The African Society of Human Genetics successfully launches global data science workshops.

To accelerate the impact of African genomics on human health, data science skills and awareness of Africa's rich genetic diversity must be strengthened globally. We describe the first African genomics data science workshop, implemented by the African Society of Human Genetics (AfSHG) and international partners, providing a framework for future workshops.

Genome-wide association study identifies novel candidate malaria resistance genes in Cameroon.

Recent data suggest that only a small fraction of severe malaria heritability is explained by the totality of genetic markers discovered so far. The extensive genetic diversity within African populations means that significant associations are likely to be found in Africa. In their series of multi-site genome-wide association studies (GWAS) across sub-Saharan Africa, the Malaria Genomic Epidemiology Network (MalariaGEN) observed specific limitations and encouraged country-specific analyses. Here, we present findings of a GWAS of Cameroonian participants that contributed to MalariaGEN projects (n = 1103). We identified protective associations at polymorphisms within the enhancer region of CHST15 [Benjamin-Hochberg false discovery rate (FDR) < 0.02] that are specific to populations of African ancestry, and that tag strong eQTLs of CHST15 in hepatic cells. In-silico functional analysis revealed a signature of epigenetic regulation of CHST15 that is preserved in populations in historically malaria endemic regions, with haplotype analysis revealing a haplotype that is specific to these populations. Association analysis by ethnolinguistic group identified protective associations within SOD2 (FDR < 0.04), a gene previously shown to be significantly induced in pre-asymptomatic malaria patients from Cameroon. Haplotype analysis revealed substantial heterogeneity within the beta-like globin (HBB) gene cluster amongst the major ethnic groups in Cameroon confirming differential malaria pressure and underscoring age-old fine-scale genetic structure within the country. Our findings revealed novel insights in the evolutionary genetics of populations living in Cameroon under malaria pressure with new significant protective loci (CHST15 and SOD2) and emphasized the significant attenuation of genetic association signals by fine-scale genetic structure.

Whole genome sequencing reveals population diversity and variation in HIV-1 specific host genes.

HIV infection continues to be a major global public health issue. The population heterogeneity in susceptibility or resistance to HIV-1 and progression upon infection is attributable to, among other factors, host genetic variation. Therefore, identifying population-specific variation and genetic modifiers of HIV infectivity can catapult the invention of effective strategies against HIV-1 in African populations. Here, we investigated whole genome sequences of 390 unrelated HIV-positive and -negative individuals from Botswana. We report 27.7 million single nucleotide variations (SNVs) in the complete genomes of Botswana nationals, of which 2.8 million were missing in public databases. Our population structure analysis revealed a largely homogenous structure in the Botswana population. Admixture analysis showed elevated components shared between the Botswana population and the Niger-Congo (65.9%), Khoe-San (32.9%), and Europeans (1.1%) ancestries in the population of Botswana. Statistical significance of the mutational burden of deleterious and loss-of-function variants per gene against a null model was estimated. The most deleterious variants were enriched in five genes: ACTRT2 (the Actin Related Protein T2), HOXD12 (homeobox D12), ABCB5 (ATP binding cassette subfamily B member 5), ATP8B4 (ATPase phospholipid transporting 8B4) and ABCC12 (ATP Binding Cassette Subfamily C Member 12). These genes are enriched in the glycolysis and gluconeogenesis (p < 2.84e-6) pathways and therefore, may contribute to the emerging field of immunometabolism in which therapy against HIV-1 infection is being evaluated. Published transcriptomic evidence supports the role of the glycolysis/gluconeogenesis pathways in the regulation of susceptibility to HIV, and that cumulative effects of genetic modifiers in glycolysis/gluconeogenesis pathways may potentially have effects on the expression and clinical variability of HIV-1. Identified genes and pathways provide novel avenues for other interventions, with the potential for informing the design of new therapeutics.

Pharmacogenomics of Hypertension in Africa: Paving the Way for a Pharmacogenetic-Based Approach for the Treatment of Hypertension in Africans.

In Africa, the burden of hypertension has been rising at an alarming rate for the last two decades and is a major cause for cardiovascular disease (CVD) mortality and morbidity. Hypertension is characterised by elevated blood pressure (BP) ≥ 140/90 mmHg. Current hypertension guidelines recommend the use of antihypertensives belonging to the following classes: calcium channel blockers (CCB), angiotensin converting inhibitors (ACEI), angiotensin receptor blockers (ARB), diuretics, ß-blockers, and mineralocorticoid receptor antagonists (MRAs), to manage hypertension. Still, a considerable number of hypertensives in Africa have their BP uncontrolled due to poor drug response and remain at the risk of CVD events. Genetic factors are a major contributing factor, accounting for 20% to 80% of individual variability in therapy and poor response. Poor response to antihypertensive drug therapy is characterised by elevated BPs and occurrence of adverse drug reactions (ADRs). As a result, there have been numerous studies which have examined the role of genetic variation and its influence on antihypertensive drug response. These studies are predominantly carried out in non-African populations, including Europeans and Asians, with few or no Africans participating. It is important to note that the greatest genetic diversity is observed in African populations as well as the highest prevalence of hypertension. As a result, this warrants a need to focus on how genetic variation affects response to therapeutic interventions used to manage hypertension in African populations. In this paper, we discuss the implications of genetic diversity in CYP11B2, GRK4, NEDD4L, NPPA, SCNN1B, UMOD, CYP411, WNK, CYP3A4/5, ACE, ADBR1/2, GNB3, NOS3, B2, BEST3, SLC25A31, LRRC15 genes, and chromosome 12q loci on hypertension susceptibility and response to antihypertensive therapy. We show that African populations are poorly explored genetically, and for the few characterised genes, they exhibit qualitative and quantitative differences in the profile of pharmacogene variants when compared to other ethnic groups. We conclude by proposing prioritization of pharmacogenetics research in Africa and possible adoption of pharmacogenetic-guided therapies for hypertension in African patients. Finally, we outline the implications, challenges, and opportunities these studies present for populations of non-European descent.

Computational approaches for network-based integrative multi-omics analysis.

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

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.

Dissecting Generalizability and Actionability of Disease-Associated Genes From 20 Worldwide Ethnolinguistic Cultural Groups.

Findings resulting from whole-genome sequencing (WGS) have markedly increased due to the massive evolvement of sequencing methods and have led to further investigations such as clinical actionability of genes, as documented by the American College of Medical Genetics and Genomics (ACMG). ACMG's actionable genes (ACGs) may not necessarily be clinically actionable across all populations worldwide. It is critical to examine the actionability of these genes in different populations. Here, we have leveraged a combined WES from the African Genome Variation and 1000 Genomes Project to examine the generalizability of ACG and potential actionable genes from four diseases: high-burden malaria, TB, HIV/AIDS, and sickle cell disease. Our results suggest that ethnolinguistic cultural groups from Africa, particularly Bantu and Khoesan, have high genetic diversity, high proportion of derived alleles at low minor allele frequency (0.0-0.1), and the highest proportion of pathogenic variants within HIV, TB, malaria, and sickle cell diseases. In contrast, ethnolinguistic cultural groups from the non-Africa continent, including Latin American, Afro-related, and European-related groups, have a high proportion of pathogenic variants within ACG than most of the ethnolinguistic cultural groups from Africa. Overall, our results show high genetic diversity in the present actionable and known disease-associated genes of four African high-burden diseases, suggesting the limitation of transferability or generalizability of ACG. This supports the use of personalized medicine as beneficial to the worldwide population as well as actionable gene list recommendation to further foster equitable global healthcare. The results point out the bias in the knowledge about the frequency distribution of these phenotypes and genetic variants associated with some diseases, especially in African and African ancestry populations.

Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis.

Over the past decades, advanced high-throughput technologies have continuously contributed to genome-wide association studies (GWASs). GWAS meta-analysis has been increasingly adopted, has cross-ancestry replicability, and has power to illuminate the genetic architecture of complex traits, informing about the reliability of estimation effects and their variability across human ancestries. However, detecting genetic variants that have low disease risk still poses a challenge. Designing a meta-analysis approach that combines the effect of various SNPs within genes or genes within pathways from multiple independent population GWASs may be helpful in identifying associations with small effect sizes and increasing the association power. Here, we proposed ancMETA, a Bayesian graph-based framework, to perform the gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes, and genes within subnetwork/pathways across multiple independent population GWASs to deconvolute the interactions between genes underlying the pathogenesis of complex diseases across human populations. We assessed the proposed framework on simulated datasets, and the results show that the proposed model holds promise for increasing statistical power for meta-analysis of genetic variants underlying the pathogenesis of complex diseases. To illustrate the proposed meta-analysis framework, we leverage seven different European bipolar disorder (BD) cohorts, and we identify variants in the angiotensinogen (AGT) gene to be significantly associated with BD across all 7 studies. We detect a commonly significant BD-specific subnetwork with the ESR1 gene as the main hub of a subnetwork, associated with neurotrophin signaling (p = 4e -14) and myometrial relaxation and contraction (p = 3e -08) pathways. ancMETA provides a new contribution to post-GWAS methodologies and holds promise for comprehensively examining interactions between genes underlying the pathogenesis of genetic diseases and also underlying ethnic differences.

Investigation of multiple populations highlight VEGFA polymorphisms to modulate anterior cruciate ligament injury.

Polymorphisms in VEGFA and KDR encoding proteins have been associated with anterior cruciate ligament (ACL) injury risk. We leveraged a collective sample from Sweden, Poland, and Australia to investigate the association of functional polymorphisms in VEGFA and KDR with susceptibility to ACL injury risk. Using a case-control genetic association approach, polymorphisms in VEGFA and KDR were genotyped and haplotypes inferred from 765 controls, and 912 cases clinically diagnosed with ACL rupture. For VEGFA, there was a significant overrepresentation of the rs2010963 CC genotype (p = 0.0001, false discovery rate [FDR]: p = 0.001, odds ratio [OR]: 2.16, 95% confidence interval [CI]: 1.47-3.19) in the combined ACL group (18%) compared to the combined control group (11%). The VEGFA (rs699947 C/A, rs1570360 G/A, rs2010963 G/C) A-A-G haplotype was significantly (p = 0.010, OR: 0.85, 95% CI: 0.69-1.05) underrepresented in the combined ACL group (23%) compared to the combined control group (28%). In addition, the A-G-G construct was significantly (p = 0.036, OR: 0.81, 95% CI: 0.64-1.02) underrepresented in the combined ACL group (12%) compared to the combined CON group (16%). Our findings support the association of the VEGFA rs2010963 CC genotype with increased risk and (ii) the VEGFA A-A-G haplotype with a reduced risk, and are in alignment with the a priori hypothesis. Collectively identifying a genetic interval within VEGFA to be implicated in ACL risk modulation and highlight further the importance of vascular regulation in ligament biology.

Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways.

Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci, while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.

Network-driven analysis of human-Plasmodium falciparum interactome: processes for malaria drug discovery and extracting in silico targets.

BACKGROUND: The emergence and spread of malaria drug resistance have resulted in the need to understand disease mechanisms and importantly identify essential targets and potential drug candidates. Malaria infection involves the complex interaction between the host and pathogen, thus, functional interactions between human and Plasmodium falciparum is essential to obtain a holistic view of the genetic architecture of malaria. Several functional interaction studies have extended the understanding of malaria disease and integrating such datasets would provide further insights towards understanding drug resistance and/or genetic resistance/susceptibility, disease pathogenesis, and drug discovery. METHODS: This study curated and analysed data including pathogen and host selective genes, host and pathogen protein sequence data, protein-protein interaction datasets, and drug data from literature and databases to perform human-host and P. falciparum network-based analysis. An integrative computational framework is presented that was developed and found to be reasonably accurate based on various evaluations, applications, and experimental evidence of outputs produced, from data-driven analysis. RESULTS: This approach revealed 8 hub protein targets essential for parasite and human host-directed malaria drug therapy. In a semantic similarity approach, 26 potential repurposable drugs involved in regulating host immune response to inflammatory-driven disorders and/or inhibiting residual malaria infection that can be appropriated for malaria treatment. Further analysis of host-pathogen network shortest paths enabled the prediction of immune-related biological processes and pathways subverted by P. falciparum to increase its within-host survival. CONCLUSIONS: Host-pathogen network analysis reveals potential drug targets and biological processes and pathways subverted by P. falciparum to enhance its within malaria host survival. The results presented have implications for drug discovery and will inform experimental studies.

Utilization of Pneumococcal Vaccine and Penicillin Prophylaxis in Sickle Cell Disease in Three African Countries: Assessment among Healthcare Providers in SickleInAfrica.

Sickle cell disease is a genetic disease with a predisposition to infections caused by encapsulated organisms, especially Streptococcus pneumoniae. Pneumococcal vaccines and prophylactic penicillin have reduced the rate of this infection and mortality in sickle cell disease. However, implementation of these interventions is limited in Africa. The objectives of the study were to assess health care providers' behaviors with the implementation of pneumococcal vaccination and penicillin prophylaxis and to identify barriers to their use. A 25-item online questionnaire was administered through SickleinAfrica: a network of researchers, and healthcare providers, in Ghana, Nigeria, and Tanzania, working to improve health outcomes of sickle cell disease in Africa. Data was collected and managed using the Research Electronic Data Capture (REDCap), tools and data analysis was done using STATA version 13 and R statistical software. Eighty-two medical practitioners responded to the questionnaire. Only 54.0 and 48.7% of respondents indicated the availability of published guidelines on sickle cell disease management and pneumococcal vaccine use, respectively, at their facilities. The majority (54.0%) perceived that the vaccines are effective but over 20.0% were uncertain of their usefulness. All respondents from Ghana and Tanzania affirmed the availability of guidelines for penicillin prophylaxis in contrast to 44.1% in Nigeria. Eighty-five percent of respondents affirmed the need for penicillin prophylaxis but 15.0% had a contrary opinion for reasons including the rarity of isolation of Streptococcus pneumoniae in African studies, and therefore, the uncertainty of its benefit. Lack of published guidelines on the management of sickle cell disease and doubts about the necessity of prophylactic measures are potential barriers to the implementation of effective interventions.

Reviewing and assessing existing meta-analysis models and tools.

Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Fine scale human genetic structure in three regions of Cameroon reveals episodic diversifying selection.

Inferences from genetic association studies rely largely on the definition and description of the underlying populations that highlight their genetic similarities and differences. The clustering of human populations into subgroups (population structure) can significantly confound disease associations. This study investigated the fine-scale genetic structure within Cameroon that may underlie disparities observed with Cameroonian ethnicities in malaria genome-wide association studies in sub-Saharan Africa. Genotype data of 1073 individuals from three regions and three ethnic groups in Cameroon were analyzed using measures of genetic proximity to ascertain fine-scale genetic structure. Model-based clustering revealed distinct ancestral proportions among the Bantu, Semi-Bantu and Foulbe ethnic groups, while haplotype-based coancestry estimation revealed possible longstanding and ongoing sympatric differentiation among individuals of the Foulbe ethnic group, and their Bantu and Semi-Bantu counterparts. A genome scan found strong selection signatures in the HLA gene region, confirming longstanding knowledge of natural selection on this genomic region in African populations following immense disease pressure. Signatures of selection were also observed in the HBB gene cluster, a genomic region known to be under strong balancing selection in sub-Saharan Africa due to its co-evolution with malaria. This study further supports the role of evolution in shaping genomes of Cameroonian populations and reveals fine-scale hierarchical structure among and within Cameroonian ethnicities that may impact genetic association studies in the country.

Whole exome sequencing identifies rare coding variants in novel human-mouse ortholog genes in African individuals diagnosed with non-syndromic hearing impairment.

Physiologically, the human and murine hearing systems are very similar, justifying the extensive use of mice in experimental models for hearing impairment (HI). About 340 murine HI genes have been reported; however, whether variants in all human-mouse ortholog genes contribute to HI has been rarely investigated. In humans, nearly 120 HI genes have been identified to date, with GJB2 and GJB6 variants accounting for half of congenital HI cases, of genetic origin, in populations of European and Asian ancestries, but not in most African populations. The contribution of variants in other known genes of HI among the populations of African ancestry is poorly studied and displays the lowest pick-up rate. We used whole exome sequencing (WES) to investigate pathogenic and likely pathogenic (PLP) variants in 34 novel human-mouse orthologs HI genes, in 40 individuals from Cameroon and South Africa diagnosed with non-syndromic hearing impairment (NSHI), and compared the data to WES data of 129 ethnically matched controls. In addition, protein modeling for selected PLP gene variants, gene enrichment, and network analyses were performed. A total of 4/38 murine genes, d6wsu163e, zfp719, grp152 and minar2, had no human orthologs. WES identified three rare PLP variants in 3/34 human-mouse orthologs genes in three unrelated Cameroonian patients, namely: OCM2, c.227G>C p.(Arg76Thr) and LRGI1, c.1657G>A p.(Gly533Arg) in a heterozygous state, and a PLP variant MCPH1, c.2311C>G p.(Pro771Ala) in a homozygous state. In silico functional analyses suggest that these human-mouse ortholog genes functionally co-expressed interactions with well-established HI genes: GJB2 and GJB6. The study found one homozygous variant in MCPH1, likely to explain HI in one patient, and suggests that human-mouse ortholog variants could contribute to the understanding of the physiology of hearing in humans.

Whole exome sequencing reveals pathogenic variants in MYO3A, MYO15A and COL9A3 and differential frequencies in ancestral alleles in hearing impairment genes among individuals from Cameroon.

There is scarcity of known gene variants of hearing impairment (HI) in African populations. This knowledge deficit is ultimately affecting the development of genetic diagnoses. We used whole exome sequencing to investigate gene variants, pathways of interactive genes and the fractions of ancestral overderived alleles for 159 HI genes among 18 Cameroonian patients with non-syndromic HI (NSHI) and 129 ethnically matched controls. Pathogenic and likely pathogenic (PLP) variants were found in MYO3A, MYO15A and COL9A3, with a resolution rate of 50% (9/18 patients). The study identified significant genetic differentiation in novel population-specific gene variants at FOXD4L2, DHRS2L6, RPL3L and VTN between HI patients and controls. These gene variants are found in functional/co-expressed interactive networks with other known HI-associated genes and in the same pathways with VTN being a hub protein, that is, focal adhesion pathway and regulation of the actin cytoskeleton (P-values <0.05). The results suggest that these novel population-specific gene variants are possible modifiers of the HI phenotypes. We found a high proportion of ancestral allele versus derived at low HI patients-specific minor allele frequency in the range of 0.0-0.1. The results showed a relatively low pickup rate of PLP variants in known genes in this group of Cameroonian patients with NSHI. In addition, findings may signal an evolutionary enrichment of some variants of HI genes in patients, as the result of polygenic adaptation, and suggest the possibility of multigenic influence on the phenotype of congenital HI, which deserves further investigations.

Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease.

Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the gene expression on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer's disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. For instance, here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer's genetic risk factors in the estimation of regional brain connectivity alterations.

Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine.

Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.