Replication of genome-wide association studies on asthma and allergic diseases in Korean adult population

Allergic diseases such as asthma, allergic rhinitis, and atopic dermatitis are heterogeneous diseases characterized by multiple symptoms and phenotypes. Recent advancements in genetic study enabled us to identify disease associated genetic factors. Numerous genome-wide association studies (GWAS) have revealed multiple associated loci for allergic diseases. However, the majority of previous studies have been conducted in populations of European ancestry. Moreover, the associations of single nucleotide polymorphisms (SNPs) with allergic diseases have not been studied amongst the large-scale general Korean population. Herein, we performed the replication study to validate the previous variants, known to be associated with allergic diseases, in the Korean population. In this study, we categorized three allergic related phenotypes, one allergy and two asthma related phenotypes, based on self-reports of physician diagnosis and their symptoms from 8,842 samples. As a result, we found nominally significant associations of 6 SNPs with at least one allergic related phenotype in the Korean population.     

Whole genome associated studies for age at menarche

Puberty, the transition from childhood to adult body size and sexual maturity, is a complex multi-staged process involving growth acceleration, weight gain and the appearance of secondary sexual physical features over a 2- to 3-year period. Recent genome-wide association (GWA) studies for age at menarche, the onset of the first menstrual period in girls, have identified the first common genetic variants to be robustly associated with pubertal timing. The findings indicate a novel link between microRNA pre-processing and the timing of whole organism growth and development. Our studies also demonstrate that the use of easily measured phenotypic markers, such as age at menarche in girls, in GWA studies can uncover genetic variants with wider relevance to more complex phenotypes, such as pubertal onset and the tempo of growth in both sexes.     

Coverage and power in genomewide association studies

The ability of genomewide association studies to decipher genetic traits is driven in part by how well the measured single-nucleotide polymorphisms "cover" the unmeasured causal variants. Estimates of coverage based on standard linkage-disequilibrium measures, such as the average maximum squared correlation coefficient (r2), can lead to inaccurate and inflated estimates of the power of genomewide association studies. In contrast, use of the "cumulative r2 adjusted power" measure presented here gives more-accurate estimates of power for genomewide association studies.     

A large-scale candidate gene association study of age at menarche and age at natural menopause

Recent genome-wide association (GWA) studies have identified several novel genetic loci associated with age at menarche and age at natural menopause. However, the stringent significance threshold used in GWA studies potentially led to false negatives and true associations may have been overlooked. Incorporating biologically relevant information, we examined whether common genetic polymorphisms in candidate genes of nine groups of biologically plausible pathways and related phenotypes are associated with age at menarche and age at natural menopause. A total of 18,862 genotyped and imputed single nucleotide polymorphisms (SNPs) in 278 genes were assessed for their associations with these two traits among a total of 24,341 women from the Nurses’ Health Study (NHS, N = 2,287) and the Women’s Genome Health Study (WGHS, N = 22,054). Linear regression was used to assess the marginal association of each SNP with each phenotype. We adjusted for multiple testing within each gene to identify statistically significant SNP associations at the gene level. To evaluate the overall evidence for an excess of statistically significant gene associations over the proportion expected by chance, we applied a one-sample test of proportion to each group of candidate genes. The steroid-hormone metabolism and biosynthesis pathway was found significantly associated with both age at menarche and age at natural menopause (P = 0.040 and 0.011, respectively). In addition, the group of genes associated with precocious or delayed puberty was found significantly associated with age at menarche (P = 0.013), and the group of genes involved in premature ovarian failure with age at menopause (P = 0.025).     

Nonlinear tests for genomewide association studies

As millions of single-nucleotide polymorphisms (SNPs) have been identified and high-throughput genotyping technologies have been rapidly developed, large-scale genomewide association studies are soon within reach. However, since a genomewide association study involves a large number of SNPs it is therefore nearly impossible to ensure a genomewide significance level of 0.05 using the available statistics, although the multiple-test problems can be alleviated, but not sufficiently, by the use of tagging SNPs. One strategy to circumvent the multiple-test problem associated with genome-wide association tests is to develop novel test statistics with high power. In this report, we introduce several nonlinear tests, which are based on nonlinear transformation of allele or haplotype frequencies. We investigate the power of the nonlinear test statistics and demonstrate that under certain conditions, some nonlinear test statistics have much higher power than the standard chi2-test statistic. Type I error rates of the nonlinear tests are validated using simulation studies. We also show that a class of similarity measure-based test statistics is based on the quadratic function of allele or haplotype frequencies, and thus they belong to nonlinear tests. To evaluate their performance, the nonlinear test statistics are also applied to three real data sets. Our study shows that nonlinear test statistics have great potential in association studies of complex diseases.     

Breast cancer susceptibility loci in association with age at menarche,age at natural menopause and the reproductive lifespan

Background: Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with breast cancer risk. Some of these loci have unknown functional significance and may mediate the effects of hormonal exposures on breast cancer risk. We examined relationships between breast cancer susceptibility variants and menstrual/reproductive factors using data from two population-based studies. Methods: The first analysis was based on a sample of 1328 women age 20–74 who participated as controls in a case–control study of breast cancer conducted in three U.S. states. We evaluated the associations between age at menarche, age at natural menopause and the reproductive lifespan with 13 previously identified breast cancer variants. Associations were also examined with a genetic score created as the sum of at-risk alleles across the 13 variants. For validation, significant results were evaluated in a second dataset comprised 1353 women age 43–86 recruited as part of a cohort study in Wisconsin. Results: Neither the genetic score nor any of the 13 variants considered individually were associated with age at menarche or reproductive lifespan. Two SNPs were associated with age at natural menopause; every increase in the minor allele (A) of rs17468277 (CASP8) was associated with a 1.12 year decrease in menopause age (p = 0.02). The minor allele (G) of rs10941679 (5p12) was associated with a 1.01 year increase in age at natural menopause (p = 0.01). The results were not replicated in the validation cohort (B = −0.61, p = 0.14 and B = −0.01, p = .0.98, respectively). Conclusions: The evaluated variants and reproductive experiences may work through separate pathways to influence breast cancer risk.     

Evaluating statistical significance in two-stage genomewide association studies

Genomewide association studies are being conducted to unravel the genetic etiology of complex human diseases. Because of cost constraints, these studies typically employ a two-stage design, under which a large panel of markers is examined in a subsample of subjects, and the most-promising markers are then examined in all subjects. This report describes a simple and efficient method to evaluate statistical significance for such genome studies. The proposed method, which properly accounts for the correlated nature of polymorphism data, provides accurate control of the overall false-positive rate and is substantially more powerful than the standard Bonferroni correction, especially when the markers are in strong linkage disequilibrium.     

An entropy-based statistic for genomewide association studies

Efficient genotyping methods and the availability of a large collection of single-nucleotide polymorphisms provide valuable tools for genetic studies of human disease. The standard chi2 statistic for case-control studies, which uses a linear function of allele frequencies, has limited power when the number of marker loci is large. We introduce a novel test statistic for genetic association studies that uses Shannon entropy and a nonlinear function of allele frequencies to amplify the differences in allele and haplotype frequencies to maintain statistical power with large numbers of marker loci. We investigate the relationship between the entropy-based test statistic and the standard chi2 statistic and show that, in most cases, the power of the entropy-based statistic is greater than that of the standard chi2 statistic. The distribution of the entropy-based statistic and the type I error rates are validated using simulation studies. Finally, we apply the new entropy-based test statistic to two real data sets, one for the COMT gene and schizophrenia and one for the MMP-2 gene and esophageal carcinoma, to evaluate the performance of the new method for genetic association studies. The results show that the entropy-based statistic obtained smaller P values than did the standard chi2 statistic.     

Bayesian graphical models for genomewide association studies

As the extent of human genetic variation becomes more fully characterized, the research community is faced with the challenging task of using this information to dissect the heritable components of complex traits. Genomewide association studies offer great promise in this respect, but their analysis poses formidable difficulties. In this article, we describe a computationally efficient approach to mining genotype-phenotype associations that scales to the size of the data sets currently being collected in such studies. We use discrete graphical models as a data-mining tool, searching for single- or multilocus patterns of association around a causative site. The approach is fully Bayesian, allowing us to incorporate prior knowledge on the spatial dependencies around each marker due to linkage disequilibrium, which reduces considerably the number of possible graphical structures. A Markov chain-Monte Carlo scheme is developed that yields samples from the posterior distribution of graphs conditional on the data from which probabilistic statements about the strength of any genotype-phenotype association can be made. Using data simulated under scenarios that vary in marker density, genotype relative risk of a causative allele, and mode of inheritance, we show that the proposed approach has better localization properties and leads to lower false-positive rates than do single-locus analyses. Finally, we present an application of our method to a quasi-synthetic data set in which data from the CYP2D6 region are embedded within simulated data on 100K single-nucleotide polymorphisms. Analysis is quick (<5 min), and we are able to localize the causative site to a very short interval.     

Pathway-based approaches for analysis of genomewide association studies

Published genomewide association (GWA) studies typically analyze and report single-nucleotide polymorphisms (SNPs) and their neighboring genes with the strongest evidence of association (the “most-significant SNPs/genes” approach), while paying little attention to the rest. Borrowing ideas from microarray data analysis, we demonstrate that pathway-based approaches, which jointly consider multiple contributing factors in the same pathway, might complement the most-significant SNPs/genes approach and provide additional insights into interpretation of GWA data on complex diseases.     

Enriching the analysis of genomewide association studies with hierarchical modeling

Genomewide association studies (GWAs) initially investigate hundreds of thousands of single-nucleotide polymorphisms (SNPs), and the most promising SNPs are further evaluated with additional subjects, for replication or a joint analysis. Deciding which SNPs merit follow-up is one of the most crucial aspects of these studies. We present here an approach for selecting the most-promising SNPs that incorporates into a hierarchical model both conventional results and other existing information about the SNPs. The model is developed for general use, its potential value is shown by application, and tools are provided for undertaking hierarchical modeling. By quantitatively harnessing all available information in GWAs, hierarchical modeling may more clearly distinguish true causal variants from noise.     

Father absence and age at menarche

Life history data, attractiveness ratings of male photographs, and attitudes towards partnership and child-rearing of 321 women were used to test four evolutionary models (quantitative reproductive strategy, male short-age, polygyny indication, and maternal reproductive interests) which attempt to explain the influence of family composition on reproductive strategies. Links between early menarche and other markers of reproductive strategy were investigated. Childhood stress and absence of a father figure, whether genetically related or not, were found to have accelerated menarche whereas having younger siblings decelerated it. Early menarche was associated with attractiveness ratings, the number of partners desired for the immediate future, and the early onset of intimate relationships. It was not linked with sociosexual orientation, mate choice criteria, and investment in the subjects’ own children, but these three markers were interrelated. The implications of the findings for the four evolutionary models are discussed.     

Seasonal characteristics of and age at menarche

Data from approximately 600 U.S. women were analyzed for seasonality in menarche. Data were converted to the ratio of observed (O) to expected (E) cases, based on an equal distribution of events over the year, per considered time span of the year. The greatest O/E ratio in menarche (1.55) occurred during January, followed by August (1.43) and July (1.24). The lowest O/E ratio in menarche occurred in February and May (0.68). Seasonal patterns in menarche were detected for the separate cohorts of women born during the 1940s, 1950s, and 1960s, with a shift from a December-January peak in menarche for those born in the 1940s and 1950s to an August-September peak for those born in the 1960s. Girls who were younger (8-14 yrs) at menarche exhibited a seasonal difference in the peak number of menarches by about 6 months in comparison to the pattern for girls who were older (15-17 yrs). Girls who experienced menarche during August-October were statistically significantly younger (p less than 0.05) than those born during the other three seasons. Season of birth was not statistically significantly associated with season of menarche. Overall, no 12-month pattern was substantiated by cosinor analysis in the month of menarche. However, a 6-month rhythm was detected in menarche for women born between 1940 and 1960 (p = 0.004, A = 29% M, phi = January and July).     

Geographical variation in age at menarche in Britain

The geographical distribution of age at menarche in great Britain is analysed in two successive generations. It appears that the variances for age at menarche are equal throughout the country. The means, however, show significant and complicated patterns of regional variation. In the southern and eastern parts of the country, menarche was earlier than in the northern parts in both generations. This patterning is not due to social class differentiation. Except for the sparsely populated areas in northern and western Scotland and for the extreme south-western parts of England and Wales all areas show evidence of a decline in age at menarche from the mothers' to the daughters' generation.     

A genomewide association study of skin pigmentation in a South Asian population

We have conducted a multistage genomewide association study, using 1,620,742 single-nucleotide polymorphisms to systematically investigate the genetic factors influencing intrinsic skin pigmentation in a population of South Asian descent. Polymorphisms in three genes—SLC24A5, TYR, and SLC45A2—yielded highly significant replicated associations with skin-reflectance measurements, an indirect measure of melanin content in the skin. The associations detected in these three genes, in an additive manner, collectively account for a large fraction of the natural variation of skin pigmentation in a South Asian population. Our study is the first to interrogate polymorphisms across the genome, to find genetic determinants of the natural variation of skin pigmentation within a human population.     

A mixed two-stage method for detecting interactions in genomewide association studies

Genomewide association studies (GWAS) are being conducted to unravel the genetic etiology of complex diseases, in which complex epistasis may play an important role. One-stage method in which interactions are tested using all samples at one time may be computationally problematic, may have low power as the number of markers tested increases and may not be cost-efficient. A common two-stage method may be a reasonable and powerful approach for detecting interacting genes using all samples in both two stages. In this study, we introduce an alternative two-stage method, in which some promising markers are selected using a proportion of samples in the first stage and interactions are then tested using the remaining samples in the second stage. This two-stage method is called mixed two-stage method. We then investigate the power of both one-stage method and mixed two-stage method to detect interacting disease loci for a range of two-locus epistatic models in a case-control study design. Our results suggest that mixed two-stage method may be more powerful than one-stage method if we choose about 30% of samples for single-locus tests in the first stage, and identify less than and equal to 1% of markers for follow-up interaction tests. In addition, we compare both two-stage methods and find that our two-stage method will lose power because we only use part of samples in both two stages.     

A weighted-Holm procedure accounting for allele frequencies in genomewide association studies

In the context of genomewide association studies where hundreds of thousand of polymorphisms are tested, stringent thresholds on the raw association test P-values are generally used to limit false-positive results. Instead of using thresholds based on raw P-values as in Bonferroni and sequential Sidak (SidakSD) corrections, we propose here to use a weighted-Holm procedure with weights depending on allele frequency of the polymorphisms. This method is shown to substantially improve the power to detect associations, in particular by favoring the detection of rare variants with high genetic effects over more frequent ones with lower effects.     

Predictors of age at menarche in the newcastle thousand families study

Several studies have found relationships between early life factors (birth weight, length of gestation, height, weight, duration of breast-feeding, maternal age, social class, periods of infection, presence of adverse life events, and quality of housing conditions in childhood) and age at menarche but none has considered all of these factors in the same study. The follow-up study of the Newcastle Thousand Families birth cohort, established in 1947, provided age at menarche data collected retrospectively at age 50 from 276 women who returned self-completion questionnaires in 1997. Three main independent associations were observed: girls who experienced a shorter gestation, girls whose mothers were younger when they were born, and girls who were heavier at age 9 had earlier menarche. Birth weight, standardized for gestational age, was found to have different relationships with age at menarche depending upon how heavy or light a girl was at age 9. The results of this study support the hypotheses that conditions in fetal and early life are associated with the timing of menarche and that greater childhood growth is associated with earlier menarche. It is suggested that future work should focus on illuminating the mechanisms underlying these statistical relationships.