Longitudinal Replication Studies of GWAS Risk SNPs Influencing Body Mass Index over the Course of Childhood and Adulthood

Genome-wide association studies (GWAS) have identified multiple common variants associated with body mass index (BMI). In this study, we tested 23 genotyped GWAS-significant SNPs (p-value<5*10-8) for longitudinal associations with BMI during childhood (3–17 years) and adulthood (18–45 years) for 658 subjects. We also proposed a heuristic forward search for the best joint effect model to explain the longitudinal BMI variation. After using false discovery rate (FDR) to adjust for multiple tests, childhood and adulthood BMI were found to be significantly associated with six SNPs each (q-value<0.05), with one SNP associated with both BMI measurements: KCTD15 rs29941 (q-value<7.6*10-4). These 12 SNPs are located at or near genes either expressed in the brain (BDNF, KCTD15, TMEM18, MTCH2, and FTO) or implicated in cell apoptosis and proliferation (FAIM2, MAP2K5, and TFAP2B). The longitudinal effects of FAIM2 rs7138803 on childhood BMI and MAP2K5 rs2241423 on adulthood BMI decreased as age increased (q-value<0.05). The FTO candidate SNPs, rs6499640 at the 5 ′-end and rs1121980 and rs8050136 downstream, were associated with childhood and adulthood BMI, respectively, and the risk effects of rs6499640 and rs1121980 increased as birth weight decreased. The best joint effect model for childhood and adulthood BMI contained 14 and 15 SNPs each, with 11 in common, and the percentage of explained variance increased from 0.17% and 9.0*10−6% to 2.22% and 2.71%, respectively. In summary, this study evidenced the presence of long-term major effects of genes on obesity development, implicated in pathways related to neural development and cell metabolism, and different sets of genes associated with childhood and adulthood BMI, respectively. The gene effects can vary with age and be modified by prenatal development. The best joint effect model indicated that multiple variants with effects that are weak or absent alone can nevertheless jointly exert a large longitudinal effect on BMI

Indirect DNA Readout by an H-NS Related Protein: Structure of the DNA Complex of the C-Terminal Domain of Ler

Ler, a member of the H-NS protein family, is the master regulator of the LEE pathogenicity island in virulent Escherichia coli strains. Here, we determined the structure of a complex between the DNA-binding domain of Ler (CT-Ler) and a 15-mer DNA duplex. CT-Ler recognizes a preexisting structural pattern in the DNA minor groove formed by two consecutive regions which are narrower and wider, respectively, compared with standard B-DNA. The compressed region, associated with an AT-tract, is sensed by the side chain of Arg90, whose mutation abolishes the capacity of Ler to bind DNA. The expanded groove allows the approach of the loop in which Arg90 is located. This is the first report of an experimental structure of a DNA complex that includes a protein belonging to the H-NS family. The indirect readout mechanism not only explains the capacity of H-NS and other H-NS family members to modulate the expression of a large number of genes but also the origin of the specificity displayed by Ler. Our results point to a general mechanism by which horizontally acquired genes may be specifically recognized by members of the H-NS family

Evolving Programs to Build Artificial Neural Networks

International audienceIn general, the topology of Artificial Neural Networks (ANNs) is human-engineered and learning is merely the process of weight adjustment. However, it is well known that this can lead to sub-optimal solutions. Topology and Weight Evolving Artificial Neural Networks (TWEANNs) can lead to better topologies however, once obtained they remain fixed and cannot adapt to new problems. In this chapter, rather than evolving a fixed structure artificial neural network as in neuroevolution, we evolve a pair of programs that build the network. One program runs inside neurons and allows them to move, change, die or replicate. The other is executed inside dendrites and allows them to change length and weight, be removed, or replicate. The programs are represented and evolved using Cartesian Genetic Programming. From the developed networks multiple traditional ANNs can be extracted, each of which solves a different problem. The proposed approach has been evaluated on multiple classification problems