Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

Four SNP-based HLA imputation methods (e-HLA, HIBAG, HLA*IMP:02 and MAGPrediction) were trained using 1000 Genomes SNP and HLA genotypes and assessed for their ability to accurately impute molecular HLA-A, -B, -C, and –DRB1 genotypes in the Human Genome Diversity Project cell panel. Imputation concordance was high (> 89%) across all methods for both HLA-A and HLA-C, but HLA-B and HLA-DRB1 proved generally difficult to impute. Overall, less than 27.8% of subjects were correctly imputed for all HLA loci by any method. Concordance across all loci was not enhanced via the application of confidence thresholds; reliance on confidence scores across methods only led to noticeable improvement (+3.2%) for HLA-DRB1. As the HLA complex is highly relevant to the study of human health and disease, a standardized assessment of SNP-based HLA imputation methods is crucial for advancing genomic research. Considerable room remains for the improvement of HLA-B and especially HLA–DRB1 imputation methods, and no imputation method is as accurate as molecular genotyping. The application of large, ancestrally diverse HLA and SNP reference datasets and multiple imputation methods has the potential to make SNP-based HLA imputation methods a tractable option for determining HLA genotypes.

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data.Applying BridGE broadly, we discovered significant interactions in Parkinson's disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/182741 doi: bioRxiv preprint first posted online Aug. 30, 2017; 3 Genome-wide association studies (GWAS) have been increasingly successful at identifying single-nucleotide polymorphisms (SNPs) with statistically significant association to a variety of diseases [1][2][3][4][5] and gene sets significantly enriched for SNPs with moderate association [6][7][8][9][10] . However, for most diseases, there remains a substantial disparity between the disease risk explained by the discovered loci and the estimated total heritable disease risk based on familial aggregation [11][12][13][14][15][16] . While there are a number of possible explanations for this "missing heritability", including many loci with small effects or rare variants [11][12][13][14][15]17 , genetic interactions between loci are one potential culprit 13,14,16,18,19 . Genetic interactions generally refer to a combination of two or more genes whose contribution to a phenotype cannot be completely explained by their independent effects 16,20,21 , For example, one example of an extreme genetic interaction is synthetic lethality, which is the case where two mutations, neither of which is lethal on its own, combines to generate a lethal double mutant phenotype. Genetic interactions allow relatively benign variation to combine and generate more extreme phenotypes, including complex human diseases [11][12][13]16,22 .While several studies have reported interactions between genetic variants in various disease contexts 20,[23][24][25][26] , and though efficient and scalable computational tools have been developed for searching for interactions amongst genome wide SNPs 20,[26][27][28] , discovering them systematically with statistical significance remains a major challenge. For examp...

In hematopoietic stem cell transplantation, donor selection is based primarily on matching donor and patient HLA genes. These genes are highly polymorphic and their typing can result in exact allele assignment at each gene (the resolution at which patients and donors are matched), but it can also result in a set of ambiguous assignments, depending on the typing methodology used. To facilitate rapid identification of matched donors, registries employ statistical algorithms to infer HLA alleles from ambiguous genotypes. Linkage disequilibrium information encapsulated in haplotype frequencies is used to facilitate prediction of the most likely haplotype assignment. An HLA typing with less ambiguity produces fewer high-probability haplotypes and a more reliable prediction. We estimated ambiguity for several HLA typing methods across four continental populations using an information theory-based measure, Shannon's entropy. We used allele and haplotype frequencies to calculate entropy for different sets of 1,000 subjects with simulated HLA typing. Using allele frequencies we calculated an average entropy in Caucasians of 1.65 for serology, 1.06 for allele family level, 0.49 for a 2002-era SSO kit, and 0.076 for single-pass SBT. When using haplotype frequencies in entropy calculations, we found average entropies of 0.72 for serology, 0.73 for allele family level, 0.05 for SSO, and 0.002 for single-pass SBT. Application of haplotype frequencies further reduces HLA typing ambiguity. We also estimated expected confirmatory typing mismatch rates for simulated subjects. In a hypothetical registry with all donors typed using the same method, the entropy values based on haplotype frequencies correspond to confirmatory typing mismatch rates of 1.31% for SSO versus only 0.08% for SBT. Intermediate-resolution single-pass SBT contains the least ambiguity of the methods we evaluated and therefore the most certainty in allele prediction. The presented measure objectively evaluates HLA typing methods and can help define acceptable HLA typing for donor recruitment.

Unrelated stem cell registries have been collecting HLA typing of volunteer bone marrow donors for over 25years. Donor selection for hematopoietic stem cell transplantation is based primarily on matching the alleles of donors and patients at five polymorphic HLA loci. As HLA typing technologies have continually advanced since the beginnings of stem cell transplantation, registries have accrued typings of varied HLA typing ambiguity. We present a new typing resolution score (TRS), based on the likelihood of self-match, that allows the systematic comparison of HLA typings across different methods, data sets and populations. We apply the TRS to chart improvement in HLA typing within the Be The Match Registry of the United States from the initiation of DNA-based HLA typing to the current state of high-resolution typing using next-generation sequencing technologies. In addition, we present a publicly available online tool for evaluation of any given HLA typing. This TRS objectively evaluates HLA typing methods and can help define standards for acceptable recruitment HLA typing.

Standard measures of linkage disequilibrium (LD) provide an incomplete description of the correlation between two loci. Recently, Thomson and Single (2014) described a new asymmetric pair of LD measures (ALD) that give a more complete description of LD. The ALD measures are symmetric and equivalent to the correlation coefficient r when both loci are bi-allelic. When the numbers of alleles at the two loci differ, the ALD measures capture this asymmetry and provide additional detail about the LD structure. In disease association studies the ALD measures are useful for identifying additional disease genes in a genetic region, by conditioning on known effects. In evolutionary genetic studies ALD measures provide insight into selection acting on individual amino acids of specific genes, or other loci in high LD (see Thomson and Single (2014) for these examples). Here we describe new software for computing and visualizing ALD. We demonstrate the utility of this software using haplotype frequency data from the National Marrow Donor Program (NMDP). This enhances our understanding of LD patterns in the NMDP data by quantifying the degree to which LD is asymmetric and also quantifies this effect for individual alleles.

Unrelated stem cell registries have been collecting HLA typing of volunteer bone marrow donors for over 25 years. Donor selection for hematopoietic stem cell transplantation is based primarily on matching the alleles of donors and patients at five polymorphic HLA loci. As HLA typing technologies have continually advanced since the beginnings of stem cell transplantation, registries have accrued typings of varied HLA typing ambiguity. We present a new typing resolution score, based on the likelihood of self-match, that allows the systematic comparison of HLA typings across different methods, data sets and populations. We apply the typing resolution score to chart improvement in HLA typing within the Be The Match Registry of the United States from the initiation of DNA-based HLA typing to the current state of high-resolution typing using next-generation sequencing technologies. In addition, we present a publicly available online tool for evaluation of any given HLA typing. This typing resolution score objectively evaluates HLA typing methods and can help define standards for acceptable recruitment HLA typing.

Disparities in survival after allogeneic hematopoietic cell transplantation have been reported for some race and ethnic groups despite comparable HLA matching. Individuals’ ethnic and race groups, as reported through self-identification, can change over time due to multiple sociological factors. We studied the effect of two measures of genetic similarity in 1,378 recipients who underwent myeloablative first allogeneic hematopoietic cell transplantation between 1995 and 2011 and their unrelated 10-of-10 HLA-A, -B, -C, -DRB1 and DQB1 matched donors. The studied factors were: i) Donor and recipient genetic ancestral admixture, and ii) Pairwise donor/recipient genetic distance. Increased African genetic admixture for either transplant recipients or donors was associated with increased risk of overall mortality (HR=2.26, p=0.005 and HR=3.09, p=0.0002 respectively), Transplant Related Mortality (HR=3.3, p=0.0003 and HR=3.86, p=0.0001 respectively) and decreased Disease Free Survival (HR=1.9, p=0.02 and HR=2.46, p=0.002 respectively). The observed effect, albeit statistically significant, was relevant to small subset of the studied population and was notably correlated with self-reported African-American race. We were not able to control for other non-genetic factors such as access to healthcare or other socio-economic factors, however the results suggest the influence of a genetic driver. Our findings confirm what has been previously reported for African-American recipients and show similar results for donors. No significant association was found with donor/recipient genetic distance.