered.bed  YourFileNameFiltered.bim  YourFileNameFiltered.fam --make-bed --out ref

You are now appended to the reference data set! If you open up the ref.fam file your family ID and individaul ID should be at the end of the list.

If you’ve slogged through this far, I thought it would be nice to end with something which shows what this is all about. Below I’ve filtered the reference data set of most African and New World populations, and run it from K = 2 to K = 12. It took about ~10 hours to complete. I’ve also limited the populations to display using the script above so that it isn’t too clustered. Here are the spreadsheets generated from the runs (they will be in folder where you run the R script, and have the form “K =2″ and such for names).

[zenphotopress album=273 sort=sort_order number=11]

(Republished from Discover/GNXP by permission of author or representative)
 
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Razib Khan
About Razib Khan

"I have degrees in biology and biochemistry, a passion for genetics, history, and philosophy, and shrimp is my favorite food. If you want to know more, see the links at http://www.razib.com"