Comparison of statistics in association tests of genetic markers for survival outcomes

On Path to Informing Hierarchy of Eplet Mismatches as Determinants of Kidney Transplant Loss

Introduction

To mitigate risks related to human leukocyte antigen (HLA) incompatibility, we assessed whether certain structurally defined HLA targets present in donors but absent from recipients, known as eplet mismatches (EMM), are associated with death-censored graft failure (DCGF).

Methods

We studied a cohort of 118,313 American 0% panel reactive antibodies (PRA) first kidney transplant recipients (2000 to 2015) from the Scientific Registry of Transplant Recipients. Imputed allele-level donor and recipient HLA-A, -B, -C, -DRB1, and -DQB1 genotypes were converted to the repertoire of EMM. We fit survival models for each EMM with significance thresholds corrected for false discovery rate and validated those in an independent PRA > 0% cohort. We conducted network-based analyses to model relationships among EMM and developed models to select the subset of EMM most predictive of DCGF.

Results

Of 412 EMM observed, 119 class I and 118 class II EMM were associated with DCGF. Network analysis showed that although 210 eplets formed profiles of 2 to 12 simultaneously occurring EMMs, 202 were singleton EMMs that were not involved in any profile. A variable selection procedure identified 55 single HLA class I and II EMMs in 70% of the dataset; of those, 15 EMMs (9 singleton and 6 involved in profiles) were predictive of DCGF in the remaining dataset.

Conclusion

Our analysis distinguished increasingly smaller subsets of EMMs associated with increased risk of DCGF. Validation of these EMMs as important predictors of transplant outcomes (in contrast to acceptable EMMs) in datasets with measured allele-level genotypes will support their role as immunodominant EMMs worthy of consideration in organ allocation schemes.

Accurate computation of survival statistics in genome-wide studies

A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations.