Parentage analysis with genetic markers in natural populations. I. The expected proportion of offspring with unambiguous paternity.

Recent studies indicate that polymorphic genetic markers are potentially helpful in resolving genealogical relationships among individuals in a natural population. Genetic data provide opportunities for paternity exclusion when genotypic incompatibilities are observed among individuals, and the present investigation examines the resolving power of genetic markers in unambiguous positive determination of paternity. Under the assumption that the mother for each offspring in a population is unambiguously known, an analytical expression for the fraction of males excluded from paternity is derived for the case where males and females may be derived from two different gene pools. This theoretical formulation can also be used to predict the fraction of births for each of which all but one male can be excluded from paternity. We show that even when the average probability of exclusion approaches unity, a substantial fraction of births yield equivocal mother-father-offspring determinations. The number of loci needed to increase the frequency of unambiguous determinations to a high level is beyond the scope of current electrophoretic studies in most species. Applications of this theory to electrophoretic data on Chamaelirium luteum (L.) shows that in 2255 offspring derived from 273 males and 70 females, only 57 triplets could be unequivocally determined with eight polymorphic protein loci, even though the average combined exclusionary power of these loci was 73%. The distribution of potentially compatible male parents, based on multilocus genotypes, was reasonably well predicted from the allele frequency data available for these loci. We demonstrate that genetic paternity analysis in natural populations cannot be reliably based on exclusionary principles alone. In order to measure the reproductive contributions of individuals in natural populations, more elaborate likelihood principles must be deployed.

Distribution of genes encoding MSCRAMMs and Pili in clinical and natural populations of Enterococcus faecium.

Enterococcus faecium has recently emerged as an important cause of nosocomial infections. We previously identified 15 predicted surface proteins with characteristics of MSCRAMMs and/or pili and demonstrated that their genes were frequently present in 30 clinical E. faecium isolates studied; one of these, acm, has been studied in further detail. To determine the prevalence of the other 14 genes among various E. faecium populations, we have now assessed 433 E. faecium isolates, including 264 isolates from human clinical infections, 69 isolates from stools of hospitalized patients, 70 isolates from stools of community volunteers, and 30 isolates from animal-related sources. A variable distribution of the 14 genes was detected, with their presence ranging from 51% to 98% of isolates. While 81% of clinical isolates carried 13 or 14 of the 14 genes tested, none of the community group isolates and only 13% of animal isolates carried 13 or 14 genes. The presence of these genes was most frequent in endocarditis isolates, with 11 genes present in all isolates, followed by isolates from other clinical sources. The number of genes significantly associated with clinical versus fecal or animal origin (P = 0.04 to <0.0001) varied from 10 to 13, depending on whether comparisons were made against individual clinical subgroups (endocarditis, blood, and other clinical isolates) or against all clinical isolates combined as one group. The strong association of these genes with clinical isolates raises the possibility that their preservation/acquisition has favored the adaptation of E. faecium to nosocomial environments and/or patients.

Natural history of placebo treatment in adults acquiring travelers' diarrhea in Mexico

The natural history of placebo treated travelers' diarrhea and the prognostic factors of recovery from diarrhea were evaluated using 9 groups of placebo treated subjects from 9 clinical trial studies conducted since 1975, for use as a historical control in the future clinical trial of antidiarrheal agents. All of these studies were done by the same group of investigators in one site (Guadalajara, Mexico). The studies are similar in terms of population, measured parameters, microbiologic identification of enteropathogens and definitions of parameters. The studies had two different durations of followup. In some studies, subjects were followed for two days, and in some they were followed for five days.^ Using definitions established by the Infectious Diseases society of America and the Food and Drug Administration, the following efficacy parameters were evaluated: Time to last unformed stool (TLUS), number of unformed stools post-initiation of placebo treatment for five consecutive days of followup, microbiologic cure, and improvement of diarrhea. Among the groups that were followed for five days, the mean TLUS ranged from 59.1 to 83.5 hours. Fifty percent to 78% had diarrhea lasting more than 48 hours and 25% had diarrhea more than five days. The mean number of unformed stools passed on the first day post-initiation of therapy ranged from 3.6 to 5.8 and, for the fifth day ranged from 0.5 to 1.5. By the end of followup, diarrhea improved in 82.6% to 90% of the subjects. Subjects with enterotoxigenic E. coli had 21.6% to 90.0% microbiologic cure; and subjects with shigella species experienced 14.3% to 60.0% microbiologic cure.^ In evaluating the prognostic factors of recovery from diarrhea (primary efficacy parameter in evaluating the efficacy of antidiarrheal agents against travelers' diarrhea). The subjects from five studies were pooled and the Cox proportional hazard model was used to evaluate the predictors of prolonged diarrhea. After adjusting for design characteristics of each trial, fever with a rate ratio (RR) of 0.40, presence of invasive pathogens with a RR of 0.41, presence of severe abdominal pain and cramps with a RR of 0.50, number of watery stools more than five with a RR of 0.60, and presence of non-invasive pathogens with a RR of 0.84 predicted a longer duration of diarrhea. Severe vomiting with a RR of 2.53 predicted a shorter duration of diarrhea. The number of soft stools, presence of fecal leukocytes, presence of nausea, and duration of diarrhea before enrollment were not associated with duration of diarrhea. ^

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.