A Deep Look Into Our Genes: Recent debates have focused on the degree of genetic variation and its impact upon health at the genomic level in humans (see the Perspective byCasals and Bertranpetit).Tennessenet al.(p.64, published online 17 May), looking at all of the protein-coding genes in the human genome, andNelsonet al.(p.100, published online 17 May), looking at genes that encode drug targets, address this question through deep sequencing efforts on samples from multiple individuals. The findings suggest that most human variation is rare, not shared between populations, and that rare variants are likely to play a role in human health.

Significance: One of the most enduring and widely debated questions in prehistoric archaeology concerns the origins of Europe’s earliest farmers: Were they the descendants of local hunter-gatherers, or did they migrate from southwestern Asia, where farming began? We recover genome-wide DNA sequences from early farmers on both the European and Asian sides of the Aegean to reveal an unbroken chain of ancestry leading from central and southwestern Europe back to Greece and northwestern Anatolia. Our study provides the coup de grâce to the notion that farming spread into and across Europe via the dissemination of ideas but without, or with only a limited, migration of people.

Approximate Bayesian computation (ABC) techniques permit inferences in complex demographic models, but are computationally inefficient. A Markov chain Monte Carlo (MCMC) approach has been proposed (Marjoram et al. 2003), but it suffers from computational problems and poor mixing. We propose several methodological developments to overcome the shortcomings of this MCMC approach and hence realize substantial computational advances over standard ABC. The principal idea is to relax the tolerance within MCMC to permit good mixing, but retain a good approximation to the posterior by a combination of subsampling the output and regression adjustment. We also propose to use a partial least-squares (PLS) transformation to choose informative statistics. The accuracy of our approach is examined in the case of the divergence of two populations with and without migration. In that case, our ABC–MCMC approach needs considerably lower computation time to reach the same accuracy than conventional ABC. We then apply our method to a more complex case with the estimation of divergence times and migration rates between three African populations.

The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations.

Near Eastern genomes from Iran: The genetic composition of populations in Europe changed during the Neolithic transition from hunting and gathering to farming. To better understand the origin of modern populations, Broushaki et al. sequenced ancient DNA from four individuals from the Zagros region of present-day Iran, representing the early Neolithic Fertile Crescent. These individuals unexpectedly were not ancestral to early European farmers, and their genetic structures did not contribute significantly to those of present-day Europeans. These data indicate that a parallel Neolithic transition probably resulted from structured farming populations across southwest Asia. Science , this issue p. 499