Forest cover change and its effects on the livelihood of the dependent local people:the case of Sesheke district of Zambia

Objective--To investigate forest cover change in Masese Local Forest Reserve and its effects on the livelihoods of the dependent communities; to establish the occurrence of forest cover change in Masese Local Forest Reserve; to determine the extent of forest cover change in Masese from 1990 to 2005; to determine the tree species that have been affected by forest cover change in Masese; and to assess the effects of forest cover change on the livelihoods of the dependent communities. Methods--Interviews and questionnaire were conducted with a proportion of 36 households from Ma- ondo and 84 households from Mulimambango settlements. Scenes of landsat images of 1990, 2000, 2005 and Earth Google image for Masese were used for map analysis. Also remote sensing was used to analyze landsat images and Google image to detect forest and non-forest classes. Results and Conclu- sion-Forest cover change has adverse implication on the provision of wood forest products and non- wood forest products to the dependent local communities. The declining status of dominant Baikiaea Forest that has exploitable species for building materials and fuel wood has affected the livelihoods of the dependent local communities. There is also increased cost and shortage of fire wood and building materials due to deforestation in Masese Forest Reserve.     

Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.