Extending admixture mapping to nuclear pedigrees: application to sarcoidosis

FactorialHMM: fast and exact inference in factorial hidden Markov models

MOTIVATION

Hidden Markov models (HMMs) are powerful tools for modeling processes along the genome. In a standard genomic HMM, observations are drawn, at each genomic position, from a distribution whose parameters depend on a hidden state, and the hidden states evolve along the genome as a Markov chain. Often, the hidden state is the Cartesian product of multiple processes, each evolving independently along the genome. Inference in these so-called Factorial HMMs has a naïve running time that scales as the square of the number of possible states, which by itself increases exponentially with the number of sub-chains; such a running time scaling is impractical for many applications. While faster algorithms exist, there is no available implementation suitable for developing bioinformatics applications.

RESULTS

We developed FactorialHMM, a Python package for fast exact inference in Factorial HMMs. Our package allows simulating either directly from the model or from the posterior distribution of states given the observations. Additionally, we allow the inference of all key quantities related to HMMs: (i) the (Viterbi) sequence of states with the highest posterior probability; (ii) the likelihood of the data and (iii) the posterior probability (given all observations) of the marginal and pairwise state probabilities. The running time and space requirement of all procedures is linearithmic in the number of possible states. Our package is highly modular, providing the user with maximal flexibility for developing downstream applications.

AVAILABILITY AND IMPLEMENTATION

https://github.com/regevs/factorial_hmm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ALDsuite: Dense marker MALD using principal components of ancestral linkage disequilibrium

Background

Mapping by admixture linkage disequilibrium (MALD) is a whole genome gene mapping method that uses LD from extended blocks of ancestry inherited from parental populations among admixed individuals to map associations for diseases, that vary in prevalence among human populations. The extended LD queried for marker association with ancestry results in a greatly reduced number of comparisons compared to standard genome wide association studies. As ancestral population LD tends to confound the analysis of admixture LD, the earliest algorithms for MALD required marker sets sufficiently sparse to lack significant ancestral LD between markers. However current genotyping technologies routinely provide dense SNP data, which convey more information than sparse sets, if this information can be efficiently used. There are currently no software solutions that offer both local ancestry inference using dense marker data and disease association statistics.

Results

We present here an R package, ALDsuite, which accounts for local LD using principal components of haplotypes from surrogate ancestral population data, and includes tools for quality control of data, MALD, downstream analysis of results and visualization graphics.

Conclusions

ALDsuite offers a fast, accurate estimation of global and local ancestry and comes bundled with the tools needed for MALD, from data quality control through mapping of and visualization of disease genes.

Admixture fine-mapping in African Americans implicates XAF1 as a possible sarcoidosis risk gene

Sarcoidosis is a complex, multi-organ granulomatous disease with a likely genetic component. West African ancestry confers a higher risk for sarcoidosis than European ancestry. Admixture mapping provides the most direct method to locate genes that underlie such ethnic variation in disease risk. We sought to identify genetic risk variants within four previously-identified ancestry-associated regions—6p24.3–p12.1, 17p13.3–13.1, 2p13.3–q12.1, and 6q23.3–q25.2—in a sample of 2,727 African Americans. We used logistic regression fit by generalized estimating equations and the MIX score statistic to determine which variants within ancestry-associated regions were associated with risk and responsible for the admixture signal. Fine mapping was performed by imputation, based on a previous genome-wide association study; significant variants were validated by direct genotyping. Within the 6p24.3–p12.1 locus, the most significant ancestry-adjusted SNP was rs74318745 (p = 9.4*10⁻¹¹), an intronic SNP within the HLA-DRA gene that did not solely explain the admixture signal, indicating the presence of more than a single risk variant within this well-established sarcoidosis risk region. The locus on chromosome 17p13.3–13.1 revealed a novel sarcoidosis risk SNP, rs6502976 (p = 9.5*10⁻⁶), within intron 5 of the gene X-linked Inhibitor of Apoptosis Associated Factor 1 (XAF1) that accounted for the majority of the admixture linkage signal. Immunohistochemical expression studies demonstrated lack of expression of XAF1 and a corresponding high level of expression of its downstream target, X-linked Inhibitor of Apoptosis (XIAP) in sarcoidosis granulomas. In conclusion, ancestry and association fine mapping revealed a novel sarcoidosis susceptibility gene, XAF1, which has not been identified by previous genome-wide association studies. Based on the known biology of the XIAP/XAF1 apoptosis pathway and the differential expression patterns of XAF1 and XIAP in sarcoidosis granulomas, we suggest that this pathway may play a role in the maintenance of sarcoidosis granulomas.