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.     

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.     

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.     

Assessing the impact of population stratification on genetic association studies

Population stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease. It has been proposed that false positive associations due to stratification can be controlled by genotyping a few dozen unlinked genetic markers. To assess stratification empirically, we analyzed data from 11 case-control and case-cohort association studies. We did not detect statistically significant evidence for stratification but did observe that assessments based on a few dozen markers lack power to rule out moderate levels of stratification that could cause false positive associations in studies designed to detect modest genetic risk factors. After increasing the number of markers and samples in a case-cohort study (the design most immune to stratification), we found that stratification was in fact present. Our results suggest that modest amounts of stratification can exist even in well designed studies.