Fast pairwise IBD association testing in genome-wide association studies

The relationship between donor-recipient genetic distance and long-term kidney transplant outcome

Background: We set out to quantify shared genetic ancestry between unrelated kidney donor-recipient pairs and test it as a predictor of time to graft failure.   Methods: In a homogenous, unrelated, European cohort of deceased-donor kidney transplant pairs (n pairs = 1,808), we calculated, using common genetic variation, shared ancestry at the genic (n loci=40,053) and genomic level. We conducted a sub-analysis focused on transmembrane protein coding genes (n transcripts=8,637) and attempted replication of a previously published nonsynonymous transmembrane mismatch score. Measures of shared genetic ancestry were tested in a survival model against time to death-censored graft failure. Results: Shared ancestry calculated across the human leukocyte antigen (HLA) significantly associated with graft survival in individuals who had a high serological mismatch (n pairs = 186) with those who did not have any HLA mismatches indicating that shared ancestry calculated specific loci can capture known associations with genes impacting graft outcome. None of the other measures of shared ancestry at a genic level, genome-wide scale, transmembrane subset or nonsynonymous transmembrane mismatch score analysis were significant predictors of time to graft failure. Conclusions: In a large unrelated, deceased-donor European ancestry renal transplant cohort, shared donor-recipient genetic ancestry, calculated using common genetic variation, has limited value in predicting transplant outcome both on a genomic scale and at a genic level (other than at the HLA loci).

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features

Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. "Full feature spectrum" knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bootstrapping for unified feature association measurement (BUFAM) for pairwise association analysis, and relational dependency network (RDN) modeling for global module detection on features across breast cancer cohorts. Discovered knowledge was cross-validated using data from Wake Forest Baptist Medical Center's electronic medical records and annotated with BioCarta signaling signatures. The clinical potential of the discovered modules was exhibited by stratifying patients for drug responses. A series of discovered associations provided new insights into breast cancer, such as the effects of patient's cultural background on preferences for surgical procedure. We also discovered two groups of highly associated features, the HER2 and the ER modules, each of which described how phenotypes were associated with molecular signatures, diagnostic features, and clinical decisions. The discovered "ER module", which was dominated by cancer immunity, was used as an example for patient stratification and prediction of drug responses to tamoxifen and chemotherapy. BUFAM-derived RDN modeling demonstrated unique ability to discover clinically meaningful and actionable knowledge across highly heterogeneous biomedical big data sets.

PIGS: improved estimates of identity-by-descent probabilities by probabilistic IBD graph sampling

Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics. IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci. Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult. To overcome this, many state of the art methods estimate the probability of IBD between each pair of haplotypes separately. While computationally efficient, these methods fail to leverage the clique structure of IBD resulting in less powerful IBD identification, especially for small IBD segments.

We develop a hybrid approach (PIGS), which combines the computational efficiency of pairwise methods with the power of multiway methods. It leverages the IBD graph structure to compute the probability of IBD conditional on all pairwise estimates simultaneously. We show via extensive simulations and analysis of real data that our method produces a substantial increase in the number of identified small IBD segments.