Microsatellites versus single-nucleotide polymorphisms in confidence interval estimation of disease loci

Detection and mapping of mtDNA SNPs in Atlantic salmon using high throughput DNA sequencing

BACKGROUND

Approximately half of the mitochondrial genome inherent within 546 individual Atlantic salmon (Salmo salar) derived from across the species' North Atlantic range, was selectively amplified with a novel combination of standard PCR and pyro-sequencing in a single run using 454 Titanium FLX technology (Roche, 454 Life Sciences). A unique combination of barcoded primers and a partitioned sequencing plate was employed to designate each sequence read to its original sample. The sequence reads were aligned according to the S. salar mitochondrial reference sequence (NC_001960.1), with the objective of identifying single nucleotide polymorphisms (SNPs). They were validated if they met with the following three stringent criteria: (i) sequence reads were produced from both DNA strands; (ii) SNPs were confirmed in a minimum of 90% of replicate sequence reads; and (iii) SNPs occurred in more than one individual.

RESULTS

Pyrosequencing generated a total of 179,826,884 bp of data, and 10,765 of the total 10,920 S. salar sequences (98.6%) were assigned back to their original samples. The approach taken resulted in a total of 216 SNPs and 2 indels, which were validated and mapped onto the S. salar mitochondrial genome, including 107 SNPs and one indel not previously reported. An average of 27.3 sequence reads with a standard deviation of 11.7 supported each SNP per individual.

CONCLUSION

The study generated a mitochondrial SNP panel from a large sample group across a broad geographical area, reducing the potential for ascertainment bias, which has hampered previous studies. The SNPs identified here validate those identified in previous studies, and also contribute additional potentially informative loci for the future study of phylogeography and evolution in the Atlantic salmon. The overall success experienced with this novel application of HT sequencing of targeted regions suggests that the same approach could be successfully applied for SNP mining in other species.

Conditional tests for localizing trait genes

BACKGROUND/AIMS

With pedigree data, genetic linkage can be detected using inheritance vector tests, which explore the discrepancy between the posterior distribution of the inheritance vectors given observed trait values and the prior distribution of the inheritance vectors. In this paper, we propose conditional inheritance vector tests for linkage localization. These conditional tests can also be used to detect additional linkage signals in the presence of previously detected causal genes.

METHODS

For linkage localization, we propose to perform inheritance vector tests conditioning on the inheritance vectors at two positions bounding a test region. We can detect additional linkage signals by conducting a further conditional test in a region with no previously detected genes. We use randomized p values to extend the marginal and conditional tests when the inheritance vectors cannot be completely determined from genetic marker data.

RESULTS

We conduct simulation studies to compare and contrast the marginal and the conditional tests and to demonstrate that randomized p values can capture both the significance and the uncertainty in the test results.

CONCLUSIONS

The simulation results demonstrate that the proposed conditional tests provide useful localization information, and with informative marker data, the uncertainty in randomized marginal and conditional test results is small.

Determining the effectiveness of High Resolution Melting analysis for SNP genotyping and mutation scanning at the TP53 locus

Background

Together single nucleotide substitutions and small insertion/deletion variants are the most common form of sequence variation in the human gene pool.

High-resolution SNP profile and/or haplotype analyses enable the identification of modest-risk susceptibility genes to common diseases, genes that may modulate responses to pharmaceutical agents, and SNPs that can affect either their expression or function. In addition, sensitive techniques for germline or somatic mutation detection are important tools for characterizing sequence variations in genes responsible for tumor predisposition. Cost-effective methods are highly desirable. Many of the recently developed high-throughput technologies are geared toward industrial scale genetic studies and arguably do not provide useful solutions for small laboratory investigator-initiated projects. Recently, the use of new fluorescent dyes allowed the high-resolution analysis of DNA melting curves (HRM).

Results

Here, we compared the capacity of HRM, applicable to both genotyping and mutation scanning, to detect genetic variations in the tumor suppressor gene TP53 with that of mutation screening by full resequencing. We also assessed the performance of a variety of available HRM-based genotyping assays by genotyping 30 TP53 SNPs. We describe a series of solutions to handle the difficulties that may arise in large-scale application of HRM to mutation screening and genotyping at the TP53 locus. In particular, we developed specific HRM assays that render possible genotyping of 2 or more, sometimes closely spaced, polymorphisms within the same amplicon. We also show that simultaneous genotyping of 2 SNPs from 2 different amplicons using a multiplex PCR reaction is feasible; the data can be analyzed in a single HRM run, potentially improving the efficiency of HRM genotyping workflows.

Conclusion

The HRM technique showed high sensitivity and specificity (1.0, and 0.8, respectively, for amplicons of <400 bp) for mutation screening and provided useful genotyping assays as assessed by comparing the results with those obtained with Sanger sequencing. Thus, HRM is particularly suitable for either performing mutation scanning of a large number of samples, even in the situation where the amplicon(s) of interest harbor a common variant that may disturb the analysis, or in a context where gathering common SNP genotypes is of interest.

Bayesian intervals for linkage locations

Intermediate fine mapping has received considerable attention recently, with the goal of providing statistically precise and valid chromosomal regions for fine mapping following initial identification of broad regions that are linked to a disease. The following classes of methods have been proposed and compared in the literature: (1) LOD-support intervals, (2) generalized estimating equations, (3) bootstrap, and (4) confidence set inference framework. These methods provide confidence intervals either with coverage levels deviating from the nominal confidence levels or that are not fully efficient. Here, we propose a novel Bayesian method for constructing such intervals using affected sibling pair data. The susceptibility gene location is treated as a parameter in this method, with a uniform prior. A Metropolis-Hastings algorithm is implemented to sample from the posterior distribution and highest posterior density intervals of the disease gene locations are constructed. Correct coverage levels are maintained by our method. Both simulation studies and an application to a rheumatoid arthritis dataset demonstrate the improved efficiency of the Bayesian intervals compared with existing methods.

Multiple testing in the genomics era: findings from Genetic Analysis Workshop 15, Group 15

Recent advances in molecular technologies have resulted in the ability to screen hundreds of thousands of single nucleotide polymorphisms and tens of thousands of gene expression profiles. While these data have the potential to inform investigations into disease etiologies and advance medicine, the question of how to adequately control both type I and type II error rates remains. Genetic Analysis Workshop 15 datasets provided a unique opportunity for participants to evaluate multiple testing strategies applicable to microarray and single nucleotide polymorphism data. The Genetic Analysis Workshop 15 multiple testing and false discovery rate group (Group 15) investigated three general categories for multiple testing corrections, which are summarized in this review: statistical independence, error rate adjustment, and data reduction. We show that while each approach may have certain advantages, adequate error control is largely dependent upon the question under consideration and often requires the use of multiple analytic strategies.

Efficient intermediate fine mapping: confidence set inference with likelihood ratio test statistic

In positional cloning of disease causing genes, identification of a linked chromosomal region via linkage studies is often followed by fine mapping via association studies. Efficiency can be gained with an intermediate step where confidence regions for the locations of disease genes are constructed. The confidence set inference [CSI; Papachristou and Lin, 2006b] achieves this goal by replacing the traditional null hypothesis of no linkage with a new set of null hypotheses where the chromosomal position under consideration is in tight linkage with a trait locus. This approach was shown to perform favorably compared with several competing methods. Using the duality of confidence sets and hypothesis testing, CSI was proposed for the Mean test statistics with affected sibling pair data (CSI-Mean). We postulate that more efficient confidence sets will result if more efficient test statistics are used in the CSI framework. One promising candidate, the maximum LOD score (MLS) statistic, makes maximum use of available identity by descent information, in addition to handling markers with incomplete polymorphism naturally. We propose a procedure that tests the CSI null hypotheses using the MLS statistic (CSI-MLS). Compared with CSI-Mean, CSI-MLS provides tighter confidence regions over a range of single and two-locus disease models. The MLS test is also shown to be more powerful than the Mean test in testing the CSI null over a wide range of disease models, the advantage being most pronounced for recessive models. In addition, CSI-MLS is computationally much more efficient than CSI-Mean.

Incorporating covariates in mapping heterogeneous traits: a hierarchical model using empirical Bayes estimation

Complex genetic traits are inherently heterogeneous, i.e., they may be caused by different genes, or non-genetic factors, in different individuals. So, for mapping genes responsible for these diseases using linkage analysis, heterogeneity must be accounted for in the model. Heterogeneity across different families can be modeled using a mixture distribution by letting each family have its own heterogeneity parameter denoting the probability that its disease-causing gene is linked to the marker map under consideration. A substantial gain in power is expected if covariates that can discriminate between the families of linked and unlinked types are incorporated in this modeling framework. To this end, we propose a hierarchical Bayesian model, in which the families are grouped according to various (categorized) levels of covariate(s). The heterogeneity parameters of families within each group are assigned a common prior, whose parameters are further assigned hyper-priors. The hyper-parameters are obtained by utilizing the empirical Bayes estimates. We also address related issues such as evaluating whether the covariate(s) under consideration are informative and grouping of families. We compare the proposed approach with one that does not utilize covariates and show that our approach leads to considerable gains in power to detect linkage and in precision of interval estimates through various simulation scenarios. An application to the asthma datasets of Genetic Analysis Workshop 12 also illustrates this gain in a real data analysis. Additionally, we compare the performances of microsatellite markers and single nucleotide polymorphisms for our approach and find that the latter clearly outperforms the former.

Two-step intermediate fine mapping with likelihood ratio test statistics: applications to Problems 2 and 3 data of GAW15

Construction of precise confidence sets of disease gene locations after initial identification of linked regions can improve the efficiency of the ensuing fine mapping effort. We took the confidence set inference, a framework proposed and implemented using the Mean test statistic (CSI-Mean) and improved the efficiency substantially by using a likelihood ratio test statistic (CSI-MLS). The CSI framework requires knowledge of some disease-model-related parameters. In the absence of prior knowledge of these parameters, a two-step procedure may be employed: 1) the parameters are estimated using a coarse map of markers; 2) CSI-Mean or CSI-MLS are applied to construct the confidence sets of the disease gene locations using a finer map of markers, assuming the estimates from Step 1 for the required parameters. In this article we show that the advantages of CSI-MLS over CSI-Mean, previously demonstrated when the required parameters are known, are preserved in this two-step procedure, using both the simulated and real data contributed to Problems 2 and 3 of Genetic Analysis Workshop 15. In addition, our result suggests that microsatellite data, when available, should be used in Step 1. Also explored in detail is the effect of the absence of parental genotypes on the performance of CSI-MLS.

Follow-up mapping supports the evidence for linkage in the candidate region at 9q22 in the NIMH Alzheimer's disease Genetics Initiative cohort

Other than the APOE peak at 19q13, the 9q22 region was identified in our original genomic scan as the candidate region with the highest multipoint lod score (MLS) in the subset of late onset Alzheimer's Disease (AD) families (MLS = 2.9 at 101 cM) from the NIMH Genetics Initiative sample. We have now genotyped an additional 12 short tandem repeats (STR) in this region. Multipoint analysis shows the region remains significant with an increase in the peak MLS from 2.9 to 3.8 at 95 cM near marker D9S1815, and the 1 LOD interval narrows from 21.5 to 11 cM. HLOD scores also provide evidence for significant linkage (4.5 with an alpha = 31%) with a further narrowing of the region to 6.6 cM (92.2-98.8 cM). Single nucleotide polymorphisms (SNPs) in the Ubiquilin1 gene (UBQLN1), located at 83.3 cM, have been reported to be significantly associated to AD, accounting for a substantial portion of the original linkage signal [Bertram et al., 2005]. Our analyses of the higher resolution genotype data generated here provide further support for the existence of a least one additional locus on chromosome 9q22. In an effort to pinpoint this putative AD susceptibility gene, we have begun to analyze SNPs in other candidate genes in and around this narrowed region to test for additional associations to AD.

A two-step procedure for constructing confidence intervals of trait loci with application to a rheumatoid arthritis dataset

Preliminary genome screens are usually succeeded by fine mapping analyses focusing on the regions that signal linkage. It is advantageous to reduce the size of the regions where follow-up studies are performed, since this will help better tackle, among other things, the multiplicity adjustment issue associated with them. We describe a two-step approach that uses a confidence set inference procedure as a tool for intermediate mapping (between preliminary genome screening and fine mapping) to further localize disease loci. Apart from the usual Hardy-Weiberg and linkage equilibrium assumptions, the only other assumption of the proposed approach is that each region of interest houses at most one of the disease-contributing loci. Through a simulation study with several two-locus disease models, we demonstrate that our method can isolate the position of trait loci with high accuracy. Application of this two-step procedure to the data from the Arthritis Research Campaign National Repository also led to highly encouraging results. The method not only successfully localized a well-characterized trait contributing locus on chromosome 6, but also placed its position to narrower regions when compared to their LOD support interval counterparts based on the same data.

A comparison of methods for intermediate fine mapping

The arrival of highly dense genetic maps at low cost has geared the focus of linkage analysis studies toward developing methods for placing putative trait loci in narrow regions with high confidence. This shift has led to a new analytic scheme that expands the traditional two-stage protocol of preliminary genome scan followed by fine mapping through inserting a new stage in between the two. The goal of this new "intermediate" fine mapping stage is to isolate disease loci to narrow intervals with high confidence so that association studies can be more focused, efficient, and cost-effective. In this paper, we compared and contrasted five methods that can be used for performing this intermediate step. These methods are: the lod support approach, the generalized estimating equations (GEE) method, the confidence set inference (CSI) procedure, and two bootstrap methods. We compared these methods in terms of the coverage probability and precision of localization of the resulting intervals. Results from a simulation study considering several two-locus models demonstrated that the two bootstrap methods yield intervals with approximately correct coverage. On the other hand, the 1-lod support intervals, and those produced by the GEE method, tend to significantly undercover the trait locus, while the regions obtained by the CSI incline to overcover the gene position. When the observed coverage of the confidence intervals produced by all the methods was held to be the same, those obtained through the CSI procedure displayed a higher ability to localize loci, especially when these loci have a minor contribution to the trait and when the amount of data available for the analysis is relatively small. However, with very large sample sizes, lod support intervals emerged as a winner. Application of the methods to the data from the Arthritis Research Campaign National Repository led to intervals containing the position of a known trait locus for all methods, with the greatest precision achieved by the CSI.