The different genetic variation discovery projects (The SNP Consortium, the International HapMap Project, the 1000 Genomes Project, etc.) aim to identify as much as possible of the underlying genetic variation in various human populations. The question we address in this article is how many new variants are yet to be found. This is an instance of the species problem in ecology, where the goal is to estimate the number of species in a closed population. We use a parametric beta-binomial model that allows us to calculate the expected number of new variants with a desired minimum frequency to be discovered in a new dataset of individuals of a specified size. The method can also be used to predict the number of individuals necessary to sequence in order to capture all (or a fraction of) the variation with a specified minimum frequency. We apply the method to three datasets: the ENCODE dataset, the SeattleSNPs dataset, and the National Institute of Environmental Health Sciences SNPs dataset. Consistent with previous descriptions, our results show that the African population is the most diverse in terms of the number of variants expected to exist, the Asian populations the least diverse, with the European population in-between. In addition, our results show a clear distinction between the Chinese and the Japanese populations, with the Japanese population being the less diverse. To find all common variants (frequency at least 1%) the number of individuals that need to be sequenced is small (∼350) and does not differ much among the different populations; our data show that, subject to sequence accuracy, the 1000 Genomes Project is likely to find most of these common variants and a high proportion of the rarer ones (frequency between 0.1 and 1%). The data reveal a rule of diminishing returns: a small number of individuals (∼150) is sufficient to identify 80% of variants with a frequency of at least 0.1%, while a much larger number (>3,000 individuals) is necessary to find all of those variants. Finally, our results also show a much higher diversity in environmental response genes compared with the average genome, especially in African populations. 1000 Genomes Project | beta-binomial model | CNVs | sequence data | SNP A main goal of the various human genome projects is to discover genetic variants in human genomes. The HapMap project (http://www.hapmap.org/) has contributed much to our understanding of the underlying genetic variation in diverse human populations and has facilitated the discovery of many loci robustly associated with common human diseases, such as diabetes, obesity, breast cancer, and many others (1-5). The recently launched 1000 Genomes Project (http://www.1000genomes.org/), by sequencing 1,000 genomes, aims to discover much of the existent common variation (frequency at least 1%), including both single-nucleotide polymorphisms (SNPs) and the less explored copy-number variants (6). In this article, we provide a systematic way to predict the number of new variants with a specified minimum frequency to be identified in future datasets of specified sizes. In particular, based on sequence data for a set of individuals, can we predict how many more new variants will be found if we were to sequence a new set of individuals of a given size? This question is related to the species problem in ecology, where one is interested in estimating the number of species in a closed population. A particular example is estimating the number of words Shakespeare knew but did not use. Specifically, if a new volume by Shakespeare were to be discovered, how many new words would we expect to see? Efron and Thisted (7) used a Gamma-Poisson model to address this question. We adapt the approach in Efron and Thisted to the problem of predicting the number of genetic variants yet to be identified in future studies. The method also allows calculation of the number of individuals required to be sequenced in order to detect all (or a fraction of) the variants with a given minimum frequency. In the following sections we develop the method and show applications to several available sequence datasets: ENCODE, SeattleSNPs, and National Institute on Environmental Health Sciences (NIEHS) SNPs. Although these datasets contain only SNPs, the method can be applied to counting other types of variants, including copy-number variants.
For example, in REF , the mutation probabilities of the variants are assumed to be i.i.d.
Estimating the number of unseen variants in the human genome
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Biology Medicine