Digital PCR enables the absolute quantitation of nucleic acids in a sample. The lack of scalable and practical technologies for digital PCR implementation has hampered the widespread adoption of this inherently powerful technique. Here we describe a high-throughput droplet digital PCR (ddPCR) system that enables processing of ∼2 million PCR reactions using conventional TaqMan assays with a 96-well plate workflow. Three applications demonstrate that the massive partitioning afforded by our ddPCR system provides orders of magnitude more precision and sensitivity than real-time PCR. First, we show the accurate measurement of germline copy number variation. Second, for rare alleles, we show sensitive detection of mutant DNA in a 100 000-fold excess of wildtype background. Third, we demonstrate absolute quantitation of circulating fetal and maternal DNA from cell-free plasma. We anticipate this ddPCR system will allow researchers to explore complex genetic landscapes, discover and validate new disease associations, and define a new era of molecular diagnostics.

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Despite the recent rapid growth in genome-wide data, much of human variation remains entirely unexplained. A significant challenge in the pursuit of the genetic basis for variation in common human traits is the efficient, coordinated collection of genotype and phenotype data. We have developed a novel research framework that facilitates the parallel study of a wide assortment of traits within a single cohort. The approach takes advantage of the interactivity of the Web both to gather data and to present genetic information to research participants, while taking care to correct for the population structure inherent to this study design. Here we report initial results from a participant-driven study of 22 traits. Replications of associations (in the genes OCA2, HERC2, SLC45A2, SLC24A4, IRF4, TYR, TYRP1, ASIP, and MC1R) for hair color, eye color, and freckling validate the Web-based, self-reporting paradigm. The identification of novel associations for hair morphology (rs17646946, near TCHH; rs7349332, near WNT10A; and rs1556547, near OFCC1), freckling (rs2153271, in BNC2), the ability to smell the methanethiol produced after eating asparagus (rs4481887, near OR2M7), and photic sneeze reflex (rs10427255, near ZEB2, and rs11856995, near NR2F2) illustrates the power of the approach.

Although a few hundred single nucleotide polymorphisms (SNPs) suffice to infer close familial relationships, high density genome-wide SNP data make possible the inference of more distant relationships such as 2 nd to 9 th cousinships. In order to characterize the relationship between genetic similarity and degree of kinship given a timeframe of 100–300 years, we analyzed the sharing of DNA inferred to be identical by descent (IBD) in a subset of individuals from the 23andMe customer database (n = 22,757) and from the Human Genome Diversity Panel (HGDP-CEPH, n = 952). With data from 121 populations, we show that the average amount of DNA shared IBD in most ethnolinguistically-defined populations, for example Native American groups, Finns and Ashkenazi Jews, differs from continentally-defined populations by several orders of magnitude. Via extensive pedigree-based simulations, we determined bounds for predicted degrees of relationship given the amount of genomic IBD sharing in both endogamous and ‘unrelated’ population samples. Using these bounds as a guide, we detected tens of thousands of 2 nd to 9 th degree cousin pairs within a heterogenous set of 5,000 Europeans. The ubiquity of distant relatives, detected via IBD segments, in both ethnolinguistic populations and in large ‘unrelated’ populations samples has important implications for genetic genealogy, forensics and genotype/phenotype mapping studies.

To aid studies of molecular evolution and to assist in gene prediction research, we have constructed an Exon-Intron Database (EID) in FASTA format. Currently, the database is derived from GenBank release 112, and it contains 51 289 protein-coding genes (287 209 exons) that harbor introns, along with extensive descriptions of each gene and its DNA and protein sequences, as well as splice motif information. There is 17% redundancy inherited from GenBank-a purge at the 99% identity level reduced the database to 42 460 genes (243 589 exons). We have created subdatabases of genes whose intron positions have been experimentally determined. One such database, constructed by comparing genomic and mRNA sequences, contains 11 242 genes (62 474 exons). A larger database of 22 196 genes (105 595 exons) was constructed by selecting on keywords to eliminate computer-predicted genes. By examining the two nucleotides adjacent to the intron boundary, we infer that there is a 2% rate of errors or other deviations from the standard GTellipsisAG motif in nuclear genes. This criterion can be used to eliminate 4921 genes from the overall database. Various tools are provided to enable generation of user-specific subsets of the EID. The EID distribution can be obtained from http://mcb.harvard.edu/gilbert/EID

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Nucleotides surrounding a codon influence the choice of this particular codon from among the group of possible synonymous codons. The strongest influence on codon usage arises from the nucleotide immediately following the codon and is known as the N1 context. We studied the relative abundance of codons with N1 contexts in genes from four eukaryotes for which the entire genomes have been sequenced: Homo sapiens, Drosophila melanogaster, Caenorhabditis elegans and Arabidopsis thaliana. For all the studied organisms it was found that 90% of the codons have a statistically significant N1 context-dependent codon bias. The relative abundance of each codon with an N1 context was compared with the relative abundance of the same 4mer oligonucleotide in the whole genome. This comparison showed that in about half of all cases the context-dependent codon bias could not be explained by the sequence composition of the genome. Ranking statistics were applied to compare context-dependent codon biases for codons from different synonymous groups. We found regularities in N1 context-dependent codon bias with respect to the codon nucleotide composition. Codons with the same nucleotides in the second and third positions and the same N1 context have a statistically significant correlation of their relative abundances.