Video Tip of the Week: Population Genetics Introduction
We are on the road this week at a workshop in Southern California, so I am going to hand off my tip responsibilities to Lynn Jorde.
Another session in the Current Topics in Genome Analysis 2012 course that has been organized by the NHGRI featured Lynn Jorde. Lynn delivered a lecture (about 1.5 hours long in total–but he makes you stand up at 1 hour to stretch ) that provides a nice and gentle introduction to population genetics.
Jorde starts with a list of applications of human genetic variation, such as:
- deciphering human history
- inferring individual ancestry
- forensics (I had no idea that there were 25,000 criminal cases a year with DNA issues)
- finding and understanding disease causing genes
He does some very clever and helpful comparisons to make his points. At one point he compares humans and broccoli. And he uses an item from the Weekly World News to illustrate a point–this made me laugh because I’ve done the same thing.
Touching carefully on the issue of “race”, he acknowledges that human genetics discussions on that can generate more heat than light. So he doesn’t use that term in his writing. And there are a number of cases where social concepts of race vs. medical treatment are not cohering. He finds “ancestry” the more useful way to think about predictions for responses to drugs or treatments.
He also notes though that there is need for caution at this point on reliance on the data we are seeing from next-gen sequencing platforms. Specifically he calls out this paper in Genetics in Medicine as a key awareness (emphasis mine):
CONCLUSIONS: Our analyses demonstrate that clinical prognoses are complicated by sequencing platform-specific errors and ethnicity. We show that disease-causing alleles are globally distributed along ethnic lines, with alleles known to be disease causing in Eurasians being significantly more likely to be homozygous in Africans.
[By the way: that paper is interesting on a couple of other fronts too: it tries to figure out what a "healthy genome" would look like, and heavily uses OMIM to assess that.]
Another clever example to illustrate relationships among people used an analysis of the Supreme Court decisions to describe neighbor-joining networks. And he used profiles of political candidates to explain distance matrix. It seemed pretty approachable to me.
This talk isn’t specific about any particular software tools, but he does reference important population genetics data sets that you should be familiar with if you use tools that have that data. He speaks about the HapMap project, the 1000 Genomes data, and VAAST (the Variant Annotation, Analysis & Search Tool) software.
So check out this talk for a nice overview of population genetics, and important and current factors around this field today.
Lecture on YouTube: http://youtu.be/Ng6vKcGkzZs
Current Topics in Genome Analysis course: http://www.genome.gov/12514288
Moore, B., Hu, H., Singleton, M., De La Vega, F., Reese, M., & Yandell, M. (2011). Global analysis of disease-related DNA sequence variation in 10 healthy individuals: Implications for whole genome-based clinical diagnostics Genetics in Medicine, 13 (3), 210-217 DOI: 10.1097/GIM.0b013e31820ed321
Yandell, M., Huff, C., Hu, H., Singleton, M., Moore, B., Xing, J., Jorde, L., & Reese, M. (2011). A probabilistic disease-gene finder for personal genomes Genome Research, 21 (9), 1529-1542 DOI: 10.1101/gr.123158.111