On the replication of genetic associations: timing can be everything!

The failure of researchers to replicate genetic-association findings is most commonly attributed to insufficient statistical power, population stratification, or various forms of between-study heterogeneity or environmental influences.(1) Here, we illustrate another potential cause for nonreplications that has so far not received much attention in the literature. We illustrate that the strength of a genetic effect can vary by age, causing "age-varying associations." If not taken into account during the design and the analysis of a study, age-varying genetic associations can cause nonreplication. By using the 100K SNP scan of the Framingham Heart Study, we identified an age-varying association between a SNP in ROBO1 and obesity and hypothesized an age-gene interaction. This finding was followed up in eight independent samples comprising 13,584 individuals. The association was replicated in five of the eight studies, showing an age-dependent relationship (one-sided combined p = 3.92 x 10(-9), combined p value from pediatric cohorts = 2.21 x 10(-8), combined p value from adult cohorts = 0.00422). Furthermore, this study illustrates that it is difficult for cross-sectional study designs to detect age-varying associations. If the specifics of age- or time-varying genetic effects are not considered in the selection of both the follow-up samples and in the statistical analysis, important genetic associations may be missed.     

Discovering disease associations by integrating electronic clinical data and medical literature

Electronic health record (EHR) systems offer an exceptional opportunity for studying many diseases and their associated medical conditions within a population. The increasing number of clinical record entries that have become available electronically provides access to rich, large sets of patients' longitudinal medical information. By integrating and comparing relations found in the EHRs with those already reported in the literature, we are able to verify existing and to identify rare or novel associations. Of particular interest is the identification of rare disease co-morbidities, where the small numbers of diagnosed patients make robust statistical analysis difficult. Here, we introduce ADAMS, an Application for Discovering Disease Associations using Multiple Sources, which contains various statistical and language processing operations. We apply ADAMS to the New York-Presbyterian Hospital's EHR to combine the information from the relational diagnosis tables and textual discharge summaries with those from PubMed and Wikipedia in order to investigate the co-morbidities of the rare diseases Kaposi sarcoma, toxoplasmosis, and Kawasaki disease. In addition to finding well-known characteristics of diseases, ADAMS can identify rare or previously unreported associations. In particular, we report a statistically significant association between Kawasaki disease and diagnosis of autistic disorder.     

Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies

Large exploratory studies, including candidate-gene-association testing, genomewide linkage-disequilibrium scans, and array-expression experiments, are becoming increasingly common. A serious problem for such studies is that statistical power is compromised by the need to control the false-positive rate for a large family of tests. Because multiple true associations are anticipated, methods have been proposed that combine evidence from the most significant tests, as a more powerful alternative to individually adjusted tests. The practical application of these methods is currently limited by a reliance on permutation testing to account for the correlated nature of single-nucleotide polymorphism (SNP)-association data. On a genomewide scale, this is both very time-consuming and impractical for repeated explorations with standard marker panels. Here, we alleviate these problems by fitting analytic distributions to the empirical distribution of combined evidence. We fit extreme-value distributions for fixed lengths of combined evidence and a beta distribution for the most significant length. An initial phase of permutation sampling is required to fit these distributions, but it can be completed more quickly than a simple permutation test and need be done only once for each panel of tests, after which the fitted parameters give a reusable calibration of the panel. Our approach is also a more efficient alternative to a standard permutation test. We demonstrate the accuracy of our approach and compare its efficiency with that of permutation tests on genomewide SNP data released by the International HapMap Consortium. The estimation of analytic distributions for combined evidence will allow these powerful methods to be applied more widely in large exploratory studies.     

Replication of genetic associations with plasma lipoprotein traits in a multiethnic sample

Recent genome-wide association studies (GWAS) have reproducibly identified loci associated with plasma triglycerides (TG), HDL cholesterol, and LDL cholesterol. We sought to replicate these findings in a multiethnic population-based cohort using the curated single nucleotide polymorphism (SNP) set found on the new Illumina cardiovascular disease (CVD) beadchip, which contains approximately 50,000 SNPs densely mapping approximately 2,100 genes, selected based on their potential role in CVD. The sample consisted of individuals with European (n = 272), South Asian (n = 330), and Chinese (n = 304) ancestry. Identity by state clustering successfully classified individuals according to self-reported ethnicities. Associations between TG and APOA5, TG and LPL, HDL and CETP, and LDL and APOE were all identified (P < 2 × 10⁻⁶). In 13 loci, associations with the same SNP or a proxy SNP were identified in the same direction as previously reported (P < 0.05). Assessing the cumulative number of risk-associated alleles at multiple replicated SNPs increased the proportion of explained lipoprotein variance over and above traditional variables such as age, sex, body mass index, and ethnicity. The findings indicate the potential utility of the Illumina CVD beadchip, but they underscore the need to consider meta-analysis of results from commonly studied clinical or epidemiological samples.     

Efficient measurement error correction with spatially misaligned data

Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally intensive alternative termed the "parameter bootstrap" that only requires solving one nonlinear optimization problem, and we also compare bootstrap methods to other recently proposed methods. We illustrate our methodology in simulations and with publicly available data from the Environmental Protection Agency.     

Initiation of genetic recombination and recombination-dependent replication

Recombination initiates at double-stranded DNA breaks and at single-stranded DNA gaps. These DNA strand discontinuities can arise from DNA-damaging agents and from normal DNA replication when the DNA polymerase encounters an imperfection in the DNA template or another protein. The machinery of homologous recombination acts at these breaks and gaps to promote the events that result in gene recombination, as well as the reattachment of detached replication arms and the resumption of DNA replication. In Escherichia coli, these events require collaboration (RecA, RecBCD, RecFOR, RecQ, RuvABC and SSB proteins) and DNA replication (PriABC proteins and the DNA polymerases). The initial steps common to these recombination and recombination-dependent replication processes are reviewed.     

Efficient detection of viral transmissions with Next-Generation Sequencing data

Background

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Molecular analysis has been frequently used in the study of HCV outbreaks and transmission chains; helping identify a cluster of sequences as linked by transmission if their genetic distances are below a previously defined threshold. However, HCV exists as a population of numerous variants in each infected individual and it has been observed that minority variants in the source are often the ones responsible for transmission, a situation that precludes the use of a single sequence per individual because many such transmissions would be missed.The use of Next-Generation Sequencing immensely increases the sensitivity of transmission detection but brings a considerable computational challenge because all sequences need to be compared among all pairs of samples.

Methods

We developed a three-step strategy that filters pairs of samples according to different criteria: (i) a k-mer bloom filter, (ii) a Levenhstein filter and (iii) a filter of identical sequences. We applied these three filters on a set of samples that cover the spectrum of genetic relationships among HCV cases, from being part of the same transmission cluster, to belonging to different subtypes.

Results

Our three-step filtering strategy rapidly removes 85.1% of all the pairwise sample comparisons and 91.0% of all pairwise sequence comparisons, accurately establishing which pairs of HCV samples are below the relatedness threshold.

Conclusions

We present a fast and efficient three-step filtering strategy that removes most sequence comparisons and accurately establishes transmission links of any threshold-based method. This highly efficient workflow will allow a faster response and molecular detection capacity, improving the rate of detection of viral transmissions with molecular data.

     

Efficient genetic analysis of fungal samples

We present a method for rapid genetic analysis of small amounts of fungal material. Sterile glass slides, sufficiently small to fit in a standard PCR tube, were placed on agar inside a Petri dish. After a few days, fungal cultures start to overgrow the glass slides. Glass slides with attached mycelium were harvested, analysed microscopically, and placed into a standard PCR tube. Conserved primers flanking the ITS regions of rDNA repeat were used in a direct PCR with the fungal material. Sequence data were generated to be included in phylogenetic analyses to investigate the relationships of the studied mycorrhizal fungi from orchids. The mycelium attached to glass slides was also used for an in situ hybridization experiment using fluorescent labelled oligonucleotides as probes. Fluorescent signal was found throughout the cytoplasm when a probe specific to a site in the nuclear small subunit rRNA is used.     

Multipoint genetic mapping with uniparental disomy data

Uniparental disomy (UPD) refers to the presence of two copies of a chromosome from one parent and none from the other parent. In genetic studies of UPDs, many genetic markers are usually used to identify the stage of nondisjunction that leads to UPD and to uncover the associated unusual patterns of recombinations. However, genetic information in such data has not been fully utilized because of the limitations of the existing statistical methods for UPD data. In the present article, we develop a multilocus statistical approach that has the advantages of being able to simultaneously consider all genetic markers for all individuals in the same analysis and to allow general models for the crossover process to incorporate crossover interference. In particular, for a general crossover-process model that assumes only that there exists in each interval at most one crossover, we describe how to use the expectation-maximization algorithm to examine the probability distribution of the recombination events underlying meioses leading to UPD. We can also use this flexible approach to create genetic maps based on UPD data and to inspect recombination differences between meioses exhibiting UPD and normal meioses. The proposed method has been implemented in a computer program, and we illustrate the proposed approach through its application to a set of UPD15 data.     

Efficient study designs for test of genetic association using sibship data and unrelated cases and controls

Linkage mapping of complex diseases is often followed by association studies between phenotypes and marker genotypes through use of case-control or family-based designs. Given fixed genotyping resources, it is important to know which study designs are the most efficient. To address this problem, we extended the likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP, to accommodate sibships of arbitrary size and disease-phenotype configuration. A key advantage of our method is the ability to combine data from different family structures. We consider scenarios for which genotypes are available for unrelated cases, affected sib pairs (ASPs), or only one sibling per ASP. We construct designs that use cases only and others that use unaffected siblings or unrelated unaffected individuals as controls. Different combinations of cases and controls result in seven study designs. We compare the efficiency of these designs when the number of individuals to be genotyped is fixed. Our results suggest that (1) when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient, followed by the ASP-control design, and familial cases contribute more association information than singleton cases; (2) when the disease is influenced by multiple genes, familial cases provide more association information than singleton cases, unless the effect of the locus being tested is much smaller than at least one other untested disease locus; and (3) the case-control design can be useful for detecting genes with small effect in the presence of genes with much larger effect. Our findings will be helpful for researchers designing and analyzing complex disease-association studies and will facilitate genotyping resource allocation.     

Apolipoprotein A-II: beyond genetic associations with lipid disorders and insulin resistance

PURPOSE OF REVIEW: Apolipoprotein A-II, the second major HDL apolipoprotein, was often considered of minor importance relatively to apolipoprotein A-I and its role was controversial. This picture is now rapidly changing, due to novel polymorphisms and mutations, to the outcome of clinical trials, and to studies with transgenic mice. RECENT FINDINGS: The -265 T/C polymorphism supports a role for apolipoprotein A-II in postprandial very-low-density lipoprotein metabolism. Fibrates, which increase apolipoprotein A-II synthesis, significantly decrease the incidence of major coronary artery disease events, particularly in subjects with low HDL cholesterol, high plasma triglyceride, and high body weight. The comparison of transgenic mice overexpressing human or murine apolipoprotein A-II has highlighted major structural differences between the two proteins; they have opposite effects on HDL size, apolipoprotein A-I content, plasma concentration, and protection from oxidation. Human apolipoprotein A-II is more hydrophobic, displaces apolipoprotein A-I from HDL, accelerates apolipoprotein A-I catabolism, and its plasma concentration is decreased by fasting. Apolipoprotein A-II stimulates ATP binding cassette transporter 1-mediated cholesterol efflux. Human and murine apolipoprotein A-II differently affect glucose metabolism and insulin resistance. A novel beneficial role for apolipoprotein A-II in the pathogenesis of hepatitis C virus has been shown. SUMMARY: The hydrophobicity of human apolipoprotein A-II is a key regulatory factor of HDL metabolism. Due to the lower plasma apolipoprotein A-II concentration during fasting, measurements of apolipoprotein A-II in fed subjects are more relevant. More clinical studies are necessary to clarify the role of apolipoprotein A-II in well-characterized subsets of patients and in the insulin resistance syndrome.     

Control of confounding of genetic associations in stratified populations

To control for hidden population stratification in genetic-association studies, statistical methods that use marker genotype data to infer population structure have been proposed as a possible alternative to family-based designs. In principle, it is possible to infer population structure from associations between marker loci and from associations of markers with the trait, even when no information about the demographic background of the population is available. In a model in which the total population is formed by admixture between two or more subpopulations, confounding can be estimated and controlled. Current implementations of this approach have limitations, the most serious of which is that they do not allow for uncertainty in estimations of individual admixture proportions or for lack of identifiability of subpopulations in the model. We describe methods that overcome these limitations by a combination of Bayesian and classical approaches, and we demonstrate the methods by using data from three admixed populations--African American, African Caribbean, and Hispanic American--in which there is extreme confounding of trait-genotype associations because the trait under study (skin pigmentation) varies with admixture proportions. In these data sets, as many as one-third of marker loci show crude associations with the trait. Control for confounding by population stratification eliminates these associations, except at loci that are linked to candidate genes for the trait. With only 32 markers informative for ancestry, the efficiency of the analysis is 70%. These methods can deal with both confounding and selection bias in genetic-association studies, making family-based designs unnecessary.     

Genetic associations with coronary heart disease: Meta-analyses of 12 candidate genetic variants

Aims

The aim of this study was to evaluate the combined contribution of 12 genetic variants to the risk of coronary heart disease (CHD).

Methods

Through a comprehensive literature search for genetic variants involved in the CHD association study, we harvested a total of 10 genes (12 variants) for the current meta-analyses. These genes consisted of GPX1 (rs1050450), PPARD (rs2016520), ALOX15 (rs34210653), SELPLG (rs2228315), FCGR2A (rs1801274), CCL5 (rs2107538), CYP1A1 (rs4646903), TP53 (rs1042522), CX37 (rs1764391), and PECAM1 (rs668, rs12953, and rs1131012).

Results

A total of 45 studies among 23,314 cases and 28,430 controls were retrieved for the meta-analyses of 12 genetic variants. The results showed a significant association between the GPX1 rs1050450 polymorphism and CHD (odd ratio (OR) = 1.61, 95% confidence interval (CI) = 1.25–2.07, P = 0.0002). Other meta-analyses of the rest 11 variants suggested a lack of association with the risk of CHD.

Conclusion

Our results confirmed that GPX1 rs1050450 was associated with susceptibility to CHD in Chinese and Indian populations.     

Heterogeneous genetic associations of nucleotide sequence variants with bone mineral density by gender

We examined genetic associations of previously identified sequence variants with bone mineral density and their heterogeneity by gender. Large-scale cohort data were used including a total of 8,419 subjects (4,034 males and 4,385 females) from the Korean Association REsource (KARE) cohort. Bone speed of sound (SOS) values were measured at distal radius or mid-shaft tibia by quantitative ultrasound. Genotypic associations of SOS were tested with each of nucleotide sequence variants identified by previous studies. The genetic association analysis revealed that 2 out of 11 nucleotide sequence variants were associated with SOS (rs1721400, rs7776725, P < 7.58 × 10⁻⁴). Further analysis with partitioning data by gender showed that the mid-shaft tibia phenotypes were associated with the rs1721400 and rs7776725 in females (P < 3.79 × 10⁻⁴), but not in males (P > 3.79 × 10⁻⁴). The current study suggested female-specific associations of rs1721400 and rs7776725 with bone mineral density and heterogeneity of genetic association by skeletal site measured for bone mineral density.