There is a growing recognition that gene conversion can be an important factor in shaping fine-scale patterns of linkage disequilibrium in the human genome. We devised simple multilocus summary statistics for estimating gene-conversion rates from genomewide polymorphism data sets. In addition to being computationally feasible for very large data sets, these summaries were designed to yield robust estimates of gene-conversion rates in the presence of variation in crossing-over rates. Using our summaries, we analyzed 21,840 biallelic single-nucleotide polymorphisms (SNPs) on human chromosome 21. Our results indicate that models including both crossing over and gene conversion fit the overall short-range data (0-5 kb) of chromosome 21 much better than do models including crossing over alone. The estimated ratio of gene-conversion rate to crossing-over rate has a range of 1.6-9.4, depending on the assumed conversion tract length (in the range of 500-50 bp). Removal of the 5,696 SNPs that occur in known mutational hotspots (CpG sites) did not significantly change our conclusions, suggesting that recurrent mutations alone cannot explain our data.