A fast and robust statistical test based on likelihood ratio with Bartlett correction to identify Granger causality between gene sets

Characterization of CYP2C19 pharmacogenetic variation in African populations and comparison with other global populations

Background: CYP2C19 is important in the metabolism of clopidogrel and several antidepressants. This study aimed to characterize the distribution of CYP2C19 star alleles (haplotypes) across diverse African populations compared with global populations. Methods: CYP2C19 star alleles and diplotypes were called from high coverage genomes using the StellarPGx pipeline. Results: CYP2C19*1 (51%), *2 (17%) and *17 (22%) were the most common star alleles across African populations in this study. It was observed that 3% of African participants had potentially novel CYP2C19 haplotypes. Conclusion: This study supports the necessity for CYP2C19 pharmacogenetic testing in African and global clinical settings, as well as the importance of comprehensive star allele characterization in the African context.

A whole genome sequencing approach to anterior cruciate ligament rupture-a twin study in two unrelated families

Predisposition to anterior cruciate ligament (ACL) rupture is multi-factorial, with variation in the genome considered a key intrinsic risk factor. Most implicated loci have been identified from candidate gene-based approach using case-control association settings. Here, we leverage a hypothesis-free whole genome sequencing in two two unrelated families (Family A and B) each with twins with a history of recurrent ACL ruptures acquired playing rugby as their primary sport, aimed to elucidate biologically relevant function-altering variants and genetic modifiers in ACL rupture. Family A monozygotic twin males (Twin 1 and Twin 2) both sustained two unilateral non-contact ACL ruptures of the right limb while playing club level touch rugby. Their male sibling sustained a bilateral non-contact ACL rupture while playing rugby union was also recruited. The father had sustained a unilateral non-contact ACL rupture on the right limb while playing professional amateur level football and mother who had participated in dancing for over 10 years at a social level, with no previous ligament or tendon injuries were both recruited. Family B monozygotic twin males (Twin 3 and Twin 4) were recruited with Twin 3 who had sustained a unilateral non-contact ACL rupture of the right limb and Twin 4 sustained three non-contact ACL ruptures (two in right limb and one in left limb), both while playing provincial level rugby union. Their female sibling participated in karate and swimming activities; and mother in hockey (4 years) horse riding (15 years) and swimming, had both reported no previous history of ligament or tendon injury. Variants with potential deleterious, loss-of-function and pathogenic effects were prioritised. Identity by descent, molecular dynamic simulation and functional partner analyses were conducted. We identified, in all nine affected individuals, including twin sets, non-synonymous SNPs in three genes: COL12A1 and CATSPER2, and KCNJ12 that are commonly enriched for deleterious, loss-of-function mutations, and their dysfunctions are known to be involved in the development of chronic pain, and represent key therapeutic targets. Notably, using Identity By Decent (IBD) analyses a long shared identical sequence interval which included the LINC01250 gene, around the telomeric region of chromosome 2p25.3, was common between affected twins in both families, and an affected brother'. Overall gene sets were enriched in pathways relevant to ACL pathophysiology, including complement/coagulation cascades (p = 3.0e-7), purine metabolism (p = 6.0e-7) and mismatch repair (p = 6.9e-5) pathways. Highlighted, is that this study fills an important gap in knowledge by using a WGS approach, focusing on potential deleterious variants in two unrelated families with a historical record of ACL rupture; and providing new insights into the pathophysiology of ACL, by identifying gene sets that contribute to variability in ACL risk.

Characterization of POR haplotype distribution in African populations and comparison with other global populations

Background & aim: POR is an enzyme that mediates electron transfer to enable the drug-metabolizing activity of CYP450 proteins. However, POR has been understudied in pharmacogenomics despite this vital role. This study aimed to characterize the genetic variation in POR across African populations and to compare the star allele (haplotype) distribution with that in other global populations. Materials & methods: POR star alleles were called from whole-genome sequencing data using the StellarPGx pipeline. Results: In addition to the common POR*1 and *28 (defined by rs1057868), five novel rare haplotypes were computationally inferred. No significant frequency differences were observed among the majority of African populations. However, POR*28 was observed at a higher frequency in individuals of non-African ancestry. Conclusion: This study highlights the distribution of POR alleles in Africa and across global populations with a view toward informing future precision medicine implementation.

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder

Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks' time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models' parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal's application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain's right to the left hemisphere is different between ASD and controls.

Identifying genetic variants and pathways associated with extreme levels of fetal hemoglobin in sickle cell disease in Tanzania

BACKGROUND

Sickle cell disease (SCD) is a blood disorder caused by a point mutation on the beta globin gene resulting in the synthesis of abnormal hemoglobin. Fetal hemoglobin (HbF) reduces disease severity, but the levels vary from one individual to another. Most research has focused on common genetic variants which differ across populations and hence do not fully account for HbF variation.

METHODS

We investigated rare and common genetic variants that influence HbF levels in 14 SCD patients to elucidate variants and pathways in SCD patients with extreme HbF levels (≥7.7% for high HbF) and (≤2.5% for low HbF) in Tanzania. We performed targeted next generation sequencing (Illumina_Miseq) covering exonic and other significant fetal hemoglobin-associated loci, including BCL11A, MYB, HOXA9, HBB, HBG1, HBG2, CHD4, KLF1, MBD3, ZBTB7A and PGLYRP1.

RESULTS

Results revealed a range of genetic variants, including bi-allelic and multi-allelic SNPs, frameshift insertions and deletions, some of which have functional importance. Notably, there were significantly more deletions in individuals with high HbF levels (11% vs 0.9%). We identified frameshift deletions in individuals with high HbF levels and frameshift insertions in individuals with low HbF. CHD4 and MBD3 genes, interacting in the same sub-network, were identified to have a significant number of pathogenic or non-synonymous mutations in individuals with low HbF levels, suggesting an important role of epigenetic pathways in the regulation of HbF synthesis.

CONCLUSIONS

This study provides new insights in selecting essential variants and identifying potential biological pathways associated with extreme HbF levels in SCD interrogating multiple genomic variants associated with HbF in SCD.

Exome sequencing identifies targets in the treatment-resistant ophthalmoplegic subphenotype of myasthenia gravis

Treatment-resistant ophthalmoplegia (OP-MG) is not uncommon in individuals with African genetic ancestry and myasthenia gravis (MG). To identify OP-MG susceptibility genes, extended whole exome sequencing was performed using extreme phenotype sampling (11 OP-MG vs 4 control-MG) all with acetylcholine receptor-antibody positive MG. This approach identified 356 variants that were twice as frequent in OP-MG compared to control-MG individuals. After performing probability test estimates and filtering variants according to those 'suggestive' of association with OP-MG (p < 0.05), only three variants remained which were expressed in extraocular muscles. Validation in 25 OP-MG and 50 control-MG cases supported the association of DDX17_delG (p = 0.014) and SPTLC3_insACAC (p = 0.055) with OP-MG, but ST8SIA1_delCCC could not be verified by Sanger sequencing. A parallel approach, using a semantic model informed by current knowledge of MG-pathways, identified an African-specific interleukin-6 receptor (IL6R) variant, IL6R c.*3043 T>C, that was more frequent in OP-MG compared to control-MG cases (p = 0.069) and population controls (p = 0.043). A weighted genetic risk score, derived from the odds ratios of association of these variants with OP-MG, correlated with the OP-MG phenotype as opposed to control MG. This unbiased approach implicates several potentially functional gene variants in the gangliosphingolipid and myogenesis pathways in the development of the OP-MG subphenotype.

A tutorial to identify nonlinear associations in gene expression time series data

The study of gene regulatory networks is the basis to understand the biological complexity of several diseases and/or cell states. It has become the core of research in the field of systems biology. Several mathematical methods have been developed in the last decade, especially in the analysis of time series gene expression data derived from microarrays and sequencing-based methods. Most of the models available in the literature assumes linear associations among genes and do not infer directionality in these connections or uses a priori biological knowledge to set the directionality. However, in several cases, a priori biological information is not available. In this context, we describe a statistical method, namely nonlinear vector autoregressive model to estimate nonlinear relationships and also to infer directionality at the edges of the network by using the temporal information of the time series gene expression data without a priori biological information.

Functional clustering of time series gene expression data by Granger causality

Background

A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes.

Results

In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence.

Conclusions

This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.

SIR2 and other genes are abundantly expressed in long-lived natural segregants for replicative aging of the budding yeast Saccharomyces cerevisiae

We investigated the mechanism underlying the natural variation in longevity within natural populations using the model budding yeast, Saccharomyces cerevisiae. We analyzed whole-genome gene expression in four progeny of a natural S. cerevisiae strain that display differential replicative aging. Genes with different expression levels in short- and long-lived strains were classified disproportionately into metabolism, transport, development, transcription or cell cycle, and organelle organization (mitochondrial, chromosomal, and cytoskeletal). With several independent validating experiments, we detected 15 genes with consistent differential expression levels between the long- and the short-lived progeny. Among those 15, SIR2, HSP30, and TIM17 were upregulated in long-lived strains, which is consistent with the known effects of gene silencing, stress response, and mitochondrial function on aging. The link between SIR2 and yeast natural life span variation offers some intriguing ties to the allelic association of the human homolog SIRT1 to visceral obesity and metabolic response to lifestyle intervention.