Whole genome association mapping by incompatibilities and local perfect phylogenies

BACKGROUND: With current technology, vast amounts of data can be cheaply and efficiently produced in association studies, and to prevent data analysis to become the bottleneck of studies, fast and efficient analysis methods that scale to such data set sizes must be developed. RESULTS: We present a fast method for accurate localisation of disease causing variants in high density case-control association mapping experiments with large numbers of cases and controls. The method searches for significant clustering of case chromosomes in the "perfect" phylogenetic tree defined by the largest region around each marker that is compatible with a single phylogenetic tree. This perfect phylogenetic tree is treated as a decision tree for determining disease status, and scored by its accuracy as a decision tree. The rationale for this is that the perfect phylogeny near a disease affecting mutation should provide more information about the affected/unaffected classification than random trees. If regions of compatibility contain few markers, due to e.g. large marker spacing, the algorithm can allow the inclusion of incompatibility markers in order to enlarge the regions prior to estimating their phylogeny. Haplotype data and phased genotype data can be analysed. The power and efficiency of the method is investigated on 1) simulated genotype data under different models of disease determination 2) artificial data sets created from the HapMap ressource, and 3) data sets used for testing of other methods in order to compare with these. Our method has the same accuracy as single marker association (SMA) in the simplest case of a single disease causing mutation and a constant recombination rate. However, when it comes to more complex scenarios of mutation heterogeneity and more complex haplotype structure such as found in the HapMap data our method outperforms SMA as well as other fast, data mining approaches such as HapMiner and Haplotype Pattern Mining (HPM) despite being significantly faster. For unphased genotype data, an initial step of estimating the phase only slightly decreases the power of the method. The method was also found to accurately localise the known susceptibility variants in an empirical data set – the ΔF508 mutation for cystic fibrosis – where the susceptibility variant is already known – and to find significant signals for association between the CYP2D6 gene and poor drug metabolism, although for this dataset the highest association score is about 60 kb from the CYP2D6 gene. CONCLUSION: Our method has been implemented in the Blossoc (BLOck aSSOCiation) software. Using Blossoc, genome wide chip-based surveys of 3 million SNPs in 1000 cases and 1000 controls can be analysed in less than two CPU hours

Long-read sequencing to unravel complex structural variants of CEP78 leading to cone-rod dystrophy and hearing loss

Inactivating variants as well as a missense variant in the centrosomal CEP78 gene have been identified in autosomal recessive cone-rod dystrophy with hearing loss (CRDHL), a rare syndromic inherited retinal disease distinct from Usher syndrome. Apart from this, a complex structural variant (SV) implicating CEP78 has been reported in CRDHL. Here we aimed to expand the genetic architecture of typical CRDHL by the identification of complex SVs of the CEP78 region and characterization of their underlying mechanisms. Approaches used for the identification of the SVs are shallow whole-genome sequencing (sWGS) combined with quantitative polymerase chain reaction (PCR) and long-range PCR, or ExomeDepth analysis on whole-exome sequencing (WES) data. Targeted or whole-genome nanopore long-read sequencing (LRS) was used to delineate breakpoint junctions at the nucleotide level. For all SVs cases, the effect of the SVs on CEP78 expression was assessed using quantitative PCR on patient-derived RNA. Apart from two novel canonical CEP78 splice variants and a frameshifting single-nucleotide variant (SNV), two SVs affecting CEP78 were identified in three unrelated individuals with CRDHL: a heterozygous total gene deletion of 235 kb and a partial gene deletion of 15 kb in a heterozygous and homozygous state, respectively. Assessment of the molecular consequences of the SVs on patient’s materials displayed a loss-of-function effect. Delineation and characterization of the 15-kb deletion using targeted LRS revealed the previously described complex CEP78 SV, suggestive of a recurrent genomic rearrangement. A founder haplotype was demonstrated for the latter SV in cases of Belgian and British origin, respectively. The novel 235-kb deletion was delineated using whole-genome LRS. Breakpoint analysis showed microhomology and pointed to a replication-based underlying mechanism. Moreover, data mining of bulk and single-cell human and mouse transcriptional datasets, together with CEP78 immunostaining on human retina, linked the CEP78 expression domain with its phenotypic manifestations. Overall, this study supports that the CEP78 locus is prone to distinct SVs and that SV analysis should be considered in a genetic workup of CRDHL. Finally, it demonstrated the power of sWGS and both targeted and whole-genome LRS in identifying and characterizing complex SVs in patients with ocular diseases

Inferring Signatures of Positive Selection in Whole-Genome Sequencing Data: An Overview of Haplotype-Based Methods

Signatures of positive selection in the genome are a characteristic mark of adaptation that can reveal an ongoing, recent, or ancient response to environmental change throughout the evolution of a population. New sources of food, climate conditions, and exposure to pathogens are only some of the possible sources of selective pressure, and the rise of advantageous genetic variants is a crucial determinant of survival and reproduction. In this context, the ability to detect these signatures of selection may pinpoint genetic variants that are responsible for a significant change in gene regulation, gene expression, or protein synthesis, structure, and function. This review focuses on statistical methods that take advantage of linkage disequilibrium and haplotype determination to reveal signatures of positive selection in whole-genome sequencing data, showing that they emerge from different descriptions of the same underlying event. Moreover, considerations are provided around the application of these statistics to different species, their suitability for ancient DNA, and the usefulness of discovering variants under selection for biomedicine and public health in an evolutionary medicine framework

Efficient mining of haplotype patterns for disease prediction

Ph.DDOCTOR OF PHILOSOPH

Association Analysis of Additive Effects and Epistasis Between Human Candidate Malaria Protective Genes

Malaria is a major cause of childhood death in Africa and host genetic factors play a key role in determining survival from this disease. Although many candidate loci have been identified, there have been difficulties in confirming the significance of some of these loci. To some extent this might be explained by the added complexity of epistasis, or gene-gene interactions. Through this thesis I aimed: (1) to re-appraise a range of candidate malaria-association genes using a large-scale case-control study of severe malaria (SM) in Kilifi, Kenya; (2) to compare different approaches to detecting epistatic interactions; (3) to look for evidence of epistasis between candidate genes in my data set; (4) to examine the haplotype structure and linkage disequilibrium (LD) patterns for two such implicated variants (HbS and α+thalassaemia) and their gene regions, that coexist in the Kilifi population, and (5) to use these exemplars as a starting point for investigating the process of detecting epistasis in SM in a genome-wide association study (GWAS). Out of 71 candidate genes investigated, I observed that polymorphisms affecting various aspects of red blood cells (including HBB, HBA, G6PD, FREM3, INPP4B, ATP2B4 and ABO) were among those associated with the strongest signals of differential susceptibility to SM. Because of their prominence in malaria, HbS and α+thalassaemia were used to illustrate interaction analysis at the GWAS level. This included looking at the structure of the genomic regions surrounding the genes. As expected, a single haplotype of approximately 200kb was seen surrounding HbS, which then diverged into 2 major haplotypes spanning a further 1Mb either side, an observation that was largely explained by ethnicity. In contrast, no marked LD/haplotype structure was observed in the genomic region surrounding the α+thalassaemia deletion, suggesting that this is a very old polymorphism. Through this study, I confirmed the negative epistasis seen between HbS and α+thalassaemia using a study design (case-control) that was different to that used previously (cohort), although this was not among the most significant of the interactions I detected. I searched for pairwise interactions between these two polymorphisms at a genome wide level using heterozygous and additive models for HbS and α+thalassaemia respectively. For each scan a single region reaching a significance level of -7 was found (STX18 for HbS and MYEOV for α+thalassaemia), plus several other novel signals were identified in the 10-6 to 10-7 significance region. Further work will be required to validate these signals and the challenge will be to try and understand their biological relevance. This is now becoming possible with datasets in many diseases, including malaria, being released into the public domain. But, as this Kenyan study has shown, having large group sizes, high quality clinical and genetic data, it is possible to begin to explore genetic interactions in a disease setting

Distilling Artificial Recombinants from Large Sets of Complete mtDNA Genomes

BACKGROUND: Large-scale genome sequencing poses enormous problems to the logistics of laboratory work and data handling. When numerous fragments of different genomes are PCR amplified and sequenced in a laboratory, there is a high imminent risk of sample confusion. For genetic markers, such as mitochondrial DNA (mtDNA), which are free of natural recombination, single instances of sample mix-up involving different branches of the mtDNA phylogeny would give rise to reticulate patterns and should therefore be detectable. METHODOLOGY/PRINCIPAL FINDINGS: We have developed a strategy for comparing new complete mtDNA genomes, one by one, to a current skeleton of the worldwide mtDNA phylogeny. The mutations distinguishing the reference sequence from a putative recombinant sequence can then be allocated to two or more different branches of this phylogenetic skeleton. Thus, one would search for two (or three) near-matches in the total mtDNA database that together best explain the variation seen in the recombinants. The evolutionary pathway from the mtDNA tree connecting this pair together with the recombinant then generate a grid-like median network, from which one can read off the exchanged segments. CONCLUSIONS: We have applied this procedure to a large collection of complete human mtDNA sequences, where several recombinants could be distilled by our method. All these recombinant sequences were subsequently corrected by de novo experiments--fully concordant with the predictions from our data-analytical approach