Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.

Quantum transport in a single molecular junction

The craze for faster and smaller electronic devices has never gone down and this has always kept researchers on their toes. Following Moore’s law, which states that the number of transistors in a single chip will double in every 18 months, today “30 million transistors can fit into the head of a 1.5 mm diameter pin”. But this miniaturization cannot continue indefinitely due to the ‘quantum leakage’ limit in the thickness of the insulating layer between the gate electrode and the current carrying channel. To bypass this limitation, scientists came up with the idea of using vastly available organic molecules as components in an electronic device. One of the primary challenges in this field was the ability to perform conductance measurements across single molecular junctions. Once that was achieved the focus shifted to a deeper understanding of the underlying physics behind the electron transport across these molecular scale devices. Our initial theoretical approach is based on the conventional Non-Equilibrium Green Function(NEGF) formulation, but the self-energy of the leads is modified to include a weighting factor that ensures negligible current in the absence of a molecular pathway as observed in a Mechanically Controlled Break Junction (MCBJ) experiment. The formulation is then made parameter free by a more careful estimation of the self-energy of the leads. The calculated conductance turns out to be atleast an order more than the experimental values which is probably due to a strong chemical bond at the metal-molecule junction unlike in the experiments. The focus is then shifted to a comparative study of charge transport in molecular wires of different lengths within the same formalism. The molecular wires, composed of a series of organic molecules, are sanwiched between two gold electrodes to make a two terminal device. The length of the wire is increased by sequentially increasing the number of molecules in the wire from 1 to 3. In the low bias regime all the molecular devices are found to exhibit Ohmic behavior. However, the magnitude of conductance decreases exponentially with increase in length of the wire. In the next study, the relative contribution of the ‘in-phase’ and the ‘out-of-phase’ components of the total electronic current under the influence of an external bias is estimated for the wires of three different lengths. In the low bias regime, the ‘out-of-phase’ contribution to the total current is minimal and the ‘in-phase’ elastic tunneling of the electrons is responsible for the net electronic current. This is true irrespective of the length of the molecular spacer. In this regime, the current-voltage characteristics follow Ohm’s law and the conductance of the wires is found to decrease exponentially with increase in length which is in agreement with experimental results. However, after a certain ‘off-set’ voltage, the current increases non-linearly with bias and the ‘out-of-phase’ tunneling of electrons reduces the net current substantially. Subsequently, the interaction of conduction electrons with the vibrational modes as a function of external bias in the three different oligomers is studied since they are one of the main sources of phase-breaking scattering. The number of vibrational modes that couple strongly with the frontier molecular orbitals are found to increase with length of the spacer and the external field. This is consistent with the existence of lowest ‘off-set’ voltage for the longest wire under study.