Decius S. Wade\u27s The Common Law

Decius S. Wade\u27s The Common La

Thermodynamically stable lithium silicides and germanides from density-functional theory calculations

Density-functional-theory (DFT) calculations have been performed on the Li-Si and Li-Ge systems. Lithiated Si and Ge, including their metastable phases, play an important technological r\^ole as Li-ion battery (LIB) anodes. The calculations comprise structural optimisations on crystal structures obtained by swapping atomic species to Li-Si and Li-Ge from the X-Y structures in the International Crystal Structure Database, where X={Li,Na,K,Rb,Cs} and Y={Si,Ge,Sn,Pb}. To complement this at various Li-Si and Li-Ge stoichiometries, ab initio random structure searching (AIRSS) was also performed. Between the ground-state stoichiometries, including the recently found Li$_{17}$Si$_{4}$ phase, the average voltages were calculated, indicating that germanium may be a safer alternative to silicon anodes in LIB, due to its higher lithium insertion voltage. Calculations predict high-density Li$_1$Si$_1$ and Li$_1$Ge$_1$ $P4/mmm$ layered phases which become the ground state above 2.5 and 5 GPa respectively and reveal silicon and germanium's propensity to form dumbbells in the Li$_x$Si, $x=2.33-3.25$ stoichiometry range. DFT predicts the stability of the Li$_{11}$Ge$_6$ $Cmmm$, Li$_{12}$Ge$_7$ $Pnma$ and Li$_7$Ge$_3$ $P32_12$ phases and several new Li-Ge compounds, with stoichiometries Li$_5$Ge$_2$, Li$_{13}$Ge$_5$, Li$_8$Ge$_3$ and Li$_{13}$Ge$_4$.Comment: 10 pages, 5 figure

Linkage disequilibrium mapping via cladistic analysis of phase-unknown genotypes and inferred haplotypes in the Genetic Analysis Workshop 14 simulated data

We recently described a method for linkage disequilibrium (LD) mapping, using cladistic analysis of phased single-nucleotide polymorphism (SNP) haplotypes in a logistic regression framework. However, haplotypes are often not available and cannot be deduced with certainty from the unphased genotypes. One possible two-stage approach is to infer the phase of multilocus genotype data and analyze the resulting haplotypes as if known. Here, haplotypes are inferred using the expectation-maximization (EM) algorithm and the best-guess phase assignment for each individual analyzed. However, inferring haplotypes from phase-unknown data is prone to error and this should be taken into account in the subsequent analysis. An alternative approach is to analyze the phase-unknown multilocus genotypes themselves. Here we present a generalization of the method for phase-known haplotype data to the case of unphased SNP genotypes. Our approach is designed for high-density SNP data, so we opted to analyze the simulated dataset. The marker spacing in the initial screen was too large for our method to be effective, so we used the answers provided to request further data in regions around the disease loci and in null regions. Power to detect the disease loci, accuracy in localizing the true site of the locus, and false-positive error rates are reported for the inferred-haplotype and unphased genotype methods. For this data, analyzing inferred haplotypes outperforms analysis of genotypes. As expected, our results suggest that when there is little or no LD between a disease locus and the flanking region, there will be no chance of detecting it unless the disease variant itself is genotyped

An Evaluation of Statistical Approaches to Rare Variant Analysis in Genetic Association Studies

Genome-wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large-scale re-sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population-based genetic studies when data are available from re-sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re-sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010. © 2009 Wiley-Liss, Inc

Lithiation of silicon via lithium Zintl-defect complexes

An extensive search for low-energy lithium defects in crystalline silicon using density-functional-theory methods and the ab initio random structure searching (AIRSS) method shows that the four-lithium-atom substitutional point defect is exceptionally stable. This defect consists of four lithium atoms with strong ionic bonds to the four under-coordinated atoms of a silicon vacancy defect, similar to the bonding of metal ions in Zintl phases. This complex is stable over a range of silicon environments, indicating that it may aid amorphization of crystalline silicon and form upon delithiation of the silicon anode of a Li-ion rechargeable battery.Comment: 4 pages, 3 figure