On the complexity measures of genetic sequences

MOTIVATION: It is well known that the regulatory regions of genomes are highly repetitive. They are rich in direct, symmetric and complemented repeats, and there is no doubt about the functional significance of these repeats. Among known measures of complexity, the Ziv-Lempel complexity measure reflects most adequately repeats occurring in the text. But this measure does not take into account isomorphic repeats. By isomorphic repeats we mean fragments that are identical (or symmetric) modulo some permutation of the alphabet letters. RESULTS: In this paper, two complexity measures of symbolic sequences are proposed that generalize the Ziv-Lempel complexity measure by taking into account any isomorphic repeats in the text (rather than just direct repeats as in Ziv-Lempel). The first of them, the complexity vector, is designed for small alphabets such as the alphabet of nucleotides. The second is based on a search for the longest isomorphic fragment in the history of sequence synthesis and can be used for alphabets of arbitrary cardinality. These measures have been used for recognition of structural regularities in DNA sequences. Some interesting structures related to the regulatory region of the human growth hormone are reported.     

Population stratification confounds genetic association studies among Latinos

In the United States, asthma prevalence and mortality are the highest among Puerto Ricans and the lowest among Mexicans. Case-control association studies are a powerful strategy for identifying genes of modest effect in complex diseases. However, studies of complex disorders in admixed populations such as Latinos may be confounded by population stratification. We used ancestry informative markers (AIMs) to identify and correct for population stratification among Mexican and Puerto Rican subjects participating in case-control studies of asthma. Three hundred and sixty-two subjects with asthma (Mexican: 181, Puerto Rican: 181) and 359 ethnically matched controls (Mexican: 181, Puerto Rican: 178) were genotyped for 44 AIMs. We observed a greater than expected degree of association between pairs of AIMs on different chromosomes in Mexicans (P < 0.00001) and Puerto Ricans (P < 0.00002) providing evidence for population substructure and/or recent admixture. To assess the effect of population stratification on association studies of asthma, we measured differences in genetic background of cases and controls by comparing allele frequencies of the 44 AIMs. Among Puerto Ricans but not in Mexicans, we observed a significant overall difference in allele frequencies between cases and controls (P = 0.0002); of 44 AIMs tested, 8 (18%) were significantly associated with asthma. However, after adjustment for individual ancestry, only two of these markers remained significantly associated with the disease. Our findings suggest that empirical assessment of the effects of stratification is critical to appropriately interpret the results of case-control studies in admixed populations.     

Variability measures of positive random variables

During the stationary part of neuronal spiking response, the stimulus can be encoded in the firing rate, but also in the statistical structure of the interspike intervals. We propose and discuss two information-based measures of statistical dispersion of the interspike interval distribution, the entropy-based dispersion and Fisher information-based dispersion. The measures are compared with the frequently used concept of standard deviation. It is shown, that standard deviation is not well suited to quantify some aspects of dispersion that are often expected intuitively, such as the degree of randomness. The proposed dispersion measures are not entirely independent, although each describes the interspike intervals from a different point of view. The new methods are applied to common models of neuronal firing and to both simulated and experimental data.     

Permutation tests for the correlation among genetic distances and measures of heterosis

It is often found that heterosis tends to increase with genetic distance of the parents, though the correlation is not usually very close. It is therefore important to test the null hypothesis that the correlation is zero. The present work shows that standard procedures tend to yield too liberal tests, owing to the lack of independence among genetic distances and among heterosis estimates. A valid alternative is to use a permutation test, which was first suggested by Mantel [(1967) Cancer Res 27: 209–220). This test is well-known among plant breeders and geneticists, who often use it to test the correlation among two distance matrices. Its use is not restricted to the comparison of distance matrices. This is demonstrated in the present work, using two published datasets on marker-based genetic distances of maize inbreds or populations and heterosis of their crosses. It is shown that the test is also applicable in the presence of missing data.     

On averaging power for genetic association and linkage studies

A power calculation is crucial in planning genetic studies. In genetic association studies, the power is often calculated using the expected number of individuals with each genotype calculated from an assumed allele frequency under Hardy-Weinberg equilibrium. Since the allele frequency is often unknown, the number of individuals with each genotype is random and so a power calculation assuming a known allele frequency may be incorrect. Ambrosius et al. recently showed that the power ignoring this randomness may lead to studies with insufficient power and proposed averaging the power due to the randomness. We extend the method of averaging power in two directions. First, for testing association in case-control studies, we use the Cochran-Armitage trend test and find that the time needed for calculating the averaged power is much reduced compared to the chi-square test with two degrees of freedom studied by Ambrosius et al. A real study is used for illustration of the method. Second, we extend the method to linkage analysis, where the number of identical-by-descent alleles shared by siblings is random. The distribution of identical-by-descent numbers depends on the underlying genetic model rather than the allele frequency. The robust test for linkage analysis is also examined using the averaged powers. We also recommend a sensitivity analysis when the true allele frequency or the number of identical-by-descent alleles is unknown.     

A study of genetic association with electrophysiological measures related to alcoholism: GAW14 data

Recently, alcohol-related traits have been shown to have a genetic component. Here, we study the association of specific genetic measures in one of the three sets of electrophysiological measures in families with alcoholism distributed as part of the Genetic Analysis Workshop 14 data, the NTTH (non-target case of Visual Oddball experiment for 4 electrode placements) phenotypes: ntth1, ntth2, ntth3, and ntth4. We focused on the analysis of the 786 Affymetrix markers on chromosome 4. Our desire was to find at least a partial answer to the question of whether ntth1, ntth2, ntth3, and ntth4 are separately or jointly genetically controlled, so we studied the principal components that explain most of the covariation of the four quantitative traits. The first principal component, which explains 70% of the covariation, showed association but not genetic linkage to two markers: tsc0272102 and tsc0560854. On the other hand, ntth1 appeared to be the trait driving the variation in the second principal component, which showed association and genetic linkage at markers in four regions: tsc0045058, tsc1213381, tsc0055068, and tsc0051777 at map distances 53.26, 85.42, 89.31, and 172.86, respectively. These results show that the partial answer to our starting question for this brief analysis is that the NTTH phenotypes are not jointly genetically controlled. The component ntth1 displays marked genetic linkage.     

On coding genotypes for genetic markers with multiple alleles in genetic association study of quantitative traits

Background

In genetic association study of quantitative traits using F_∞ models, how to code the marker genotypes and interpret the model parameters appropriately is important for constructing hypothesis tests and making statistical inferences. Currently, the coding of marker genotypes in building F_∞ models has mainly focused on the biallelic case. A thorough work on the coding of marker genotypes and interpretation of model parameters for F_∞ models is needed especially for genetic markers with multiple alleles.

Results

In this study, we will formulate F_∞ genetic models under various regression model frameworks and introduce three genotype coding schemes for genetic markers with multiple alleles. Starting from an allele-based modeling strategy, we first describe a regression framework to model the expected genotypic values at given markers. Then, as extension from the biallelic case, we introduce three coding schemes for constructing fully parameterized one-locus F_∞ models and discuss the relationships between the model parameters and the expected genotypic values. Next, under a simplified modeling framework for the expected genotypic values, we consider several reduced one-locus F_∞ models from the three coding schemes on the estimability and interpretation of their model parameters. Finally, we explore some extensions of the one-locus F_∞ models to two loci. Several fully parameterized as well as reduced two-locus F_∞ models are addressed.

Conclusions

The genotype coding schemes provide different ways to construct F_∞ models for association testing of multi-allele genetic markers with quantitative traits. Which coding scheme should be applied depends on how convenient it can provide the statistical inferences on the parameters of our research interests. Based on these F_∞ models, the standard regression model fitting tools can be used to estimate and test for various genetic effects through statistical contrasts with the adjustment for environmental factors.     

Strong genetic influences on measures of behavioral‐regulation among inbred rat strains

A fundamental challenge for any complex nervous system is to regulate behavior in response to environmental challenges. Three measures of behavioral‐regulation were tested in a panel of eight inbred rat strains. These measures were: (1) sensation seeking as assessed by locomotor response to novelty and the sensory reinforcing effects of light onset, (2) attention and impulsivity, as measured by a choice reaction time task and (3) impulsivity as measured by a delay discounting task. Deficient behavioral‐regulation has been linked to a number of psychopathologies, including ADHD, Schizophrenia, Autism, drug abuse and eating disorders. Eight inbred rat strains (August Copenhagen Irish, Brown Norway, Buffalo, Fischer 344, Wistar Kyoto, Spontaneous Hypertensive Rat, Lewis, Dahl Salt Sensitive) were tested. With n = 9 for each strain, we observed robust strain differences for all tasks; heritability was estimated between 0.43 and 0.66. Performance of the eight inbred rat strains on the choice reaction time task was compared to the performance of outbred Sprague Dawley (n = 28) and Heterogeneous strain rats (n = 48). The results indicate a strong genetic influence on complex tasks related to behavioral‐regulation and indicate that some of the measures tap common genetically driven processes. Furthermore, our results establish the potential for future studies aimed at identifying specific alleles that influence variability for these traits. Identification of such alleles could contribute to our understanding of the molecular genetic basis of behavioral‐regulation, which is of fundamental importance and likely contributes to multiple psychiatric disorders .     

Comparison of three methods to estimate genetic ancestry and control for stratification in genetic association studies among admixed populations

Population stratification may confound the results of genetic association studies among unrelated individuals from admixed populations. Several methods have been proposed to estimate the ancestral information in admixed populations and used to adjust the population stratification in genetic association tests. We evaluate the performances of three different methods: maximum likelihood estimation, ADMIXMAP and Structure through various simulated data sets and real data from Latino subjects participating in a genetic study of asthma. All three methods provide similar information on the accuracy of ancestral estimates and control type I error rate at an approximately similar rate. The most important factor in determining accuracy of the ancestry estimate and in minimizing type I error rate is the number of markers used to estimate ancestry. We demonstrate that approximately 100 ancestry informative markers (AIMs) are required to obtain estimates of ancestry that correlate with correlation coefficients more than 0.9 with the true individual ancestral proportions. In addition, after accounting for the ancestry information in association tests, the excess of type I error rate is controlled at the 5% level when 100 markers are used to estimate ancestry. However, since the effect of admixture on the type I error rate worsens with sample size, the accuracy of ancestry estimates also needs to increase to make the appropriate correction. Using data from the Latino subjects, we also apply these methods to an association study between body mass index and 44 AIMs. These simulations are meant to provide some practical guidelines for investigators conducting association studies in admixed populations.     

Meta-analysis of genetic association studies

Meta-analysis, a statistical tool for combining results across studies, is becoming popular as a method for resolving discrepancies in genetic association studies. Persistent difficulties in obtaining robust, replicable results in genetic association studies are almost certainly because genetic effects are small, requiring studies with many thousands of subjects to be detected. In this article, we describe how meta-analysis works and consider whether it will solve the problem of underpowered studies or whether it is another affliction visited by statisticians on geneticists. We show that meta-analysis has been successful in revealing unexpected sources of heterogeneity, such as publication bias. If heterogeneity is adequately recognized and taken into account, meta-analysis can confirm the involvement of a genetic variant, but it is not a substitute for an adequately powered primary study.     

Frequency of genetic polymorphisms of ADAM33 and their association with allergic rhinitis among Jordanians

Allergic rhinitis is a chronic inflammatory disease that is assumed to be due to an interaction between different genetic and/or environmental factors. A disintegrin and metalloprotease domain 33 (ADAM33) has been extensively studied as a susceptibility gene in asthma and has been linked to bronchial hyper-responsiveness. In this study, we investigated the association between ADAM33 single nucleotide polymorphisms and the incidence of allergic rhinitis among the Jordanian population. We conducted a case–control association study on 120 adult individuals diagnosed with allergic rhinitis and 128 normal healthy controls. 8 single-nucleotide polymorphisms in ADAM33 were genotyped using PCR-RFLP method. No significant differences in the allelic frequencies of all SNPs tested between AR patients and the control volunteers were found, although S2 C/G SNP showed a tendency toward significance with P = 0.06. On the genotype level significant association were found in the following genotypes: T1 AA, T1 AG, T2 GG, T2 AG, T + 1 GG, T + 1 AG, V4 CG, S2 CC, S2 CG, Q-1AA. Seven haplotypes were present only within AR patients and eight haplotypes were completely absent from the AR patients. Three haplotypes exhibited significant association with AR P ≤ 0.05, two of them were present only in AR patients. In conclusion, the polymorphisms in the ADAM33 gene are associated with susceptibility to AR in the Jordanian population. Furthermore, the haplotype of the tested SNPs were also associated with the risk of AR.     

Demographic versus genetic dispersal measures

Quantifying dispersal, a fundamental biological process, is far from simple. Here, both direct and indirect methods were employed to estimate dispersal in an endangered butterfly species. A high and significant correlation between the dispersal patterns, generated by an inverse power function fitted to capture-mark-recapture (CMR) data on the one hand, and population genetic analyses on the other hand, was observed. Stepping-stone type movements were detected by both methods, evidence for the importance of connectivity in the studied metapopulation. These results are particularly relevant to those population and conservation biology studies where quantifying dispersal is essential for the elaboration of successful management actions.     

Investigation of the genetic association between quantitative measures of psychosis and schizophrenia: a polygenic risk score analysis

The presence of subclinical levels of psychosis in the general population may imply that schizophrenia is the extreme expression of more or less continuously distributed traits in the population. In a previous study, we identified five quantitative measures of schizophrenia (positive, negative, disorganisation, mania, and depression scores). The aim of this study is to examine the association between a direct measure of genetic risk of schizophrenia and the five quantitative measures of psychosis. Estimates of the log of the odds ratios of case/control allelic association tests were obtained from the Psychiatric GWAS Consortium (PGC) (minus our sample) which included genome-wide genotype data of 8,690 schizophrenia cases and 11,831 controls. These data were used to calculate genetic risk scores in 314 schizophrenia cases and 148 controls from the Netherlands for whom genotype data and quantitative symptom scores were available. The genetic risk score of schizophrenia was significantly associated with case-control status (p<0.0001). In the case-control sample, the five psychosis dimensions were found to be significantly associated with genetic risk scores; the correlations ranged between.15 and.27 (all p<.001). However, these correlations were not significant in schizophrenia cases or controls separately. While this study confirms the presence of a genetic risk for schizophrenia as categorical diagnostic trait, we did not find evidence for the genetic risk underlying quantitative schizophrenia symptom dimensions. This does not necessarily imply that a genetic basis is nonexistent, but does suggest that it is distinct from the polygenic risk score for schizophrenia.