Approaches to estimating inbreeding coefficients in clinical isolates of Plasmodium falciparum from genomic sequence data

John D O’Brien, Lucas Amenga-Etego, Ruiqi Li
doi: http://dx.doi.org/10.1101/021519

A recent genomic characterization of more than $200$ Plasmodium falciparum samples isolated from the bloodstreams of clinical patients across three continents further supports the presence of significant strain mixture within infections. Consistent with previous studies, these data suggest that the degree of genetic strain admixture within infections varies significantly both within and across populations. The life cycle of the parasite implies that the mixture of multiple genotypes within an infected individual controls the outcrossing rate across populations, making methods for measuring this process in situ central to understanding the genetic epidemiology of the disease. Peculiar features of the P. falciparum genome mean that standard methods for assessing structure within a population — inbreeding coefficients and related $F$-statistics — cannot be used directly. Here we review an initial effort to estimate the degree of mixture within clinical isolates of P. falciparum using these statistics, and provide several generalizations using both frequentist and Bayesian approaches. Using the Bayesian approach, based on the Balding-Nichols model, we provide estimates of inbreeding coefficients for 168 samples from northern Ghana and find significant admixture in more than 70% of samples, and characterize the model fit using posterior predictive checks. We also compare this approach to a recently introduced mixture model and find that for a significant minority of samples the F-statistic-based approach provides a significantly better explanation for the data. We show how to extend this model to a multi-level testing framework that can integrate other data types and use it to demonstrate that transmission intensity significantly associates with degree of structure of within-sample mixture in northern Ghana.

A novel test for detecting gene-gene interactions in trio studies

Brunilda Balliu, Noah Zaitlen
doi: http://dx.doi.org/10.1101/021469

Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new gene-gene interaction test for use in trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard one degree of freedom correlation test of interaction. We show via extensive simulations that our test substantially outperforms existing tests of interaction in trio studies. The gain in power under standard models of phenotype is large, with previous tests requiring more than twice the number of trios to obtain the power of our test. We also demonstrate a bias in a previous trio interaction test and identify its origin. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at http://www.github.com/BrunildaBalliu/TrioEpi.

On enhancing variation detection through pan-genome indexing

Daniel Valenzuela, Niko Välimäki, Esa Pitkänen, Veli Mäkinen
doi: http://dx.doi.org/10.1101/021444

Detection of genomic variants is commonly conducted by aligning a set of reads sequenced from an individual to the reference genome of the species and analyzing the resulting read pileup. Typically, this process finds a subset of variants already reported in databases and additional novel variants characteristic to the sequenced individual. Most of the effort in the literature has been put to the alignment problem on a single reference sequence, although our gathered knowledge on species such as human is pan-genomic: We know most of the common variation in addition to the reference sequence. There have been some efforts to exploit pan-genome indexing, where the most widely adopted approach is to build an index structure on a set of reference sequences containing observed variation combinations. The enhancement in alignment accuracy when using pan-genome indexing has been demonstrated in experiments, but so far the above multiple references pan-genome indexing approach has not been tested on its final goal, that is, in enhancing variation detection. This is the focus of this article: We study a generic approach to add variation detection support on top of the multiple references pan-genomic indexing approach. Namely, we study the read pileup on a multiple alignment of reference genomes, and propose a heaviest path algorithm to extract a new recombined reference sequence. This recombined reference sequence can then be utilized in any standard read alignment and variation detection workflow. We demonstrate that the approach enhances variation detection on realistic data sets.

The structure of the genotype-phenotype map strongly constrains the evolution of non-coding RNA

Kamaludin Dingle, Steffen Schaper, Ard A. Louis
(Submitted on 17 Jun 2015)

The prevalence of neutral mutations implies that biological systems typically have many more genotypes than phenotypes. But can the way that genotypes are distributed over phenotypes determine evolutionary outcomes? Answering such questions is difficult because the number of genotypes can be hyper-astronomically large. By solving the genotype-phentoype (GP) map for RNA secondary structure for systems up to length L=126 nucleotides (where the set of all possible RNA strands would weigh more than the mass of the visible universe) we show that the GP map strongly constrains the evolution of non-coding RNA (ncRNA). Remarkably, simple random sampling over genotypes accurately predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA. Since we ignore natural selection, this close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces. The mapping is extremely biased: the majority of genotypes map to an exponentially small portion of the morphospace of all biophysically possible structures. Such strong constraints provide a non-adaptive explanation for the convergent evolution of structures such as the hammerhead ribozyme. ncRNA presents a particularly clear example of bias in the arrival of variation strongly shaping evolutionary outcomes.