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PubMed2002

Susceptibility to malaria as a complex trait: big pressure from a tiny creature.

Malaria, which is a major infectious disease worldwide, is caused by the Plasmodium parasite, one of the longest-known parasites infecting humans. The malaria situation is complicated by the emergence of drug resistance and the lack of an effective vaccine. Genetic factors play a key role in disease susceptibility, progression and outcome. Interestingly, an increasing large number of polymorphisms associated with resistance and susceptibility in humans have been found in proteins from erythrocytes, the site of Plasmodium replication. Some of these deleterious alleles have been selected by direct genetic pressure from the parasite in endemic areas of malaria. A number of additional gene effects have been mapped both in humans and in mice using population studies and experimental models of malaria, respectively. These recent studies have started to reveal additional aspects of the complex host-parasite interactions.

Animals↗
PubMed2003

Methodologic quality and genotyping reproducibility in studies of tumor necrosis factor -308 G-->A single nucleotide polymorphism and bacterial sepsis: implications for studies of complex traits.

OBJECTIVE: Studies of genetic associations with common diseases, such as between cytokine gene polymorphisms and severe bacterial sepsis, have reached conflicting conclusions. Failure to follow methodologic standards may have contributed to discordant findings. The -308 G-->A transition in the tumor necrosis factor-alpha promoter has been genotyped by a variety of methods. Based on our observation of genotyping inaccuracies, we sought to determine whether published studies followed a series of acceptable methodologic standards and whether failure to follow the standard of genotyping reproducibility could lead to erroneous conclusions about gene-disease associations. DESIGN: Systematic review and reanalysis of banked genetic material. We applied a published series of seven methodologic standards to five reports of the association between this variant and bacterial sepsis. We then studied the accuracy of restriction fragment length polymorphism for the -308 site using DNA from a cohort of injury victims. SETTING: Surgery research laboratory. MEASUREMENTS AND MAIN RESULTS: We observed that methodologic quality was not uniform and that reproducibility of genotyping was infrequently met. In our subjects, we found that 4 of 46 heterozygotes analyzed by restriction fragment length polymorphism were actually GG-homozygotes (9% misclassified) according to alternative genotyping methods. CONCLUSIONS: Failure to confirm genotype may have led to conclusions that this polymorphism is not associated with sepsis or outcome. Our observations have implications for the conduct and evaluation of studies of complex genetic disease.

Genetic Techniques↗
PubMed1998

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.

Automation↗
PubMed1997

Linkage analysis of complex traits using affected sibpairs: effects of single-locus approximations on estimates of the required sample size.

We investigated the power of the affected sibpair method for detecting a disease locus when the disease is inherited through two bi-allelic loci. The power was computed for all possible values of the gene frequencies and penetrances that lead to a given population prevalence and a given sibling relative risk. A method to generate rapidly all possible models that give a specific population prevalence and relative risk is provided. We applied it to the case of a two-locus disease with a prevalence of 10% and a low sibling relative risk of 1.5. For this particular example, regardless of the true underlying model, a sample size (N = 450 for alpha = 0.05, N = 1,500 for alpha = 0.0001) may be determined such that one would expect enough power (0.80) to detect at least one of the two disease genes. In addition to the general case, we examined a special class of models in which the marginal penetrances at each locus are either recessive or dominant. In this instance, the gene frequencies were excellent predictors of the power afforded by a particular sample size. These methods have been implemented in a C program called SIBPOWER which is freely available from the first author. With this program, investigators can perform their own power calculations for any two-locus model of their choice thus avoiding the need to use single-locus approximations that may grossly underestimate the necessary sample size.

Gene Frequency↗
PubMed1997

A comprehensive analysis of complex traits in problem 2A.

We used descriptive analysis to investigate the relationship between affection status and five quantitative traits (Q1-Q5) in Problem 2A and results suggested the five quantitative traits fall into two groups. The first group comprised three strongly correlated traits, Q1-Q3, which underlie affection status, and the second group comprised Q4 and Q5, which are not directly related to affection status. Segregation and linkage analyses of traits Q1-Q3 and affection status from the first replicate detected one of the major loci for Q1 (MG1) linked to marker 14 on chromosome 5 (D5G14). Because our segregation analysis failed to show evidence of a major locus effect on Q2, and we overlooked the interaction between MG3 and sex, we did not detect either MG2 or MG3. Using Haseman-Elston sib-pair analysis [Haseman and Elston, 1972], we also examined the statistical power of Q1 and type I error rate (using the environmental factor as an index), for the remaining 199 replicates in the context of a genome screen.

Chromosome Mapping↗
PubMed2000

Least squares estimation of variance components for linkage.

We develop least squares (LS) procedures for variance components estimation in genetic linkage studies. The LS procedure is expressed by simple expressions, and does not require inversion of large matrices. Simulations comparing LS with maximum likelihood (ML) procedures for normal data show that both yield unbiased estimators, but the efficiency of the LS procedure was less than 50% of the ML procedure. For bivariate normal data, the efficiency of the LS procedure relative to the ML method was better, generally over 60%. For skewed data, the LS method was markedly more efficient than ML for parameter estimation. The LS method was computationally rapid, over 4,000 times faster than ML estimation for bivariate data. Because ML estimation is time consuming, LS methods are suggested for initial interval mapping with multivariate data.

Computer Simulation↗
PubMed1989

HLA and insulin-dependent diabetes: an overview.

The present knowledge of the HLA system and its biological function is summarized as a basis for the subsequent discussion of the associations between this system and insulin-dependent diabetes (IDDM) and some mechanisms that may explain them. Although the serologically detectable DR determinants are still the most handy markers, there is now increasing evidence from studies of restriction enzyme fragment length polymorphism (RFLP) in IDDM that DQ determinants may play a primary role in causing susceptibility and/or resistance to this disease. Thus, it is now evident that about 90% of DR4-positive diabetics carry the DQw8 determinant present in only about 65% of DR4-positive controls. Most recently, it has been claimed that an aspartic acid in position 57 of the DQB1 (DQ-beta-1) chain confers resistance to IDDM. Although this may be true, it does not explain the disproportionate decrease of DR2 or the particularly high risk of DR3/4 heterozygotes, which is still good evidence that several HLA genes are involved. Because Class II antigens show the strongest associations, the most plausible hypothesis about the mechanism(s) involves specific presentation of as yet unknown antigenic peptides to T-helper lymphocytes, which may induced the formation of both anti-islet cell antibodies and T-cytotoxic lymphocytes capable of destroying beta cells. However, T-suppressor lymphocytes also may be involved. If this hypothesis is correct, the most urgent task is to define the antigenic peptides in question, whether they are environmental (e.g., viral) or autologous.

Diabetes Mellitus, Type 1↗
PubMed1995

Segregation and linkage analysis of the complex trait Q1.

Segregation and linkage analysis of GAW9 Problem 2 quantitative trait 1 (Q1) was performed. Eight segregation models comprising all possible combinations of the environmental factor (EF), quantitative trait 2 (Q2), and quantitative trait 3 (Q3) as covariates were considered. Seven of the eight segregation models showed strong evidence for a major gene, the other model was marginal. When all genotypes are known, some evidence for linkage (lod > 2) was found to all three of the markers that affect Q1. Furthermore, four of the eight models each showed some linkage (lod > 2) to two of the three markers that affect Q1 with no false positives. Each of these segregation analysis major genes is a hybrid combination of the true multiple loci that affect Q1.

Alleles↗
PubMed1999

Genetic dissection of a complex trait.

A number of genetic and statistical tools were applied to various partitions of the simulated data to identify susceptibility loci, relevant environmental factors, and their interaction(s). The distribution of genotypes at D1G24 among affected children in the first population was found to differ significantly from Hardy-Weinberg expectation. Two transmission/disequilibrium tests identified the preferential transmission of allele 1 as the source of the disequilibrium. Simple contingency table analysis revealed a positive association between exposure to environmental factor E1 and disease phenotype. Multipoint linkage analyses on various subsets of the data identified three "signal" regions (in addition to the aforementioned D1G24) localized at D1G9-10, D3G45, and D5G38. The even numbered chromosomes appeared to be devoid of susceptibility loci. Further analyses of subsamples of affected sib pairs, selected according to their disease phenotype and their exposure to E1, clarified some linkage relationships, particularly for D3G45, thereby suggesting the presence of a specific gene x environment interaction. Logistic analysis designed to clarify the relationship between disease phenotype and two risk factors (E1 exposure and the presence of allele 1 at D1G24) in the first population, revealed a significantly negative interaction which, upon learning the details of the generating model, we now attribute to the presence of heterogeneity.

Environment↗
PubMed1999

A comparison of some allele-sharing based linkage analysis methods for detecting complex trait loci.

Using randomly selected sib pairs from a subset of the GAW11 simulated data in Problem 2, we compared the results of some linkage analysis methods based on allele sharing. One method was the Haseman-Elston test for a binary disease outcome (unaffected vs. mild or severe). The other methods, which analyzed the trinary ordered outcome unaffected/mild/severe were the Haseman-Elston test, an extended Haseman-Elston incorporating sib-pair sums, variance components analysis, and regression analysis. Our analysis was done without knowledge of the generating model.

Alleles↗
PubMed1999

Analysis of complex traits using neural networks.

A recently developed approach that employs artificial neural networks (ANNs) was applied to the simulated data set to identify sets of marker loci involved in disease etiology. In this implementation, ANNs are trained to predict the disease state (output) from the given genetic marker data (input). A contribution value (CV) for each locus is calculated from the weights that represent the strength of the connections for the trained ANN; a higher CV indicates a higher probability of linkage. The highest CV values were chosen as the most likely candidate regions involved in the disease.

Computer Simulation↗
PubMed2001

Two approaches for consolidating results from genome scans of complex traits: selection methods and scan statistics.

This work has two purposes: (i) empirically selecting levels of significance that maximize the fraction of markers close to a gene (hit rate) when performing linkage analyses of simulated data and (ii) evaluating the utility of a previously reported scan statistic on the same data. Genotype data were simulated from a trait model of seven susceptibility genes. For purpose (i), five statistics were evaluated on all marker loci in fifty replicates; two-point lod and heterogeneity lod scores maximized over dominance (mlod, mhlod), a multi-allelic TDT test, an affected sib-pair test (ASP), and a model-free test on all sib-pairs (ALL_SIBS). Within each replicate the fraction of markers (hit rate) significant at specified levels of significance and also (a) within fifty markers of, or (b) on the same chromosome as a major gene was calculated. For purpose (ii), scan statistics of length 15 were calculated for each chromosome and their empirical significance levels estimated on the basis of 500 replicates generated under no linkage. The scan statistic was applied to the mhlod scores from one replicate (Replicate 5). Empirical p-values for the scan statistic were determined by computing mhlod scores on 500 replicates of simulated null data. For purpose (i), significance levels between 0.001 and 0.01 had the greatest hit rate for all five methods and both criteria. For criterion (a) at the 0.001 level of significance, both mlod and mhlod displayed the highest hit rates, approximately 0.4 for each. For criterion (b), all methods but ALL_SIBS and ASP had hit rates ranging between 0.4 and 0.5. For purpose (ii), the scan statistic proved equally or more powerful than the single-locus statistic for two of the seven susceptibility genes while the remaining five genes were not detected.

Chromosome Mapping↗
PubMed2001

A robust multipoint linkage statistic (tlod) for mapping complex trait loci.

Classical parametric two-point linkage analysis is a powerful analysis tool, however there are clear disadvantages too, including the sensitivity to allele frequency mis-specification. Conversely, multipoint linkage analysis is not sensitive to allele frequency mis-specification, but it is sensitive to genetic model mis-specification. Göring and Terwilliger [Am J Hum Genet 66:1095-106, 2000] proposed a new robust multipoint statistic that increased the robustness of multipoint analyses. In this paper we have referred to this new statistic as the tlod. We applied this new statistic to the Genetic Analysis Workshop (GAW) 12 data using affected status (AFF) as the phenotype of interest. The heterogeneity tlod and two-point hlod scores correlated highly across the genome (p < 0.0001), as expected, but the het-tlod had a lower number false positives. In addition, the tlod analysis handled missing data better, as would be expected for a multipoint method. When one-third of the genotype data was removed (dead people) the tlod analysis was less affected than the two-point analysis. When tlod scores were compared with multipoint lod scores in true gene locations, the robustness of the tlod to model mis-specification was clearly evident. When the "best" replicate from the general population was analyzed, a borderline genome-wide significant two-point hlod result (3.6) was found 4 cM from MG6 and MG7 on chromosome 6. The heterogeneity tlod score was lower than the two-point hlod score (1.8), but greater than the heterogeneity multipoint lod score (0.4). However, when replicate 1 of the isolated population was analyzed none of the true gene locations were identified with either statistic.

Chromosome Mapping↗