2 6 9 t e c h n i c a l r e p o r t s Accurate and complete analysis of genome variation in large populations will be required to understand the role of genome variation in complex disease. We present an analytical framework for characterizing genome deletion polymorphism in populations using sequence data that are distributed across hundreds or thousands of genomes. Our approach uses population-level concepts to reinterpret the technical features of sequence data that often reflect structural variation. In the 1000 Genomes Project pilot, this approach identified deletion polymorphism across 168 genomes (sequenced at 4× average coverage) with sensitivity and specificity unmatched by other algorithms. We also describe a way to determine the allelic state or genotype of each deletion polymorphism in each genome; the 1000 Genomes Project used this approach to type 13,826 deletion polymorphisms (48-995,664 bp) at high accuracy in populations. These methods offer a way to relate genome structural polymorphism to complex disease in populations. Describing genome variation in populations and identifying the alleles that influence complex phenotypes will require sequencing thousands of genomes. Genome sequencing will therefore increasingly be performed in clinical and reference cohorts of substantial size 1 . An important challenge will be to identify how genomes vary at large as well as fine scales. Short sequence reads can reflect large variants in several ways: individual reads can span a variant's breakpoints 2,3 ; molecularly paired sequences can flank a variant 4-6 ; and read depth is influenced by the underlying copy number of a genomic segment 7-9 ( Fig. 1) . However, identifying large variants from short sequence reads is error prone: molecular libraries contain millions of chimeric molecules that masquerade as structural variants; read depth varies across the genome in ways that vary among sequencing libraries; and alignment algorithms are misled by the genome's internal repeats. Illustrating this challenge, the 1000 Genomes Project found that even for deeply sequenced (greater than 30×) individual genomes, 14 published and new methods for analyzing deletions generated false discovery rates (FDRs) of 9-89%, such that additional experiments (array CGH, PCR) were required to identify the real variants among the false discoveries 1,10 . These difficulties are potentially more severe in sequence data that are generated on a population scale. As more genomes are sequenced, false discoveries accumulate more quickly than real variants do, as many real variants are simply rediscovered in more genomes. In addition, in population-based studies, investigators may use lower sequence coverage (across many more genomes) than is used for deeply sequenced personal genomes, as the resulting large sample size will allow studies to ascertain more low-frequency alleles and increase the power for relating variation to phenotype. The high false discovery rate of structural variation algorithms in deeply sequenced individual genomes 1,10 has suggested that the problem of accurate inference at lower coverage will be challenging. We hypothesized, however, that sequencing at a population scale will also enable new kinds of analytical approaches. True structural alleles might leave additional footprints in population-scale data (Fig. 1) . Segregating alleles distinguish some genomes from others, substitute for alternative structural alleles, give rise to discrete allelic states in a diploid genome, are often shared across genomes and segregate on haplotypes with other variants 11,12 . Here we show that analysis of structural variation in populations is made far more accurate and powerful by apprehending patterns at a population level. We present the results of an analysis applying these principles to map deletion polymorphism in the genomes of 168 individuals sequenced at low coverage (2×-8× paired-end sequencing on the Illumina platform) in the 1000 Genomes Project pilot. We focus on deletion polymorphism, the most numerous and validated class of structural variation, though the population-level analytic concepts we describe can also be used to analyze other forms of genome variation. We show that population-aware analysis enables structural inference with high accuracy. Our approach can identify an unprecedented number of deletions with few false discoveries, ascertain variants down to sub-kilobase sizes and low allele frequencies, localize breakpoints at high resolution, determine genotype (allelic state) at each locus in each genome and create a high-resolution map of linkage disequilibrium (LD) between single-nucleotide and structural alleles. The resulting dataset has been validated by independent experiments. It comprises a substantial fraction of the deletion loci and all of the structural variation genotype data released by the 1000 Genomes Project pilot 1 .
GenomeStrip REF has been in use at the 1000 Genomes project (The 1000 Genomes Project Consortium, 2015 for genotyping large deletions.
Discovery and genotyping of genome structural polymorphism by sequencing on a population scale
Nature Genetics
Nature Genetics
Biology Medicine