Simulated Genetic Drift in Populations

This video shows a simulation of genetic drift in a synthetically generated population as a result of sexual reproduction. A fixed population size is divided into different distinct sub-populations with differing allele frequencies. Starting with an initial population, 80 simulated epochs are passed in which each population member is replaced via sexual reproduction from two randomly selected parents. Pairs of parents are chosen such that the probability they come from the same sub-population is higher than the probability that they from come different sub-populations.

Figure 1: Simulated Genetic Drift in Populations

In the animation, the high-dimensional allele features for each population member are represented in two dimensions using PCA. The size and color of each marker encodes information about sub-population makeup and may be used to help distinguish highly mixed samples.

Although the data is entirely synthetic, it demonstrates the middling affect that cross-breeding has on the genetic makeup of the overall population. As the number of sub-populations grows and the overall population size remains fixed, the impact of cross-breeding is intensified and the population as a whole converges more rapidly.

Ancestry Determination Part 3: openSNP Data Evaluation

The website openSNP allows users to share and discuss genetic information [1]. The purpose of the site is perhaps best summarized by its bio on Twitter: “crowdsourcing genome wide association studies” [2]. This post uses the machine learning and statistical techniques developed in previous blog entries to analyze the ancestry of openSNP members who have genotyping data associated to their accounts. By comparing the derived results with the self-reported answers from openSNP users, the accuracy of these methods on real-world data is assessed.
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Ancestry Determination via Genetic Variant Analysis Part 2

In this post, the techniques outlined in an earlier blog post are employed to predict the ancestry of the author. Two approaches are considered: an approach using a classification model and one using similarity functions. Finally, scatter plots depicting low dimensional projections of the data are shown, plotting the genome of the author alongside samples from the IGSR dataset.

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Ancestry Determination via Genetic Variant Analysis

Introduction

Sequencing of the human genome began in 1990 as part of the Human Genome Project. With the technology available at the time, the project was a substantial undertaking. The human genome contains two sets of 23 chromosomes each with roughly 3.2 billion base pairs. A number of institutions, in countries around the world, participated in the project. Thirteen years later the project was complete at a cost of roughly three billion US dollars. The result was the first reference human genome.

Rapid advances in the field of genomics have dramatically lowered the cost of genetic sequencing and have ushered in the age of the once fabled “$1000 genome.” Now, a growing list of companies offer whole genome sequencing for hundreds of dollars with turn around time measured in weeks. This technology enables introspection into the sequences of nucleobases that comprise DNA and thus the genes of anyone curious enough to take the plunge.

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