Cyclic hematopoiesis is a stem cell disease in which the number of neutrophils and other blood cells oscillates in weekly phases. Autosomal dominant mutations of ELA2, encoding the protease neutrophil elastase, found in lysosome-like granules, cause cyclic hematopoiesis and most cases of the pre-leukemic disorder severe congenital neutropenia (SCN; ref. 3) in humans. Over 20 different mutations of neutrophil elastase have been identified, but their consequences are elusive, because they confer no consistent effects on enzymatic activity. The similar autosomal recessive disease of dogs, canine cyclic hematopoiesis, is not caused by mutations in ELA2 (data not shown). Here we show that homozygous mutation of the gene encoding the dog adaptor protein complex 3 (AP3) beta-subunit, directing trans-Golgi export of transmembrane cargo proteins to lysosomes, causes canine cyclic hematopoiesis. C-terminal processing of neutrophil elastase exposes an AP3 interaction signal responsible for redirecting neutrophil elastase trafficking from membranes to granules. Disruption of either neutrophil elastase or AP3 perturbs the intracellular trafficking of neutrophil elastase. Most mutations in ELA2 that cause human cyclic hematopoiesis prevent membrane localization of neutrophil elastase, whereas most mutations in ELA2 that cause SCN lead to exclusive membrane localization.

Evidence suggests that lung injury, inflammation and extracellular matrix remodeling precede lung fibrosis in interstitial lung disease (ILD). We examined whether a quantitative measure of increased lung attenuation on computed tomography (CT) detects lung injury, inflammation and extracellular matrix remodeling in community-dwelling adults sampled without regard to respiratory symptoms or smoking. We measured high attenuation areas (HAA; percentage of lung voxels between -600 and -250 Hounsfield Units) on cardiac CT scans of adults enrolled in the Multi-Ethnic Study of Atherosclerosis. HAA was associated with higher serum matrix metalloproteinase-7 (mean adjusted difference 6.3% per HAA doubling, 95% CI 1.3 to 11.5), higher interleukin-6 (mean adjusted difference 8.8%, 95% CI 4.8 to 13.0), lower forced vital capacity (mean adjusted difference -82 mL, 95% CI -119 to -44), lower 6-minute walk distance (mean adjusted difference -40 m, 95% CI -1 to -80), higher odds of interstitial lung abnormalities at 9.5 years (adjusted OR 1.95, 95% CI 1.43 to 2.65), and higher all cause-mortality rate over 12.2 years (HR 1.58, 95% CI 1.39 to 1.79). High attenuation areas are associated with biomarkers of inflammation and extracellular matrix remodeling, reduced lung function, interstitial lung abnormalities, and a higher risk of death among community-dwelling adults.

Genome-wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene-trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N > 80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene-environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention.

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1 , but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2-5 . It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be fully recovered from whole-genome sequence (WGS) data on 21,620 unrelated individuals of European ancestry. We assigned 47.1 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned variation accordingly. The estimated heritability was 0.79 (SE 0.09) for height and 0.40 (SE 0.09) for BMI, consistent with pedigree estimates. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein altering variants, consistent with negative selection thereon. Cumulatively variants in the MAF range of 0.0001 to 0.1 explained 0.54 (SE 0.05) and 0.51 (SE 0.11) of heritability for height and BMI, respectively. Our results imply that the still missing heritability of complex traits and disease is accounted for by rare variants, in particular those in regions of low LD.