Analyzing Copy Number Variation with Droplet Digital PCR.

Many genomic segments vary in copy number among individuals of the same species, or between cancer and normal cells within the same person. Correctly measuring this copy number variation is critical for studying its genetic properties, its distribution in populations and its relationship to phenotypes. Droplet digital PCR (ddPCR) enables accurate measurement of copy number by partitioning a PCR reaction into thousands of nanoliter-scale droplets, so that a genomic sequence of interest-whose presence or absence in a droplet is determined by end-point fluorescence-can be digitally counted. Here, we describe how we analyze copy number variants using ddPCR and review the design of effective assays, the performance of ddPCR with those assays, the optimization of reactions, and the interpretation of data.

Using Droplet Digital PCR to Analyze Allele-Specific RNA Expression.

Genome-wide association studies have discovered thousands of common alleles that associate with human phenotypes and disease. Many of these variants are in non-protein-coding (regulatory) regions and are believed to affect phenotypes by modifying gene expression. In any organism with a diploid genome, such as humans, measuring the expression of each allele of a gene provides a well-controlled way to identify allelic influences on that gene's expression. Here, we describe a protocol for precisely measuring the allele-specific expression of individual genes. This method targets the nucleotide differences between the two alleles of a gene within an individual and measures the "allelic skew," the extent to which one allele is expressed more than the other. We cover the design of effective assays, the optimization of reactions, and the interpretation of the resulting data.

Complex and multi-allelic copy number variation in human disease.

Hundreds of copy number variants are complex and multi-allelic, in that they have many structural alleles and have rearranged multiple times in the ancestors who contributed chromosomes to current humans. Not only are the relationships of these multi-allelic CNVs (mCNVs) to phenotypes generally unknown, but many mCNVs have not yet been described at the basic levels-alleles, allele frequencies, structural features-that support genetic investigation. To date, most reported disease associations to these variants have been ascertained through candidate gene studies. However, only a few associations have reached the level of acceptance defined by durable replications in many cohorts. This likely stems from longstanding challenges in making precise molecular measurements of the alleles individuals have at these loci. However, approaches for mCNV analysis are improving quickly, and some of the unique characteristics of mCNVs may assist future association studies. Their various structural alleles are likely to have different magnitudes of effect, creating a natural allelic series of growing phenotypic impact and giving investigators a set of natural predictions and testable hypotheses about the extent to which each allele of an mCNV predisposes to a phenotype. Also, mCNVs' low-to-modest correlation to individual single-nucleotide polymorphisms (SNPs) may make it easier to distinguish between mCNVs and nearby SNPs as the drivers of an association signal, and perhaps, make it possible to preliminarily screen candidate loci, or the entire genome, for the many mCNV-disease relationships that remain to be discovered.

Structural forms of the human amylase locus and their relationships to SNPs, haplotypes and obesity.

Hundreds of genes reside in structurally complex, poorly understood regions of the human genome. One such region contains the three amylase genes (AMY2B, AMY2A and AMY1) responsible for digesting starch into sugar. Copy number of AMY1 is reported to be the largest genomic influence on obesity, although genome-wide association studies for obesity have found this locus unremarkable. Using whole-genome sequence analysis, droplet digital PCR and genome mapping, we identified eight common structural haplotypes of the amylase locus that suggest its mutational history. We found that the AMY1 copy number in an individual's genome is generally even (rather than odd) and partially correlates with nearby SNPs, which do not associate with body mass index (BMI). We measured amylase gene copy number in 1,000 obese or lean Estonians and in 2 other cohorts totaling ∼3,500 individuals. We had 99% power to detect the lower bound of the reported effects on BMI, yet found no association.