Hepatitis C is associated with type 2 diabetes mellitus in HIV-infected persons without traditional risk factors

OBJECTIVES

Hepatitis C virus (HCV) monoinfection has been linked to type 2 diabetes mellitus (DM). We evaluated the effect of HCV on risk of DM in relation to traditional risk factors such as age, body mass index (BMI) and family history of DM in an HIV-infected population.

METHODS

This was a retrospective, cross-sectional study of 1529 HIV-infected out-patients. An adjusted multivariate logistic regression was performed to assess the effect of HCV seropositivity on DM stratified by low and high propensity for DM based on age, BMI and family history.

RESULTS

Race, age, BMI, family history and HCV were associated with DM. Use of protease inhibitors (PIs) was not associated with DM, but HIV/HCV-coinfected patients were less likely to be on PIs than those with HIV infection alone. In a multivariate analysis controlled for race, the association between HCV and DM was stronger in lean, young patients without a family history of DM; the low-risk group. No association between HCV and type 2 DM was seen in patients who were older or overweight or had a family history of DM; the high-risk group. PI use did not affect the association between HCV and DM.

CONCLUSIONS

Hepatitis C is an independent risk factor for type 2 diabetes in young, lean, HIV-infected patients. HIV-infected patients with HCV infection, regardless of whether they are on PIs, should be carefully screened for DM even if traditional risk factors for DM are not present.

Integrated designs for gene discovery and characterization

Recent advances, including near completion of the human genome map, ever improving high-throughput technologies, and successes in discovering chronic disease-related genes, have stimulated the further development of genetic epidemiology. The primary mission of genetic epidemiology is to discover and characterize genes, whether independent of or interactive with environmental factors, that cause human diseases. To accomplish such a mission, genetic epidemiology needs to integrate both genetic and epidemiologic approaches. One of the challenges facing such an integrated approach is the identification of study designs that are efficient for both gene discovery and characterization. Because designs for gene discovery alone and designs for gene characterization alone have been elaborated in the other two panels, the focus of this paper is to describe those designs that may be useful for discovery and characterization jointly, including case-family and case-control-family designs. Examples of integrated designs are described, and studies of breast cancer conducted at the Fred Hutchinson Cancer Research Center are used for illustration. Finally, related analytic issues are also discussed.

A note on a conditional-likelihood approach for family-based association studies of candidate genes

The family-based association study design is a variation of the case-control study design, where unaffected family members instead of unrelated subjects are sampled as controls. This variation is useful in assessing the effects of candidate genes on disease, because it avoids false associations caused by admixture of populations. A complication of this design is that because of an inherited genotypic correlation among family members, the genotypic distributions between cases and relative controls may be distorted by the ascertainment criteria of families, which could involve not only cases and relative controls, but also other relatives. Analyzing such data naively may lead to biased estimates of relative risk. In this note, we will discuss the consistency of a conditional-likelihood approach. We show analytically that maximum conditional-likelihood estimators are consistent for the true relative risks, if genotypes for family members are exchangeable under the sampling process, for example, sibling clusters. Besides being straightforward conceptually and computationally, this approach is robust to ascertainment bias and naturally accommodates genetic heterogeneity across families.

Mapping alcoholism genes using linkage/linkage disequilibrium analysis

Using a recently developed semiparametric method for combined linkage/linkage-disequilibrium analysis, we analyzed the Collaborative Study on the Genetics of Alcoholism data subset developed for Genetic Analysis Workshop 11 (GAW11). This semiparametric approach estimates recombination fractions for linkage, marker log odds ratios for linkage-disequilibrium, their product for combined linkage/linkage-disequilibrium, and corresponding z-scores. We used two outcomes: alcohol dependence and "alcoholism-free" and a genome-wide significance level of 4.1 (which corresponds to a genome-wide lod score of 3.6). For the alcohol dependence outcome, we observed significant linkage signals at D1S1588-D1S1631, D1S547, D2S399, D2S425, D4S2361, D7S1796, and D7S1824. We also found significant linkage-disequilibrium signals at D1S547 and D7S1795. For the "alcoholism-free" outcome, we found significant linkage signals at D4S2457, D41651 (both flank ADH3), D11S2359, and D16S47 and significant linkage-disequilibrium signals at D4S2361, FABP2, D11S2359, D19S431 and D19S47-D19S198-D19S601.

A genome-wide scan for a simulated data set using two newly developed methods

A genome-wide scan of a simulated data set for fictitious disease genes was conducted using both semiparametric and nonparametric methods. The semiparametric model-based method, which tests for linkage/linkage disequilibrium separately and together, correctly identified all three underlying disease loci along with two false positives through the linkage analysis. However, the nonparametric model-free method which tests combined linkage/linkage disequilibrium, failed to yield any results due to the lack of linkage disequilibrium information in the data.

An efficient protocol for rare mutation genotyping in a large population

We introduce a method to efficiently detect rare mutations for individual subjects in a large population by pooling samples and retesting subgroups of positive pooled samples. We conducted computer simulations of this method and discovered that it seems efficient for mutation prevalences less than 0.1, regardless of the number of samples. The simulations also indicate that splitting the pooled samples into three to five subgroups at each level is optimal. The expected number of necessary tests and relative efficiency of this method are given, by mutation prevalence and sample size.

An efficient, robust and unified method for mapping complex traits (III): combined linkage/linkage-disequilibrium analysis

Extending the method for linkage analysis [Zhao et al., 1998a: Am. J. Med. Genet. 77:366-383; 1998b: Am. J. Med. Genet. 79:49-61], this article describes a method for the linkage-disequilibrium analysis, and for combining linkage and linkage-disequilibrium analyses. As highly dense markers are increasingly used in genome scans, one or more markers are not only linked with the disease genes if they exist, but also likely in linkage-disequilibrium with those putative genes. Hence, linkage-disequilibrium analysis potentially offers additional information about positions of putative disease genes. Combining both linkage and linkage-disequilibrium signals, this approach is able to improve positional signals. As before, the proposed method is a model-based approach, but semiparametric via the estimating equation technique. Under the assumptions of penetrance and allele frequency, this method efficiently estimates recombination fractions for linkage analysis and odds ratios for linkage-disequilibrium analysis. As described in two previous papers, this method is relatively more robust than the lod score methods, since it requires weaker assumption than conditional independence. While the estimated recombination fractions are used for inference as part of linkage analysis, the estimated odds ratios are used for linkage-disequilibrium inference and combined linkage, and linkage-disequilibrium parameters can be used to test combined linkage/linkage-disequilibrium analysis. This approach has been implemented, named gSCAN, and its compiled version is available for trial on request via the web site (http:/lynx.fhcrc.org/qge). We applied this new approach to affected sib-pair data collected for the genome scan to localize type 1 diabetes genes. Under an assumed autosomal dominant gene model, the linkage analysis confirms an earlier suggestion of one major gene around D6S281. Interestingly, the linkage-disequilibrium analysis suggests several additional signals around D6S250, GATA30, D6S311, D6S441, D6S442, D6S415, D6S411, D6S305, and a290xh9. The linkage analysis, on the other hand, suggests a signal around D6S281, while providing supporting evidence for several other marker loci. However, the combined analysis did not provide strong support for any of the findings, implying that linkage and linkage-disequilibrium findings are not consistent.

An efficient, robust, and unified method for mapping complex traits (II): multipoint linkage analysis

Extending the method for two-point linkage analysis [Zhao et al., 1998: Am J Med Genet 77:366-383], this paper introduces a semiparametric method for multipoint linkage analysis, expected to gain efficiency by using multiple markers simultaneously. Overcoming the longstanding statistical and computational challenge to the parametric approaches (or lod score methods) for multipoint linkage analysis, this semiparametric approach, based on the estimating equation technique, yields statistically efficient and yet robust estimates and enjoys the computational efficiency in processing multiple markers from large pedigrees. Its computational burden increases linearly with the sizes of pedigrees and with the number of marker loci. To illustrate this semiparametric method, we apply it to marker data gathered for the Breast Cancer Consortium. The result supports the earlier finding of the positive linkage with BRCA1 and has also shown that the multipoint linkage analysis has an improved power. In addition, we have applied this method to analyze genome scanning data that have been used to localize genes responsible for type 1 diabetes. In support of the earlier findings, the genome scanning detects the linkage signals on chromosome 6 but does not support the earlier suggestions of two major genes in that genome segment. Through sensitivity analysis, it appears that the results are robust to misspecification of penetrance and allele frequency.

Mapping of complex traits by single-nucleotide polymorphisms

Molecular geneticists are developing the third-generation human genome map with single-nucleotide polymorphisms (SNPs), which can be assayed via chip-based microarrays. One use of these SNP markers is the ability to locate loci that may be responsible for complex traits, via linkage/linkage-disequilibrium analysis. In this communication, we describe a semiparametric method for combined linkage/linkage-disequilibrium analysis using SNP markers. Asymptotic results are obtained for the estimated parameters, and the finite-sample properties are evaluated via a simulation study. We also applied this technique to a simulated genome-scan experiment for mapping a complex trait with two major genes. This experiment shows that separate linkage and linkage-disequilibrium analyses correctly detected the signals of both major genes; but the rates of false-positive signals seem high. When linkage and linkage-disequilibrium signals were combined, the analysis yielded much stronger and clearer signals for the presence of two major genes than did two separate analyses.

Efficient, robust, and unified method for mapping complex traits (I): two-point linkage analysis

The completion of a preliminary human genome map and development of molecular methods have enabled researchers to assay a large number of polymorphic markers that are evenly spaced along the entire human genome. Among many applications, marker data are valuable for mapping complex traits through linkage or linkage-disequilibrium analysis, the former of which is the focus of this paper, the first in a series on this subject. Formalizing the concept and computation for linkage analysis, Elston and Stewart [1971; Human Heredity 21:523-542] introduced a likelihood function to capture relevant genetic information and a recursive algorithm for computing the likelihood function. However, the computing burden is prohibitive in processing complex pedigrees. Since that fundamental development, improving the computational algorithm and extending the method has been a dynamic area of research. The primary objective of this communication is to introduce a semiparametric method for linkage analysis. It is a particularly suitable approach with desirable properties for mapping complex traits that may be binary, continuous, and partially observed (i.e., censored). It incorporates candidate genes, environmental factors, and their interactions with the putative gene and is expected to be robust and efficient in comparison with likelihood-based methods. The properties of the estimates have been studied in finite samples with a limited simulation study. This method is illustrated with an application to family data contributed to the Breast Cancer Consortium.

A seroprevalence and descriptive epidemiological study of malaria among Indian tribes of the Amazon basin of Brazil

Data on the seroprevalences of Plasmodium falciparum, P. vivax, and P. malariae in four isolated Indian tribes of the Amazon basin in Brazil, as determined by IFAT, were re-analysed. Age-, sex- and tribe-specific geometric mean antibody titres and externally standardized prevalence ratios were calculated for each parasite species. Correlation coefficients and prevalence odds ratios were also calculated for multiple infections with different combinations of the three Plasmodium species. Titres of all but one of the antibodies studied were similar in males and females; titres of antibodies to the blood stages of P. malariae were slightly higher in females than in males. Titres of antibodies to all three Plasmodium species increased with subject age, and this age effect was not confounded by sex or tribal differences. There were striking differences between tribes, with the Parakana tribe having relatively low titres of antibodies against P. falciparum and P. malariae; these tribal effects were not confounded by sex or age differences between tribes. The results indicate that conditions conductive to the transmission of P. malariae exist in this region of the Amazon. The potential for zoonotic transmission of P. brasilianum, a parasite of monkeys which is morphologically similar to P. malarie, and the generally high rates of seropositivity to all three species of Plasmodium indicate that control measures which are adequate and applicable to the region studied need to be developed.