Whole genome association mapping by incompatibilities and local perfect phylogenies

BACKGROUND: With current technology, vast amounts of data can be cheaply and efficiently produced in association studies, and to prevent data analysis to become the bottleneck of studies, fast and efficient analysis methods that scale to such data set sizes must be developed. RESULTS: We present a fast method for accurate localisation of disease causing variants in high density case-control association mapping experiments with large numbers of cases and controls. The method searches for significant clustering of case chromosomes in the "perfect" phylogenetic tree defined by the largest region around each marker that is compatible with a single phylogenetic tree. This perfect phylogenetic tree is treated as a decision tree for determining disease status, and scored by its accuracy as a decision tree. The rationale for this is that the perfect phylogeny near a disease affecting mutation should provide more information about the affected/unaffected classification than random trees. If regions of compatibility contain few markers, due to e.g. large marker spacing, the algorithm can allow the inclusion of incompatibility markers in order to enlarge the regions prior to estimating their phylogeny. Haplotype data and phased genotype data can be analysed. The power and efficiency of the method is investigated on 1) simulated genotype data under different models of disease determination 2) artificial data sets created from the HapMap ressource, and 3) data sets used for testing of other methods in order to compare with these. Our method has the same accuracy as single marker association (SMA) in the simplest case of a single disease causing mutation and a constant recombination rate. However, when it comes to more complex scenarios of mutation heterogeneity and more complex haplotype structure such as found in the HapMap data our method outperforms SMA as well as other fast, data mining approaches such as HapMiner and Haplotype Pattern Mining (HPM) despite being significantly faster. For unphased genotype data, an initial step of estimating the phase only slightly decreases the power of the method. The method was also found to accurately localise the known susceptibility variants in an empirical data set – the ΔF508 mutation for cystic fibrosis – where the susceptibility variant is already known – and to find significant signals for association between the CYP2D6 gene and poor drug metabolism, although for this dataset the highest association score is about 60 kb from the CYP2D6 gene. CONCLUSION: Our method has been implemented in the Blossoc (BLOck aSSOCiation) software. Using Blossoc, genome wide chip-based surveys of 3 million SNPs in 1000 cases and 1000 controls can be analysed in less than two CPU hours

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

A Longitudinal Analysis of Mosquito Net Ownership and Use in an Indigenous Batwa Population after a Targeted Distribution

Major efforts for malaria prevention programs have gone into scaling up ownership and use of insecticidal mosquito nets, particularly in sub-Saharan Africa where the malaria burden is high. Socioeconomic inequities in access to long lasting insecticidal nets (LLINs) are reduced with free distributions of nets. However, the relationship between social factors and retention of nets after a free distribution has been less studied, particularly using a longitudinal approach. Our research aimed to estimate the ownership and use of LLINs, and examine the determinants of LLIN retention, within an Indigenous Batwa population after a free LLIN distribution. Two LLINs were given free of charge to each Batwa household in Kanungu District, Uganda in November 2012. Surveyors collected data on LLIN ownership and use through six cross-sectional surveys pre- and post-distribution. Household retention, within household access, and individual use of LLINs were assessed over an 18-month period. Socioeconomic determinants of household retention of LLINs post-distribution were modelled longitudinally using logistic regression with random effects. Direct house-to-house distribution of free LLINs did not result in sustainable increases in the ownership and use of LLINs. Three months post-distribution, only 73% of households owned at least one LLIN and this period also saw the greatest reduction in ownership compared to other study periods. Eighteen-months post distribution, only a third of households still owned a LLIN. Self-reported age-specific use of LLINs was generally higher for children under five, declined for children aged 6–12, and was highest for older adults aged over 35. In the model, household wealth was a significant predictor of LLIN retention, controlling for time and other variables. This research highlights on-going socioeconomic inequities in access to malaria prevention measures among the Batwa in southwestern Uganda, even after free distribution of LLINs, and provides critical information to inform local malaria programs on possible intervention entry-points to increase access and use among this marginalized population

Segmental copy number variation shapes tissue transcriptomes.

Copy number variation (CNV) is a key source of genetic diversity, but a comprehensive understanding of its phenotypic effect is only beginning to emerge. We have generated a CNV map in wild mice and classical inbred strains. Genome-wide expression data from six major organs show not only that expression of genes within CNVs tend to correlate with copy number changes, but also that CNVs influence the expression of genes in their vicinity, an effect that extends up to half a megabase. Genes within CNVs show lower expression and more specific spatial expression patterns than genes mapping elsewhere. Our analyses reveal differential constraint on copy number changes of genes expressed in different tissues. Dosage alterations of brain-expressed genes are less frequent than those of other genes and are buffered by tighter transcriptional regulation. Our study provides initial evidence that CNVs shape tissue transcriptomes on a global scale