Michael Jordan (University of California Berkeley): Inference of ancestral information in recently admixed populations, in which every individual is composed of a mixed ancestry (e.g., African Americans in the United States), is a challenging problem. Several previous model-based approaches to admixture have been based on hidden Markov models (HMMs) and Markov hidden Markov models (MHMMs). We present an augmented form of these models that can be used to predict historical recombination events and can model background linkage disequilibrium (LD) more accurately. We also study some of the computational issues that arise in using such Markovian models on realistic data sets. In particular, we present an effective initialization procedure that, when combined with expectation-maximization (EM) algorithms for parameter estimation, yields high accuracy at significantly decreased computational cost relative to the Markov chain Monte Carlo (MCMC) algorithms that have generally been used in earlier studies. We present experiments exploring these modeling and algorithmic issues in two scenarios—the inference of locus-specific ancestries in a population that is assumed to originate from two unknown ancestral populations, and the inference of allele frequencies in one ancestral population given those in another.

Molly Przeworski (University of Chicago): Recent linkage disequilibrium and sperm typing studies suggest that recombination rates vary tremendously across the human genome, with most events occurring in narrow "hotspots". To examine variation in fine-scale recombination patterns among individuals, we used dense, genome-wide SNP data collected in nuclear families to localize crossovers with high spatial resolution. This analysis revealed that overall recombination hotspot usage is similar in males and females, with individual hotspots often active in both sexes. Across the genome, approximately 60% of crossovers occurred in hotspots inferred from studies of linkage disequilibrium. Remarkably, however, we found extensive and heritable variation among both males and females in the proportion of crossovers occurring in these hotspots.

Liisa Kauppi (Memorial Sloan–Kettering Cancer Center): Here, we present a genomic approach to identify mouse crossover hotspots, based on targeting haplotype block boundaries.

Honghua Li (University of Medicine and Dentistry of New Jersey): To investigate the role of meiotic recombination on haplotype block formation, single nucleotide polymorphisms were selected at a high density from a 2.5-Mb region of human chromosome 21. Direct analysis of meiotic recombination by high-throughput multiplex genotyping of 662 single sperm identifies 41 recombinants. The crossovers were nonrandomly distributed within 16 small areas. All, except one, of these crossovers fall in areas where the haplotype structure exhibits breakdown, displaying a strong statistically positive association between crossovers and haplotype block breaks.

Bernard de Massy (Institute of Human Genetics, Centre National de la Recherche Scientifique, France): We previously characterized a recombination hotspot located close to the Psmb9 gene in the mouse major histocompatibility complex by sperm typing, demonstrating that it is a site of recombination initiation. We show here that a haplotype-specific element acts at distance and in trans to activate about 2,000-fold the recombination activity at Psmb9. Another haplotype-specific element acts in cis to repress initiation of recombination, and we propose this control to be due to polymorphisms located within the initiation zone.

Alec Jeffreys (University of Leicester, UK): We now describe the analysis of an unremarkable 206-kb region on human chromosome 1, which identified localized regions of linkage disequilibrium breakdown that mark the locations of sperm crossover hot spots. We also accidentally detected additional hot spots in regions of strong association. This raises the possibility that some hot spots, particularly those in regions of strong association, may have evolved very recently and not left their full imprint on haplotype diversity. These results suggest that hot spots could be very abundant and possibly fluid features of the human genome.