To understand the genetic heterogeneity underlying developmental delay, we compare copy-number variants (CNVs) in 15,767 children with intellectual disability and various congenital defects to 8,329 adult controls. We estimate that ~14.2% of disease in these individuals is due to large CNVs > 400 kbp. We find greater CNV enrichment in patients with craniofacial anomalies and cardiovascular defects than epilepsy or autism. We identify 59 pathogenic CNVs including 14 novel or previously weakly supported candidates. We refine the critical interval for several genomic disorders such as the 17q21.31 microdeletion syndrome and identify 940 candidate dosage-sensitive genes. We also develop methods to opportunistically discover small, disruptive CNVs within the large and growing diagnostic array datasets. This evolving CNV morbidity map combined with exome/genome sequencing will be critical for deciphering the genetic basis of developmental delay, intellectual disability, and autism spectrum disorders.

Computational efforts to identify functional elements within genomes leverage comparative sequence information by looking for regions that exhibit evidence of selective constraint. One way of detecting constrained elements is to follow a bottom-up approach by computing constraint scores for individual positions of a multiple alignment and then defining constrained elements as segments of contiguous, highly scoring nucleotide positions. Here we present GERP++, a new tool that uses maximum likelihood evolutionary rate estimation for position-specific scoring and, in contrast to previous bottom-up methods, a novel dynamic programming approach to subsequently define constrained elements. GERP++ evaluates a richer set of candidate element breakpoints and ranks them based on statistical significance, eliminating the need for biased heuristic extension techniques. Using GERP++ we identify over 1.3 million constrained elements spanning over 7% of the human genome. We predict a higher fraction than earlier estimates largely due to the annotation of longer constrained elements, which improves one to one correspondence between predicted elements with known functional sequences. GERP++ is an efficient and effective tool to provide both nucleotide- and element-level constraint scores within deep multiple sequence alignments.

Comparisons of orthologous genomic DNA sequences can be used to characterize regions that have been subject to purifying selection and are enriched for functional elements. We here present the results of such an analysis on an alignment of sequences from 29 mammalian species. The alignment captures ∼3.9 neutral substitutions per site and spans ∼1.9 Mbp of the human genome. We identify constrained elements from 3 bp to over 1 kbp in length, covering ∼5.5% of the human locus. Our estimate for the total amount of nonexonic constraint experienced by this locus is roughly twice that for exonic constraint. Constrained elements tend to cluster, and we identify large constrained regions that correspond well with known functional elements. While constraint density inversely correlates with mobile element density, we also show the presence of unambiguously constrained elements overlapping mammalian ancestral repeats. In addition, we describe a number of elements in this region that have undergone intense purifying selection throughout mammalian evolution, and we show that these important elements are more numerous than previously thought. These results were obtained with Genomic Evolutionary Rate Profiling (GERP), a statistically rigorous and biologically transparent framework for constrained element identification. GERP identifies regions at high resolution that exhibit nucleotide substitution deficits, and measures these deficits as "rejected substitutions." Rejected substitutions reflect the intensity of past purifying selection and are used to rank and characterize constrained elements. We anticipate that GERP and the types of analyses it facilitates will provide further insights and improved annotation for the human genome as mammalian genome sequence data become richer.