Weighted Score Tests Implementing Model-Averaging Schemes in Detection of Rare Variants in Case-Control Studies

[An improved association analysis pipeline for tumor susceptibility variant in haplotype amplification area]

OBJECTIVE

Haplotype amplification on germline variants is suggested to imply potential selective advantages and clonal expansion susceptibility and has become an important signature for seeking cancer susceptibility gene.Here we propose an improved association method that fully considers the haplotype amplification status.

METHODS

The haplotype amplification status was estimated by the variant allelic frequencies.We adopted a permutation test on variant allelic frequencies to divide the candidate variants into multiple groups.A likelihood clustering method was then applied to establish the neighborhood system of the hidden Markov random field framework.A filtering pipeline was introduced into the proposed method to further refine the candidate variants, including a Wilson's interval filter and a false discovery rate controller.The final candidate set along with the haplotype amplification status was collapsed into the weighted virtual sites for association tests.

RESULTS

Through simulated tests on a series of datasets, we compared the type Ⅰ error rates of different minor allele frequencies, which stably fell within 2%, suggesting good robustness of the algorithm.In addition, we compared another 5 published association approaches for Type-Ⅰ and Type-Ⅱ error rates with the proposed method, which resulted in the error rates all within 2%, demonstrating significant advantages and a good statistical ability of the proposed method.

CONCLUSIONS

The proposed method can accurately identify tumor susceptibility variants in haplotype amplification area with good robustness and stability.

An improved burden-test pipeline for identifying associations from rare germline and somatic variants

BACKGROUND

Identifying rare germline and somatic variants associated with cancer progression is an important research topic in cancer genomics. Although many approaches are proposed for rare variant association study, they are not fit for cancer sequencing data due to multiple issues, such as overly relying on pre-selection, losing sight of interacting hotspots, etc.

RESULTS

In this article, we propose an improved pipeline to identify germline variant and somatic mutation interactions influencing cancer susceptibility from pair-wise cancer sequencing data. The proposed pipeline, RareProb-C performs an algorithmic selection on the given variants by incorporating the variant allelic frequencies. The interactions among the variants are considered within the regions which are limited by a four-gamete test. Then it filters singular cases according to the posterior probability at each site. Finally, it outputs the selected candidates that pass a collapse test.

CONCLUSIONS

We apply RareProb-C on a series of carefully constructed simulation cases and it outperforms six existing genetic model-free approaches. We also test RareProb-C on 429 TCGA ovarian cancer cases, and RareProb-C successfully identifies the known highlighted variants which are considered increasing disease susceptibilities.

Estimation of QT interval prolongation through model-averaging

The current method to analyze concentration-QT interval data, which is based on predictions conditional on a best model, fails to take into account the uncertainty of the model. Previous studies have suggested that failure to take into account model uncertainty using a best model approach can result in confidence intervals that are overly optimistic and may be too narrow. Theoretically, more realistic estimates are obtained using model-averaging where the overall point estimate and confidence interval are a weighted-average from a set of candidate models, the weights of which are equal to each model's Akaike weight. Monte Carlo simulation was used to determine the degree of narrowness in the confidence interval for the degree of QT prolongation under a single ascending dose and thorough QT trial design. Results showed that model averaging performed as well as the best model approach under most conditions with no numeric advantage to using a model averaging approach. No difference was observed in the coverage of the confidence intervals when the best model and model averaging was done by AIC, AICc, or BIC, although in certain circumstances the coverage of the confidence interval themselves tended to be too narrow when using BIC. Modelers can continue to use the best model approach for concentration-QT modeling with confidence, although model averaging may offer more face validity, may be of value in cases where there is uncertainty or misspecification in the best model, and be more palatable to a non-technical reviewer than the best model approach.

Whole exome association of rare deletions in multiplex oral cleft families

By sequencing the exomes of distantly related individuals in multiplex families, rare mutational and structural changes to coding DNA can be characterized and their relationship to disease risk can be assessed. Recently, several rare single nucleotide variants (SNVs) were associated with an increased risk of nonsyndromic oral cleft, highlighting the importance of rare sequence variants in oral clefts and illustrating the strength of family-based study designs. However, the extent to which rare deletions in coding regions of the genome occur and contribute to risk of nonsyndromic clefts is not well understood. To identify putative structural variants underlying risk, we developed a pipeline for rare hemizygous deletions in families from whole exome sequencing and statistical inference based on rare variant sharing. Among 56 multiplex families with 115 individuals, we identified 53 regions with one or more rare hemizygous deletions. We found 45 of the 53 regions contained rare deletions occurring in only one family member. Members of the same family shared a rare deletion in only eight regions. We also devised a scalable global test for enrichment of shared rare deletions.