Cost and power analysis of linkage disequilibrium methods for locating genes influencing quantitative traits in humans

Linkage disequilibrium methods can be used to find genes influencing quantitative trait variation in humans. Linkage disequilibrium methods can require smaller sample sizes than linkage equilibrium methods, such as the variance component approach to find loci with a specific effect size. The increase in power is at the expense of requiring more markers to be typed to scan the entire genome. This thesis compares different linkage disequilibrium methods to determine which factors influence the power to detect disequilibrium. The costs of disequilibrium and equilibrium tests were compared to determine whether the savings in phenotyping costs when using disequilibrium methods outweigh the additional genotyping costs.^ Nine linkage disequilibrium tests were examined by simulation. Five tests involve selecting isolated unrelated individuals while four involved the selection of parent child trios (TDT). All nine tests were found to be able to identify disequilibrium with the correct significance level in Hardy-Weinberg populations. Increasing linked genetic variance and trait allele frequency were found to increase the power to detect disequilibrium, while increasing the number of generations and distance between marker and trait loci decreased the power to detect disequilibrium. Discordant sampling was used for several of the tests. It was found that the more stringent the sampling, the greater the power to detect disequilibrium in a sample of given size. The power to detect disequilibrium was not affected by the presence of polygenic effects.^ When the trait locus had more than two trait alleles, the power of the tests maximized to less than one. For the simulation methods used here, when there were more than two-trait alleles there was a probability equal to 1-heterozygosity of the marker locus that both trait alleles were in disequilibrium with the same marker allele, resulting in the marker being uninformative for disequilibrium.^ The five tests using isolated unrelated individuals were found to have excess error rates when there was disequilibrium due to population admixture. Increased error rates also resulted from increased unlinked major gene effects, discordant trait allele frequency, and increased disequilibrium. Polygenic effects did not affect the error rates. The TDT, Transmission Disequilibrium Test, based tests were not liable to any increase in error rates.^ For all sample ascertainment costs, for recent mutations ($<$100 generations) linkage disequilibrium tests were less expensive than the variance component test to carry out. Candidate gene scans saved even more money. The use of recently admixed populations also decreased the cost of performing a linkage disequilibrium test. ^

Inheritance of porcine receptors for enterotoxigenic Escherichia coli with fimbriae F4ad and their relation to other F4 receptors.

Enteric Escherichia coli infections are a highly relevant cause of disease and death in young pigs. Breeding genetically resistant pigs is an economical and sustainable method of prevention. Resistant pigs are protected against colonization of the intestine through the absence of receptors for the bacterial fimbriae, which mediate adhesion to the intestinal surface. The present work aimed at elucidation of the mode of inheritance of the F4ad receptor which according to former investigations appeared quite confusing. Intestines of 489 pigs of an experimental herd were examined by a microscopic adhesion test modified in such a manner that four small intestinal sites instead of one were tested for adhesion of the fimbrial variant F4ad. Segregation analysis revealed that the mixed inheritance model explained our data best. The heritability of the F4ad phenotype was estimated to be 0.7±0.1. There are no relations to the strong receptors for variants F4ab and F4ac. Targeted matings allowed the discrimination between two F4ad receptors, that is, a fully adhesive receptor (F4adRFA) expressed on all enterocytes and at all small intestinal sites, and a partially adhesive receptor (F4adRPA) variably expressed at different sites and often leading to partial bacterial adhesion. In pigs with both F4ad receptors, the F4adRPA receptor is masked by the F4adRFA. The hypothesis that F4adRFA must be encoded by at least two complementary or epistatic dominant genes is supported by the Hardy-Weinberg equilibrium statistics. The F4adRPA receptor is inherited as a monogenetic dominant trait. A comparable partially adhesive receptor for variant F4ab (F4abRPA) was also observed but the limited data did not allow a prediction of the mode of inheritance. Pigs were therefore classified into one of eight receptor phenotypes: A1 (F4abRFA/F4acR+/F4adRFA); A2 (F4abRFA/F4acR+/F4adRPA); B (F4abRFA/F4acR+/F4adR-); C1 (F4abRPA/F4acR-/F4adRFA); C2 (F4abRPA/F4acR-/F4adRPA); D1 (F4abR-/F4acR-/F4adRFA); D2 (F4abR-/F4acR-/F4adRPA); E (F4abR-/F4acR-/F4adR-).

Echinococcus P29 antigen: molecular characterization and implication on post-surgery follow-up of CE patients infected with different species of the Echinococcus granulosus complex.

The protein P29 is a potential serological marker for post-treatment monitoring of cystic echinococcosis (CE) especially in young patients. We now have demonstrated that P29 is encoded in the Echinococcus genus by a single gene consisting of 7 exons spanning 1.2 kb of DNA. Variability of the p29 gene at inter- and intra-species level was assessed with 50 cDNA and 280 genomic DNA clones isolated from different E. granulosus s.l. isolates (E. granulosus sensu stricto (G1), E. equinus (G4), E. ortleppi (G5), E. canadensis (G6), E. canadensis (G7) and E. canadensis (G10)) as well as four E. multilocularis isolates. Scarce interspecies polymorphism at the p29 locus was observed and affected predominantly E. granulosus s.s. (G1), where we identified two alleles (A1 and A2) coding for identical P29 proteins and yielding in three genotypes (A1/A1, A2/A2 and A1/A2). Genotypic frequencies expected under Hardy-Weinberg equilibrium revealed a high rate of heterozygosity (47%) that strongly supports the hypothesis that E. granulosus s.s. (G1) is predominantly outbreeding. Comparative sequence analyses of the complete p29 gene showed that phylogenetic relationships within the genus Echinococcus were in agreement with those of previous nuclear gene studies. At the protein level, the deduced P29 amino acid (AA) sequences exhibited a high level of conservation, ranging from 97.9% AA sequence identity among the whole E. granulosus s.l. group to 99.58% identity among E. multilocularis isolates. We showed that P29 proteins of these two species differ by three AA substitutions without implication for antigenicity. In Western-blot analyses, serum antibodies from a human CE patient infected with E. canadensis (G6) strongly reacted with recombinant P29 from E. granulosus s.s. (G1) (recEg(G1)P29). In the same line, human anti-Eg(G1)P29 antibodies bound to recEcnd(G6)P29. Thus, minor AA sequence variations appear not to impair the prognostic serological use of P29.

Traditional linkage analysis in admixed families

Currently there is no general method to study the impact of population admixture within families on the assumptions of random mating and consequently, Hardy-Weinberg equilibrium (HWE) and linkage equilibrium (LE) and on the inference obtained from traditional linkage analysis. ^ First, through simulation, the effect of admixture of two populations on the log of the odds (LOD) score was assessed, using Prostate Cancer as the typical disease model. Comparisons between simulated mixed and homogeneous families were performed. LOD scores under both models of admixture (within families and within a data set of homogeneous families) were closest to the homogeneous family scores of the population having the highest mixing proportion. Random sampling of families or ascertainment of families with disease affection status did not affect this observation, nor did the mode of inheritance (dominant/recessive) or sample size. ^ Second, after establishing the effect of admixture on the LOD score and inference for linkage, the presence of induced disequilibria by population admixture within families was studied and an adjustment procedure was developed. The adjustment did not force all disequilibria to disappear but because the families were adjusted for the population admixture, those replicates where the disequilibria exist are no longer affected by the disequilibria in terms of maximization for linkage. Furthermore, the adjustment was able to exclude uninformative families or families that had such a high departure from HWE and/or LE that their LOD scores were not reliable. ^ Together these observations imply that the presence of families of mixed population ancestry impacts linkage analysis in terms of the LOD score and the estimate of the recombination fraction. ^

Financial Planning for College: What Parents Do To Prepare

This study explores reported parental financial college preparations and the amount parents have saved for college, with a goal of determining strategies used by different parents based on parental college aspirations and expectations for their child, as well as the highest reported parental and grandparental educational levels. Regression analysis indicates that parents' expectations, but not their aspirations, correspond to engagement in financial planning. Family education is strongly associated with taking some financial planning actions and the amount saved. The results may be helpful to those who are working to increase the effectiveness of disseminating college financial information to parents.

Association between glycemic index and glycemic load and the risk of incident coronary heart disease among whites and African Americans with and without type 2 diabetes: The Atherosclerosis Risk in Communities study

Several studies have examined the association between high glycemic index (GI) and glycemic load (GL) diets and the risk for coronary heart disease (CHD). However, most of these studies were conducted primarily on white populations. The primary aim of this study was to examine whether high GI and GL diets are associated with increased risk for developing CHD in whites and African Americans, non-diabetics and diabetics, and within stratifications of body mass index (BMI) and hypertension (HTN). Baseline and 17-year follow-up data from ARIC (Atherosclerosis Risk in Communities) study was used. The study population (13,051) consisted of 74% whites, 26% African Americans, 89% non-diabetics, 11% diabetics, 43% male, 57% female aged 44 to 66 years at baseline. Data from the ARIC food frequency questionnaire at baseline were analyzed to provide GI and GL indices for each subject. Increases of 25 and 30 units for GI and GL respectively were used to describe relationships on incident CHD risk. Adjusted hazard ratios for propensity score with 95% confidence intervals (CI) were used to assess associations. During 17 years of follow-up (1987 to 2004), 1,683 cases of CHD was recorded. Glycemic index was associated with 2.12 fold (95% CI: 1.05, 4.30) increased incident CHD risk for all African Americans and GL was associated with 1.14 fold (95% CI: 1.04, 1.25) increased CHD risk for all whites. In addition, GL was also an important CHD risk factor for white non-diabetics (HR=1.59; 95% CI: 1.33, 1.90). Furthermore, within stratum of BMI 23.0 to 29.9 in non-diabetics, GI was associated with an increased hazard ratio of 11.99 (95% CI: 2.31, 62.18) for CHD in African Americans, and GL was associated with 1.23 fold (1.08, 1.39) increased CHD risk in whites. Body mass index modified the effect of GI and GL on CHD risk in all whites and white non-diabetics. For HTN, both systolic blood pressure and diastolic blood pressure modified the effect on GI and GL on CHD risk in all whites and African Americans, white and African American non-diabetics, and white diabetics. Further studies should examine other factors that could influence the effects of GI and GL on CHD risk, including dietary factors, physical activity, and diet-gene interactions. ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Description of two mass species of Pacific copepods in terms of two polymorphic loci

Population genetics of two species of mass copepods Undinula darwini and Calanus australis, with different range types, is investigated. Both species exhibit considerable genetic diversity, especially C. australis (observed heterozygoticity = 0.36), which inhabits a variable biotope in the zone of the Peru current. Samples of both species exhibited highly significant genetic heterogeneity as well as heterozygote deficiency compared with the situation expected from the Hardy-Weinberg law. Contribution of distance isolation to genetic differentiation of populations is estimated. Gene drift is discussed as a source of heterogeneity in populations of planktic copepods. Possible aspects of population genetic research on marine plank-tic crustaceans are discussed.

Cenozoic terrestrial palynomorphs from the late Oligocene to early Miocene section of sediment core CRP-2/2A (Table 1)

Sparse terrestrial palynomorphs (spores and pollen) were recovered from glacigene Lower Miocene and Oligocene core samples from the Cape Roberts Project (CRP) drillhole CRP-2/2A, Victoria Land Basin, Antarctica. Rarity of palynomorphs probably results from the spares periglacial vegetation in the surrounding landscape at the time of deposition, as well as dilution from rapid sediment accumulation. The Miocene and Late Oligocene vegetation is interpreted as including herb-moss tundra with low-growing woody plants (including Nothofagus and podocarp conifers) in more protected areas, similar to that encountered in the Miocene of CRP-1. Species richness and numbers of specimens increase downhole, a trend that begins very gradually below ~307 mbsf, and increases below ~443 mbsf through the Early Oligocene. These lower assemblages reflect low diversity woody vegetation dominated by several species of Nofhofagus and podocarps, growing in somewhat milder conditions, though still cold temperate to periglacial in the Early Oligocene. The CRP-2/2A core provides new biostratigraphical information, such as the First Appearance Datums (FADS) of Tricolpites sp. a near the Oligocene/Miocene boundary, and Marchantiaceae in the Early/Late Oligocene transition: these are taxa that along with N. lachlaniae, Coptospora spp. and Podocarpidites sp.b characterize assemblages recovered from outcrops of the Pliocene Sirius Group in the Transantarctic Mountains. Some elements of the extremely hardy periglacial tundra vegetation that survived in Antarctica into the Pliocene had their origin in the Oligocene during a time of deteriorating (colder, drier) climatic conditions. The CRP results highlight the long persistence of this tundra vegetation, through approximately 30 million years of dynamically changing climatic conditions. Rare Jurassic and more common Permian-Triassic spores and pollen occur sporadically throughout the core. These are derived from Jurassic Ferrar Group sediments, and from the Permian-Triassic Victoria Group, upper Beacon Supergroup. Higher frequencies of reworked Beacon palynomorphs and coaly organic matter below ~307 mbsf indicate greater erosion of the Beacon Supergroup for this lower part of the core. A color range from black, severely metamorphosed specimens, to light-colored, yellow (indicating low thermal alteration), reworked Permian palynomorphs, indicates local provenance in the dolerite-intruded Beacon strata of the Transantarctic Mountains, as well as areas (now sub-ice) of Beacon strata with little or no associated dolerite well inland (cratonwards) of the present Transantarctic Mountains.