A reduced representation approach to population genetic analyses and applications to human evolution

Owing to rapidly declining costs, second-generation sequencing has become an affordable means to perform surveys of sequence variation on a genome-wide scale. Several complete genome sequences have already been obtained for humans (and recently also for Neanderthals) (Bentley et al. 2008; Wheeler et al. 2008; Abdulla et al. 2009; Ahn et al. 2009; McKernan et al. 2009; Pushkarev et al. 2009; Green et al. 2010; Schuster et al. 2010) and for model organisms (Doniger et al. 2008; Ossowski et al. 2008; Daines et al. 2009; Hillier et al. 2009). In addition, de novo whole-genome sequencing has become feasible for nonmodel organism species, thus extending the power of genomic approaches to species important for ecological studies or for conservation biology. Despite the lower sequencing costs, collecting whole-genome sequence data for many individuals is still unaffordable for many laboratories. Surveying a large and representative set of unlinked loci, rather than the entire genome, can provide a valuable alternative for many types of studies, especially if the cost of preparing the sequencing target is low. For example, this is the case when the goal is to develop markers for genetic mapping in species for which genetic tools are unavailable. It is also the case if the goal is to estimate demographic parameters; this is because, in the presence of recombination, the variance of the estimators decreases as the surveyed length increases due to the sampling of a larger number of independent realizations of the evolutionary process (Pluzhnikov and Donnelly 1996). Therefore, as long as many independent loci are sampled, resequencing data can provide sufficient information to estimate population parameters accurately even if only a portion of the genome is surveyed and even if obtained from a single sampled individual (Felsenstein 2006). Owing to their efficiency, surveys of variation in single individuals have been used to estimate interpopulation and interspecies divergence (Sun et al. 2009) and, together with ascertained variation data, to reconstruct the allele frequency spectrum (Keinan et al. 2007). Until recently, most variation surveys used Sanger sequencing of PCR products in many individuals per population at relatively few loci (Voight et al. 2005; Wall et al. 2008; Laval et al. 2010). Second-generation sequencing has overcome many of the limitations of Sanger sequencing, but specific protocols for the preparation of the resequencing target are required. Two main approaches have been developed to select a subset of a genome to be analyzed by second-generation sequencing. The first approach enriches the resequencing target for specific regions of interest, which are selected either by PCR amplification or by hybridization to complementary oligonucleotides (Albert et al. 2007; Okou et al. 2007); a disadvantage of this approach is that the oligonucleotide libraries significantly add to the overall costs. An alternative approach is to produce a reduced representation of the genome by restriction digestion followed by direct sequencing the ends of either a subset of the size-selected restriction fragments (Van Tassell et al. 2008) or all the restriction fragments in the genome (Baird et al. 2008). To increase cost efficiency, samples subjected to reduced representation can be pooled prior to sequencing. Many previous studies employing reduced representation protocols aimed to develop markers for subsequent genotyping studies and did not attempt to estimate evolutionary parameters. In general, obtaining unbiased estimates of evolutionary parameters based on shotgun sequence data presents several challenges, which include sequencing and alignment errors as well as missing data (for review, see Pool et al. 2010). Deep sequencing (e.g., in the order of 30–40×) may overcome these challenges by increasing the probability of sampling both alleles at heterozygous sites and the ability to distinguish true heterozygous sites from sequencing and alignment errors. An alternative approach consists in sequencing multiple individuals from the same population at low depth (e.g., average 4× as in the case of the 1000 Genomes project; www.1000genomes.org/), thus reaching higher confidence in the genotype calls for each individual by leveraging the sequence information gathered for other individuals in the same population. Here, we have developed a reduced representation approach for second-generation sequencing that is suitable for population genetic analyses. Because our main goal was to collect good-quality genotype data for population genetic inferences, we modified the reduced representation protocol developed by Van Tassell et al. (2008) to obtain an appropriate sequencing depth for single individual samples. Our protocol retains the features of being inexpensive and applicable to any species for which a draft or complete reference genome sequence is available. Because of the wealth of information available on human sequence variation, we applied it to 19 individuals from a total of 18 different human populations, and where available, we compared genotype calls to the HapMap calls. We showed this method to be reproducible across individuals, resulting in a large overlap in surveyed regions. In addition to the experimental protocol for preparing the sequencing target, we have developed new tools and methods for aligning reads, calling genotypes, and incorporating sample-specific sequencing error rates in the estimate of population genetic parameters. When applied to human population samples, this approach provided, for the first time, a test of the serial founder model of human dispersal and a time frame for the colonization of Australia based on unascertained sequence variation data.