Visualization of pairwise and multilocus linkage disequilibrium structure using latent forests

A SIMPLE AND FLEXIBLE TEST OF SAMPLE EXCHANGEABILITY WITH APPLICATIONS TO STATISTICAL GENOMICS

In scientific studies involving analyses of multivariate data, basic but important questions often arise for the researcher: Is the sample exchangeable, meaning that the joint distribution of the sample is invariant to the ordering of the units? Are the features independent of one another, or perhaps the features can be grouped so that the groups are mutually independent? In statistical genomics, these considerations are fundamental to downstream tasks such as demographic inference and the construction of polygenic risk scores. We propose a non-parametric approach, which we call the V test, to address these two questions, namely, a test of sample exchangeability given dependency structure of features, and a test of feature independence given sample exchangeability. Our test is conceptually simple, yet fast and flexible. It controls the Type I error across realistic scenarios, and handles data of arbitrary dimensions by leveraging large-sample asymptotics. Through extensive simulations and a comparison against unsupervised tests of stratification based on random matrix theory, we find that our test compares favorably in various scenarios of interest. We apply the test to data from the 1000 Genomes Project, demonstrating how it can be employed to assess exchangeability of the genetic sample, or find optimal linkage disequilibrium (LD) splits for downstream analysis. For exchangeability assessment, we find that removing rare variants can substantially increase the p -value of the test statistic. For optimal LD splitting, the V test reports different optimal splits than previous approaches not relying on hypothesis testing. Software for our methods is available in R (CRAN: flintyR) and Python (PyPI: flintyPy).

Modelling and visualizing fine-scale linkage disequilibrium structure

BACKGROUND

Detailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models.

RESULTS

An efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity.

CONCLUSIONS

The proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis.

EGLN1 variants influence expression and SaO2 levels to associate with high-altitude pulmonary oedema and adaptation

EGLN1 [encoding HIF (hypoxia-inducible factor)-prolyl hydroxylase 2] plays a pivotal role in the HIF pathway and has emerged as one of the most intriguing genes with respect to physiology at HA (high altitude). EGLN1, being an actual oxygen sensor, appears to have a potential role in the functional adaptation to the hypobaric hypoxic environment. In the present study, we screened 30 polymorphisms of EGLN1, evaluated its gene expression and performed association analyses. In addition, the role of allelic variants in altering TF (transcription factor)-binding sites and consequently the replacement of TFs at these loci was also investigated. The study was performed in 250 HAPE-p [HAPE (HA pulmonary oedema)-patients], 210 HAPE-f (HAPE-free controls) and 430 HLs (healthy Ladakhi highland natives). The genotypes of seven polymorphisms, rs1538664, rs479200, rs2486729, rs2790879, rs480902, rs2486736 and rs973252, differed significantly between HAPE-p and HAPE-f (P<0.008). The genotypes AA, TT, AA, GG, CC, AA and GG of rs1538664, rs479200, rs2486729, rs2790879, rs480902, rs2486736 and rs973252, prevalent in HAPE-p, were identified as risk genotypes and their counterpart homozygotes, prevalent in HLs, were identified as protective. EGLN1 expression was up-regulated 4.56-fold in HAPE-p (P=0.0084). The risk genotypes, their haplotypes and interacting genotypes were associated with up-regulated EGLN1 expression (P<0.05). Similarly, regression analysis showed that the risk alleles and susceptible haplotypes were associated with decreased SaO2 (arterial oxygen saturation) levels in the three groups. The significant inverse correlation of SaO2 levels with PASP (pulmonary artery systolic pressure) and EGLN1 expression and the association of these polymorphisms with SaO2 levels and EGLN1 expression contributed to uncovering the molecular mechanism underlying hypobaric hypoxic adaptation and maladaptation.