Tumour evolution inferred by single-cell sequencing

Genomic analysis provides insights into the role of copy number variation in disease, but most methods are not designed to resolve mixed populations of cells. In tumours, where genetic heterogeneity is common, very important information may be lost that would be useful for reconstructing evolutionary history. Here we show that with flow-sorted nuclei, whole genome amplification and next generation sequencing we can accurately quantify genomic copy number within an individual nucleus. We apply single-nucleus sequencing to investigate tumour population structure and evolution in two human breast cancer cases. Analysis of 100 single cells from a polygenomic tumour revealed three distinct clonal subpopulations that probably represent sequential clonal expansions. Additional analysis of 100 single cells from a monogenomic primary tumour and its liver metastasis indicated that a single clonal expansion formed the primary tumour and seeded the metastasis. In both primary tumours, we also identified an unexpectedly abundant subpopulation of genetically diverse 'pseudodiploid' cells that do not travel to the metastatic site. In contrast to gradual models of tumour progression, our data indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.     

ANG mutations segregate with familial and 'sporadic' amyotrophic lateral sclerosis

We recently identified angiogenin (ANG) as a candidate susceptibility gene for amyotrophic lateral sclerosis (ALS), a neurodegenerative disorder characterized by adult-onset loss of motor neurons. We now report the finding of seven missense mutations in 15 individuals, of whom four had familial ALS and 11 apparently 'sporadic' ALS. Our findings provide further evidence that variations in hypoxia-inducible genes have an important role in motor neuron degeneration.     

MYH9 is associated with nondiabetic end-stage renal disease in African Americans

As end-stage renal disease (ESRD) has a four times higher incidence in African Americans compared to European Americans, we hypothesized that susceptibility alleles for ESRD have a higher frequency in the West African than the European gene pool. We carried out a genome-wide admixture scan in 1,372 ESRD cases and 806 controls and found a highly significant association between excess African ancestry and nondiabetic ESRD (lod score = 5.70) but not diabetic ESRD (lod = 0.47) on chromosome 22q12. Each copy of the European ancestral allele conferred a relative risk of 0.50 (95% CI = 0.39-0.63) compared to African ancestry. Multiple common SNPs (allele frequencies ranging from 0.2 to 0.6) in the gene encoding nonmuscle myosin heavy chain type II isoform A (MYH9) were associated with two to four times greater risk of nondiabetic ESRD and accounted for a large proportion of the excess risk of ESRD observed in African compared to European Americans.     

Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation

Allele-specific DNA methylation (ASM) is a hallmark of imprinted genes, but ASM in the larger nonimprinted fraction of the genome is less well characterized. Using methylation-sensitive SNP analysis (MSNP), we surveyed the human genome at 50K and 250K resolution, identifying ASM as recurrent genotype call conversions from heterozygosity to homozygosity when genomic DNAs were predigested with the methylation-sensitive restriction enzyme HpaII. Using independent assays, we confirmed ASM at 16 SNP-tagged loci distributed across various chromosomes. At 12 of these loci (75%), the ASM tracked strongly with the sequence of adjacent SNPs. Further analysis showed allele-specific mRNA expression at two loci from this methylation-based screen--the vanin and CYP2A6-CYP2A7 gene clusters--both implicated in traits of medical importance. This recurrent phenomenon of sequence-dependent ASM has practical implications for mapping and interpreting associations of noncoding SNPs and haplotypes with human phenotypes.     

Extremely low-coverage sequencing and imputation increases power for genome-wide association studies

Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.     

Nesting and summer habitat use by translocated Sage Grouse ( Centrocercus Urophasianus ) in central Idaho

We translocated 196 Sage Grouse ( Centrocercus urophasianus into Sawtooth Valley, Idaho, during March-April 1986-87 to augment a small resident population. Forty-four grouse equipped with radio transmitters were monitored through spring and summer. Nest sites ( n = 6) had greater ( P = .032) horizontal cover than did independent random plots ( n = 7). During summer, grouse used sites ( n = 50) with taller live and dead shrub heights, greater shrub canopy cover, and more ground litter ( P n = 50) 50-300 m from use sites. Distance to edge and mountain sagebrush ( Artemisia tridentata vaseyana ) density best separated use sites from independent random plots in logistic regression analysis and correctly classified 64% of the use sites and 78% of the independent random plots. Sage Grouse used sties that had narrower frequency distributions for many variables than did independent plots ( P < .04), suggesting selection for uniform habitat.     

A state space approach to forecasting the final vintage of revised data with an application to the index of industrial production

There is considerable interest in the index of industrial production (IIP) as an indicator of the state of the UK's industrial base and, more generally, as a leading economic indicator. However, this index, in common with a number of key macroeconomic time series, is subject to revision as more information becomes available. This raises the problem of forecasting the final vintage of data on IIP. We construct a state space model to solve this problem which incorporates bias adjustments, a model of the measurement error process, and a dynamic model for the final vintage of IIP. Application of the Kalman filter produces an optimal forecast of the final vintage of data.