The genome revolution and its role in understanding complex diseases

Genomics and multiomics in the age of precision medicine

Precision medicine is a transformative healthcare model that utilizes an understanding of a person's genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in healthcare, and accrue health cost-benefits both at an individual and population level. Through faster and cost-effective genomics data, next-generation sequencing has provided us the impetus to understand the nuances of complex interactions between genes, diet, and lifestyle that are heterogeneous across the population. The emergence of multiomics technologies, including transcriptomics, proteomics, epigenomics, metabolomics, and microbiomics, has enhanced the knowledge necessary for maximizing the applicability of genomics data for better health outcomes. Integrative multiomics, the combination of multiple 'omics' data layered over each other, including the interconnections and interactions between them, helps us understand human health and disease better than any of them separately. Integration of these multiomics data is possible today with the phenomenal advancements in bioinformatics, data sciences, and artificial intelligence. Our review presents a broad perspective on the utility and feasibility of a genomics-first approach layered with other omics data, offering a practical model for adopting an integrated multiomics approach in pediatric health care and research. IMPACT: Precision medicine provides a paradigm shift from a conventional, reactive disease control approach to proactive disease prevention and health preservation. Phenomenal advancements in bioinformatics, data sciences, and artificial intelligence have made integrative multiomics feasible and help us understand human health and disease better than any of them separately. The genotype-first approach or reverse phenotyping has the potential to overcome the limitations of the phenotype-first approach by identifying new genotype-phenotype associations, enhancing the subclassification of diseases by widening the phenotypic spectrum of genetic variants, and understanding functional mechanisms of genetic variations.

Genome-wide association testing beyond SNPs

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Predictive Capabilities of Polygenic Scores in an East-Asian Population-based Cohort: The Singapore Chinese Health Study

Background

Existing polygenic scores (PGS) are derived primarily from studies performed in European populations. It is still unclear how these perform in improving risk predictions in East-Asians.

Methods

We generated 2,173 PGSs from 519 traits and assessed their associations with 58 baseline phenotypes in the Singapore Chinese Health Study (SCHS), a prospective cohort of 23,622 middle-aged and older Chinese residing in Singapore. We used linear regression to evaluate PGS performances for quantitative traits by calculating the explained variance (r²). For dichotomized phenotypes, we employed logistic regression to estimate the area under the receiver operating characteristic curve (AUC) in predictive models.

Results

Overall, traits with higher heritability scores exhibited stronger associations with PGSs, while behavioural traits, for example sleep duration and hours spent watching TV, showed weaker associations. Height and type 2 diabetes (T2D) exhibited the largest SNP-based heritability estimates with the largest increments in explained variance and AUC, respectively, compared to phenotypic models. We explored the effect of T2D risk factors on the association between the T2D PGS (PGS003444) and incident T2D. The PGS association was significantly mediated and modified by hypertension ( P indirect =1.56×10 -18 , P interaction =1.11×10 -6 ) and body mass index (BMI, P indirect =1.25×10 -36 , P interaction =2.10×10 -3 ). The prediction ability of PGS003444 for incident T2D was stronger was stronger among individuals who were non-overweight without hypertension (AUC=0.774) than in overweight individuals with hypertension (AUC=0.709).

Conclusions

In conclusion, our study demonstrated the divergent ability of PGSs in predictions of complex traits, and showed that for certain traits, such as T2D, PGSs may have the potential for improving risk prediction and personalized healthcare.

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

Multiple sclerosis: Exploring the limits and implications of genetic and environmental susceptibility

OBJECTIVE

To explore and describe the basis and implications of genetic and environmental susceptibility to multiple sclerosis (MS) using the Canadian population-based data.

BACKGROUND

Certain parameters of MS-epidemiology are directly observable (e.g., the recurrence-risk of MS in siblings and twins, the proportion of women among MS patients, the population-prevalence of MS, and the time-dependent changes in the sex-ratio). By contrast, other parameters can only be inferred from the observed parameters (e.g., the proportion of the population that is "genetically susceptible", the proportion of women among susceptible individuals, the probability that a susceptible individual will experience an environment "sufficient" to cause MS, and if they do, the probability that they will develop the disease).

DESIGN/METHODS

The "genetically susceptible" subset (G) of the population (Z) is defined to include everyone with any non-zero life-time chance of developing MS under some environmental conditions. The value for each observed and non-observed epidemiological parameter is assigned a "plausible" range. Using both a Cross-sectional Model and a Longitudinal Model, together with established parameter relationships, we explore, iteratively, trillions of potential parameter combinations and determine those combinations (i.e., solutions) that fall within the acceptable range for both the observed and non-observed parameters.

RESULTS

Both Models and all analyses intersect and converge to demonstrate that probability of genetic-susceptibitly, P(G), is limited to only a fraction of the population {i.e., P(G) ≤ 0.52)} and an even smaller fraction of women {i.e., P(G│F) < 0.32)}. Consequently, most individuals (particularly women) have no chance whatsoever of developing MS, regardless of their environmental exposure. However, for any susceptible individual to develop MS, requires that they also experience a "sufficient" environment. We use the Canadian data to derive, separately, the exponential response-curves for men and women that relate the increasing likelihood of developing MS to an increasing probability that a susceptible individual experiences an environment "sufficient" to cause MS. As the probability of a "sufficient" exposure increases, we define, separately, the limiting probability of developing MS in men (c) and women (d). These Canadian data strongly suggest that: (c < d ≤ 1). If so, this observation establishes both that there must be a "truly" random factor involved in MS pathogenesis and that it is this difference, rather than any difference in genetic or environmental factors, which primarily accounts for the penetrance difference between women and men.

CONCLUSIONS

The development of MS (in an individual) requires both that they have an appropriate genotype (which is uncommon in the population) and that they have an environmental exposure "sufficient" to cause MS given their genotype. Nevertheless, the two principal findings of this study are that: P(G) ≤ 0.52)} and: (c < d ≤ 1). Threfore, even when the necessary genetic and environmental factors, "sufficient" for MS pathogenesis, co-occur for an individual, they still may or may not develop MS. Consequently, disease pathogenesis, even in this circumstance, seems to involve an important element of chance. Moreover, the conclusion that the macroscopic process of disease development for MS includes a "truly" random element, if replicated (either for MS or for other complex diseases), provides empiric evidence that our universe is non-deterministic.

Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs

Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways-disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.

Genetic susceptibility to multiple sclerosis in African Americans

Objective

To explore the nature of genetic-susceptibility to multiple sclerosis (MS) in African-Americans.

Background

Recently, the number of genetic-associations with MS has exploded although the MS-associations of specific haplotypes within the major histocompatibility complex (MHC) have been known for decades. For example, the haplotypes HLA-DRB1*15:01~HLA-DQB1*06:02, and HLA-DRB1*03:01~ HLA-DQB1*02:01 have odds ratios (ORs) for an MS-association orders of magnitude stronger than many of these newly-discovered associations. Nevertheless, all these haplotypes are part of much larger conserved extended haplotypes (CEHs), which span both the Class I and Class II MHC regions. African-Americans are at greater risk of developing MS compared to a native Africans but at lesser risk compared to Europeans. It is the purpose of this manuscript to explore the relationship between MS-susceptibility and the CEH make-up of our African-American cohort.

Design/methods

The African-American (AA) cohort consisted of 1,305 patients with MS and 1,155 controls, who self-identified as being African-American. For comparison, we used the 18,492 controls and 11,144 MS-cases from the predominantly European Wellcome Trust Case Control Consortium (WTCCC) and the 28,557 phased native Africans from the multinational “Be the Match” registry. The WTCCC and the African-Americans were phased at each of five HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRB1 and HLA-DQB1) and the at 11 SNPs (10 of which were in non-coding regions) surrounding the Class II region of the DRB1 gene using previously-published probabilistic phasing algorithms.

Results

Of the 32 most frequent CEHs, 18 (56%) occurred either more frequently or exclusively in Africans) whereas 9 (28%) occurred more frequently or exclusively in Europeans. The remaining 5 CEHs occurred in neither control group although, likely, these were African in origin. Eight of these CEHs carried the DRB1*15:03~DQB1*06:02~a36 haplotype and three carried the DRB1*15:01~DQB1*06:02~a1 haplotype. In African Americans, a single-copy of the European CEH (03:01_07:02_07:02_15:01_06:02_a1) was associated with considerable MS-risk (OR = 3.30; p = 0.0001)–similar to that observed in the WTCCC (OR = 3.25; p<10⁻¹⁶⁸). By contrast, the MS-risk for the European CEH (02:01_07:02_07:02_15:01_06:02_a1) was less (OR = 1.49; ns)–again, similar to the WTCCC (OR = 2.2; p<10⁻³⁸). Moreover, four African haplotypes were “protective” relative to a neutral reference, to three European CEHs, and also to the five other African CEHs.

Conclusions

The common CEHs in African Americans are divisible into those that are either African or European in origin, which are derived without modification from their source population. European CEHs, linked to MS-risk, in general, had similar impacts in African-Americans as they did in Europeans. By contrast, African CEHs had mixed MS-risks. For a few, the MS-risk exceeded that in a neutral-reference group whereas, for many others, these CEHs were “protective”–perhaps providing a partial rationale for the lower MS-risk in African-Americans compared to European-Americans.

Structural and functional analysis of disease-associated mutations in GOT1 gene: An in silico study

Disease-associated single nucleotide polymorphisms (SNPs) alter the natural functioning and the structure of proteins. Glutamic-oxaloacetic transaminase 1 (GOT1) is a gene associated with multiple cancers and neurodegenerative diseases which codes for aspartate aminotransferase. The present study involved a comprehensive in-silico analysis of the disease-associated SNPs of human GOT1. Four highly deleterious nsSNPs (L36R, Y159C, W162C and L345P) were identified through SNP screening using several sequence-based and structure-based tools. Conservation analysis and oncogenic analysis showed that most of the nsSNPs are at highly conserved residues, oncogenic in nature and cancer drivers. Molecular dynamics simulations (MDS) analysis was performed to understand the dynamic behaviour of native and mutant proteins. PTM analysis revealed that the nsSNP Y159C is at a PTM site and will mostly affect phosphorylation at that site. Based on the overall analyses carried out in this study, L36R is the most deleterious mutation amongst the aforementioned deleterious mutations of GOT1.

Genetic susceptibility to multiple sclerosis: interactions between conserved extended haplotypes of the MHC and other susceptibility regions

BACKGROUND

To study the accumulation of MS-risk resulting from different combinations of MS-associated conserved-extended-haplotypes (CEHs) of the MHC and three non-MHC "risk-haplotypes" nearby genes EOMES, ZFP36L1, and CLEC16A. Many haplotypes are MS-associated despite having population-frequencies exceeding the percentage of genetically-susceptible individuals. The basis of this frequency-disparity requires explanation.

METHODS

The SNP-data from the WTCCC was phased at the MHC and three non-MHC susceptibility-regions. CEHs at the MHC were classified into five haplotype-groups: (HLA-DRB1*15:01 ~ DQB1*06:02 ~ a1)-containing (H +); extended-risk (ER); all-protective (AP); neutral (0); and the single-CEH (c1). MS-associations for different "risk-combinations" at the MHC and other non-MHC "risk-loci" and the appropriateness of additive and multiplicative risk-accumulation models were assessed.

RESULTS

Different combinations of "risk-haplotypes" produce a final MS-risk closer to additive rather than multiplicative risk-models but neither model was consistent. Thus, (H +)-haplotypes had greater impact when combined with (0)-haplotypes than with (H +)-haplotypes, whereas, (H +)-haplotypes had greater impact when combined with a (c1)-haplotypes than with (0)-haplotypes. Similarly, risk-genotypes (0,H +), (c1,H +), (H + ,H +) and (0,c1) were additive with risks from non-MHC risk-loci, whereas risk-genotypes (ER,H +) and (AP,c1) were unaffected.

CONCLUSIONS

Genetic-susceptibility to MS is essential for MS to develop but actually developing MS depends heavily upon both an individual's particular combination of "risk-haplotypes" and how these loci interact.

Minireview: Novel Micropeptide Discovery by Proteomics and Deep Sequencing Methods

A novel class of small proteins, called micropeptides, has recently been discovered in the genome. These proteins, which have been found to play important roles in many physiological and cellular systems, are shorter than 100 amino acids and were overlooked during previous genome annotations. Discovery and characterization of more micropeptides has been ongoing, often using -omics methods such as proteomics, RNA sequencing, and ribosome profiling. In this review, we survey the recent advances in the micropeptides field and describe the methodological and conceptual challenges facing future micropeptide endeavors.

The nature of genetic and environmental susceptibility to multiple sclerosis

OBJECTIVE

To understand the nature of genetic and environmental susceptibility to multiple sclerosis (MS) and, by extension, susceptibility to other complex genetic diseases.

BACKGROUND

Certain basic epidemiological parameters of MS (e.g., population-prevalence of MS, recurrence-risks for MS in siblings and twins, proportion of women among MS patients, and the time-dependent changes in the sex-ratio) are well-established. In addition, more than 233 genetic-loci have now been identified as being unequivocally MS-associated, including 32 loci within the major histocompatibility complex (MHC), and one locus on the X chromosome. Despite this recent explosion in genetic associations, however, the association of MS with the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 (H+) haplotype has been known for decades.

DESIGN/METHODS

We define the "genetically-susceptible" subset (G) to include everyone with any non-zero life-time chance of developing MS. Individuals who have no chance of developing MS, regardless of their environmental experiences, belong to the mutually exclusive "non-susceptible" subset (G-). Using these well-established epidemiological parameters, we analyze, mathematically, the implications that these observations have regarding the genetic-susceptibility to MS. In addition, we use the sex-ratio change (observed over a 35-year interval in Canada), to derive the relationship between MS-probability and an increasing likelihood of a sufficient environmental exposure.

RESULTS

We demonstrate that genetic-susceptibitly is confined to less than 7.3% of populations throughout Europe and North America. Consequently, more than 92.7% of individuals in these populations have no chance whatsoever of developing MS, regardless of their environmental experiences. Even among carriers of the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype, far fewer than 32% can possibly be members the (G) subset. Also, despite the current preponderance of women among MS patients, women are less likely to be in the susceptible (G) subset and have a higher environmental threshold for developing MS compared to men. Nevertheless, the penetrance of MS in susceptible women is considerably greater than it is in men. Moreover, the response-curves for MS-probability in susceptible individuals increases with an increasing likelihood of a sufficient environmental exposure, especially among women. However, these environmental response-curves plateau at under 50% for women and at a significantly lower level for men.

CONCLUSIONS

The pathogenesis of MS requires both a genetic predisposition and a suitable environmental exposure. Nevertheless, genetic-susceptibility is rare in the population (< 7.3%) and requires specific combinations of non-additive genetic risk-factors. For example, only a minority of carriers of the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype are even in the (G) subset and, thus, genetic-susceptibility to MS in these carriers must result from the combined effect this haplotype together with the effects of certain other (as yet, unidentified) genetic factors. By itself, this haplotype poses no MS-risk. By contrast, a sufficient environmental exposure (however many events are involved, whenever these events need to act, and whatever these events might be) is common, currently occurring in, at least, 76% of susceptible individuals. In addition, the fact that environmental response-curves plateau well below 50% (especially in men), indicates that disease pathogenesis is partly stochastic. By extension, other diseases, for which monozygotic-twin recurrence-risks greatly exceed the disease-prevalence (e.g., rheumatoid arthritis, diabetes, and celiac disease), must have a similar genetic basis.

Integrated Network and Gene Ontology Analysis Identifies Key Genes and Pathways for Coronary Artery Diseases

BACKGROUND

The prevalence of Coronary Artery Disease (CAD) in developing countries is on the rise, owing to rapidly changing lifestyle. Therefore, it is imperative that the underlying genetic and molecular mechanisms be understood to develop specific treatment strategies. Comprehensive disease network and Gene Ontology (GO) studies aid in prioritizing potential candidate genes for CAD and also give insights into gene function by establishing gene and disease pathway relationships.

METHODS

In the present study, CAD-associated genes were collated from different data sources and protein-protein interaction network was constructed using STRING. Highly interconnected network clusters were inferred and GO analysis was performed.

RESULTS

Interrelation between genes and pathways were analyzed on ClueGO and 38 candidates were identified from 1475 CAD-associated genes, which were significantly enriched in CAD-related pathways such as metabolism and regulation of lipid molecules, platelet activation, macrophage derived foam cell differentiation, and blood coagulation and fibrin clot formation.

DISCUSSION

Integrated network and ontology analysis enables biomarker prioritization for common complex diseases such as CAD. Experimental validation and future studies on the prioritized genes may reveal valuable insights into CAD development mechanism and targeted treatment strategies.

Evaluation of Male Fertility-Associated Loci in a European Population of Patients with Severe Spermatogenic Impairment

Infertility is a growing concern in developed societies. Two extreme phenotypes of male infertility are non-obstructive azoospermia (NOA) and severe oligospermia (SO), which are characterized by severe spermatogenic failure (SpF). We designed a genetic association study comprising 725 Iberian infertile men as a consequence of SpF and 1058 unaffected controls to evaluate whether five single-nucleotide polymorphisms (SNPs), previously associated with reduced fertility in Hutterites, are also involved in the genetic susceptibility to idiopathic SpF and specific clinical entities. A significant difference in the allele frequencies of USP8-rs7174015 was observed under the recessive model between the NOA group and both the control group (p = 0.0226, OR = 1.33) and the SO group (p = 0.0048, OR = 1.78). Other genetic associations for EPSTI1-rs12870438 and PSAT1-rs7867029 with SO and between TUSC1-rs10966811 and testicular sperm extraction (TESE) success in the context of NOA were observed. In silico analysis of functional annotations demonstrated cis-eQTL effects of such SNPs likely due to the modification of binding motif sites for relevant transcription factors of the spermatogenic process. The findings reported here shed light on the molecular mechanisms leading to severe phenotypes of idiopathic male infertility, and may help to better understand the contribution of the common genetic variation to the development of these conditions.

The Biological Significance of Multi-copy Regions and Their Impact on Variant Discovery

Identification of genetic variants via high-throughput sequencing (HTS) technologies has been essential for both fundamental and clinical studies. However, to what extent the genome sequence composition affects variant calling remains unclear. In this study, we identified 63,897 multi-copy sequences (MCSs) with a minimum length of 300 bp, each of which occurs at least twice in the human genome. The 151,749 genomic loci (multi-copy regions, or MCRs) harboring these MCSs account for 1.98% of the genome and are distributed unevenly across chromosomes. MCRs containing the same MCS tend to be located on the same chromosome. Gene Ontology (GO) analyses revealed that 3800 genes whose UTRs or exons overlap with MCRs are enriched for Golgi-related cellular component terms and various enzymatic activities in the GO biological function category. MCRs are also enriched for loci that are sensitive to neocarzinostatin-induced double-strand breaks. Moreover, genetic variants discovered by genome-wide association studies and recorded in dbSNP are significantly underrepresented in MCRs. Using simulated HTS datasets, we show that false variant discovery rates are significantly higher in MCRs than in other genomic regions. These results suggest that extra caution must be taken when identifying genetic variants in the MCRs via HTS technologies.

Determining population stratification and subgroup effects in association studies of rare genetic variants for nicotine dependence

Supplemental Digital Content is available in the text.

Background

Rare variants (minor allele frequency < 1% or 5 %) can help researchers to deal with the confounding issue of ‘missing heritability’ and have a proven role in dissecting the etiology for human diseases and complex traits.

Methods

We extended the combined multivariate and collapsing (CMC) and weighted sum statistic (WSS) methods and accounted for the effects of population stratification and subgroup effects using stratified analyses by the principal component analysis, named here as ‘str-CMC’ and ‘str-WSS’. To evaluate the validity of the extended methods, we analyzed the Genetic Architecture of Smoking and Smoking Cessation database, which includes African Americans and European Americans genotyped on Illumina Human Omni2.5, and we compared the results with those obtained with the sequence kernel association test (SKAT) and its modification, SKAT-O that included population stratification and subgroup effect as covariates. We utilized the Cochran–Mantel–Haenszel test to check for possible differences in single nucleotide polymorphism allele frequency between subgroups within a gene. We aimed to detect rare variants and considered population stratification and subgroup effects in the genomic region containing 39 acetylcholine receptor-related genes.

Results

The Cochran–Mantel–Haenszel test as applied to GABRG2 (P = 0.001) was significant. However, GABRG2 was detected both by str-CMC (P= 8.04E-06) and str-WSS (P= 0.046) in African Americans but not by SKAT or SKAT-O.

Conclusions

Our results imply that if associated rare variants are only specific to a subgroup, a stratified analysis might be a better approach than a combined analysis.

Genetic Landscape of Nonobstructive Azoospermia and New Perspectives for the Clinic

Nonobstructive azoospermia (NOA) represents the most severe expression of male infertility, involving around 1% of the male population and 10% of infertile men. This condition is characterised by the inability of the testis to produce sperm cells, and it is considered to have an important genetic component. During the last two decades, different genetic anomalies, including microdeletions of the Y chromosome, karyotype defects, and missense mutations in genes involved in the reproductive function, have been described as the primary cause of NOA in many infertile men. However, these alterations only explain around 25% of azoospermic cases, with the remaining patients showing an idiopathic origin. Recent studies clearly suggest that the so-called idiopathic NOA has a complex aetiology with a polygenic inheritance, which may alter the spermatogenic process. Although we are far from a complete understanding of the molecular mechanisms underlying NOA, the use of the new technologies for genetic analysis has enabled a considerable increase in knowledge during the last years. In this review, we will provide a comprehensive and updated overview of the genetic basis of NOA, with a special focus on the possible application of the recent insights in clinical practice.

Oil induces chlorophyll deficient propagules in mangroves

In Australia, some trees of the mangrove, Avicennia marina, growing in a chronic oil polluted site, produce chlorophyll deficient (albino) propagules. We tested the hypothesis that albinism was due to an oil-induced mutant allele that controls photosynthesis. We determined whether there are genetic differences between normal and chlorophyll deficient propagules. Four gene regions (nuclear 18S-26S cistron; chloroplast - trnH-psbA, rsp16 and matK) were sequenced and analysed for normal and albino propagules. Mutations occurred in both nuclear (ITS) and coding chloroplast (matK) genes of albino propagules. There were 10 mutational differences between normal and albino propagules in the matK samples. Analysis of molecular variation (AMOVA) of the matK dataset indicated highly significant genetic differentiation between normal and albino propagules. Our study suggests for the first time that PAHs from a chronic oil polluted site resulted in mutations in both nuclear and chloroplast genes, resulting in the production of albino propagules.

Understanding the molecular interaction of human argonaute-2 and miR-20a complex: A molecular dynamics approach

Argonaute-2 (AGO2), a member of the Argonaute family, is the only member possessing catalytic and RNA silencing activity. In here, a molecular dynamics (MDs) simulation was performed using the crystal structure of human AGO2 protein complex with miR-20a. miR-20a is involved with various kind of biological process like heart and lung development, oncogenic process, etc. In precise, MD simulation was carried out with AGO2 protein complex with wild type, two mutant sites and four mutant sites in guided microRNA (miRNA). It has been noted that root-mean-square deviation (RMSD) of atomic positions of nucleic acid for wild type and two mutant sites guided miRNA has the same pattern of fluctuations, which stabilizes around 0.27 nm after 2 ns. Cα atom of AGO2 protein in the complex shows that this complex with wild type and two mutant site mutation duplex has a stable RMSD value after 20 ns, ranging between 0.14 and 0.21 nm. From the root-mean-square fluctuation (RMSF), we observed an increased pattern of fluctuations for the atoms of four mutant complex of AGO2-miR-20a complex. This increased RMSF of non-mutated nucleic acids is contributed by U-A bond breaking at the site of the nucleotide of U2 of guided miRNA, as observed from the duplex structure taken at different time steps of the simulation. Superimposed structure of the miRNA-mRNA duplex for the three complexes depicts that the three miRNA-mRNA duplexes are stable during the simulation. Current work demonstrates the possible correlations between the conformational changes of this AGO2-miR-20a duplex structure and the interactions of different atoms.

A Single Human Neuron Approach to Synapse Function

To understand human neuronal function, it is crucial to obtain knowledge of how human synapses operate. New approaches are necessary to define the unique properties of human synapses. Recently, new culturing approaches have been developed to obtain cultures of single human neurons for the first time (Rhee et al., Cell Rep. 2019, Meijer et al., Cell Rep. 2019, and Fenske et al., Sci. Rep. 2019).

Genome-Wide Association Study (GWAS) on Bilirubin Concentrations in Subjects with Metabolic Syndrome: Sex-Specific GWAS Analysis and Gene-Diet Interactions in a Mediterranean Population

Although, for decades, increased serum bilirubin concentrations were considered a threatening sign of underlying liver disease and had been associated with neonatal jaundice, data from recent years show that bilirubin is a powerful antioxidant and suggest that slightly increased serum bilirubin concentrations are protective against oxidative stress-related diseases, such as cardiovascular diseases. Therefore, a better understanding of the gene-diet interactions in determining serum bilirubin concentrations is needed. None of the previous genome-wide association studies (GWAS) on bilirubin concentrations has been stratified by sex. Therefore, considering the increasing interest in incorporating the gender perspective into nutritional genomics, our main aim was to carry out a GWAS on total serum bilirubin concentrations in a Mediterranean population with metabolic syndrome, stratified by sex. Our secondary aim was to explore, as a pilot study, the presence of gene-diet interactions at the GWAS level. We included 430 participants (188 men and 242 women, aged 55–75 years, and with metabolic syndrome) in the PREDIMED Plus-Valencia study. Global and sex-specific GWAS were undertaken to analyze associations and gene-diet interaction on total serum bilirubin. Adherence (low and high) to the Mediterranean diet (MedDiet) was analyzed as the dietary modulator. In the GWAS, we detected more than 55 SNPs associated with serum bilirubin at p < 5 × 10⁻⁸ (GWAS level). The top-ranked were four SNPs (rs4148325 (p = 9.25 × 10⁻²⁴), rs4148324 (p = 9.48 × 10⁻²⁴), rs6742078 (p = 1.29 × 10⁻²³), rs887829 (p = 1.39 × 10⁻²³), and the rs4148324 (p = 9.48 × 10⁻²⁴)) in the UGT1A1 (UDP glucuronosyltransferase family 1 member A1) gene, which replicated previous findings revealing the UGT1A1 as the major locus. In the sex-specific GWAS, the top-ranked SNPs at the GWAS level were similar in men and women (the lead SNP was the rs4148324-UGT1A1 in both men (p = 4.77 × 10⁻¹¹) and women (p = 2.15 × 10⁻¹⁴), which shows homogeneous genetic results for the major locus. There was more sex-specific heterogeneity for other minor genes associated at the suggestive level of GWAS significance (p < 1 × 10⁻⁵). We did not detect any gene-MedDiet interaction at p < 1 × 10⁻⁵ for the major genetic locus, but we detected some gene-MedDiet interactions with other genes at p < 1 × 10⁻⁵, and even at the GWAS level for the IL17B gene (p = 3.14 × 10⁻⁸). These interaction results, however, should be interpreted with caution due to our small sample size. In conclusion, our study provides new data, with a gender perspective, on genes associated with total serum bilirubin concentrations in men and women, and suggests possible additional modulations by adherence to MedDiet.

Lifestyle Medicine: A Brief Review of Its Dramatic Impact on Health and Survival

By ignoring the root causes of disease and neglecting to prioritize lifestyle measures for prevention, the medical community is placing people at harm. Advanced nations, influenced by a Western lifestyle, are in the midst of a health crisis, resulting largely from poor lifestyle choices. Epidemiologic, ecologic, and interventional studies have repeatedly indicated that most chronic illnesses, including cardiovascular disease, cancer, and type 2 diabetes, are the result of lifestyles fueled by poor nutrition and physical inactivity.In this article, we describe the practice of lifestyle medicine and its powerful effect on these modern instigators of premature disability and death. We address the economic benefits of prevention-based lifestyle medicine and its effect on our health care system: A system on the verge of bankruptcy. We recommend vital changes to a disastrous course. Many deaths and many causes of pain, suffering, and disability could be circumvented if the medical community could effectively implement and share the power of healthy lifestyle choices. We believe that lifestyle medicine should become the primary approach to the management of chronic conditions and, more importantly, their prevention. For future generations, for our own health, and for the Hippocratic Oath we swore to uphold ("First do no harm"), the medical community must take action. It is our hope that the information presented will inspire our colleagues to pursue lifestyle medicine research and incorporate such practices into their daily care of patients. The time to make this change is now.

The Collaborative African Genomics Network (CAfGEN): Applying Genomic technologies to probe host factors important to the progression of HIV and HIV-tuberculosis infection in sub-Saharan Africa

Background: Here, we describe how the Collaborative African Genomics Network ( CAfGEN) of the Human Heredity and Health in Africa (H3Africa) consortium is using genomics to probe host genetic factors important to the progression of HIV and HIV-tuberculosis (TB) coinfection in sub-Saharan Africa.   The H3Africa was conceived to facilitate the application of genomics technologies to improve health across Africa..          Methods: CAfGEN is an H3Africa collaborative centre comprising expertise from the University of Botswana; Makerere University; Baylor College of Medicine Children's Clinical Centers of Excellence (COEs) in Botswana, Uganda, and Swaziland; as well as Baylor College of Medicine, Texas. The COEs provide clinical expertise for community engagement, participant recruitment and sample collection while the three University settings facilitate processing and management of genomic samples and provide infrastructure and training opportunities to sustain genomics research. Results: The project has focused on utilizing whole-exome sequencing to identify genetic variants contributing to extreme HIV disease progression phenotypes in children, as well as RNA sequencing and integrated genomics to identify host genetic factors associated with TB disease progression among HIV-positive children. These cohorts, developed using the COEs' electronic medical records, are exceptionally well-phenotyped and present an unprecedented opportunity to assess genetic factors in individuals whose HIV was acquired by a different route than their adult counterparts in the context of a unique clinical course and disease pathophysiology. Conclusions: Our approach offers the prospect of developing a critical mass of well-trained, highly-skilled, continent-based African genomic scientists. To ensure long term genomics research sustainability in Africa, CAfGEN contributes to a wide range of genomics capacity and infrastructure development on the continent, has laid a foundation for genomics graduate programs at its institutions, and continues to actively promote genomics research through innovative forms of community engagement brokered by partnerships with governments and academia to support genomics policy formulation.

The Collaborative African Genomics Network (CAfGEN): Applying Genomic technologies to probe host factors important to the progression of HIV and HIV-tuberculosis infection in sub-Saharan Africa

Background: The Human Heredity and Health in Africa consortium (H3Africa) was conceived to facilitate the application of genomics technologies to improve health across Africa. Here, we describe how the Collaborative African Genomics Network (CAfGEN) of the H3Africa consortium is using genomics to probe host genetic factors important to the progression of HIV and HIV-tuberculosis (TB) coinfection in sub-Saharan Africa.          Methods: CAfGEN is an H3Africa collaborative centre comprising expertise from the University of Botswana; Makerere University; Baylor College of Medicine Children’s Clinical Centers of Excellence (COEs) in Botswana, Uganda, and Swaziland; as well as Baylor College of Medicine, Texas. The COEs provide clinical expertise for community engagement, participant recruitment and sample collection while the three University settings facilitate processing and management of genomic samples and provide infrastructure and training opportunities to sustain genomics research. Results: The project has focused on utilizing whole-exome sequencing to identify genetic variants contributing to extreme HIV disease progression phenotypes in children, as well as RNA sequencing and integrated genomics to identify host genetic factors associated with TB disease progression among HIV-positive children. These cohorts, developed using the COEs’ electronic medical records, are exceptionally well-phenotyped and present an unprecedented opportunity to assess genetic factors in individuals whose HIV was acquired by a different route than their adult counterparts in the context of a unique clinical course and disease pathophysiology. Conclusions: Our approach offers the prospect of developing a critical mass of well-trained, highly-skilled, continent-based African genomic scientists. To ensure long term genomics research sustainability in Africa, CAfGEN contributes to a wide range of genomics capacity and infrastructure development on the continent, has laid a foundation for genomics graduate programs at its institutions, and continues to actively promote genomics research through innovative forms of community engagement brokered by partnerships with governments and academia to support genomics policy formulation.

Highly conserved extended haplotypes of the major histocompatibility complex and their relationship to multiple sclerosis susceptibility

Objective

To determine the relationship between highly-conserved extended-haplotypes (CEHs) in the major histocompatibility complex (MHC) and MS-susceptibility.

Background

Among the ~200 MS-susceptibility regions, which are known from genome-wide analyses of single nucleotide polymorphisms (SNPs), the MHC accounts for roughly a third of the currently explained variance and the strongest MS-associations are for certain Class II alleles (e.g., HLA-DRB1*15:01; HLA-DRB1*03:01; and HLA-DRB1*13:03), which frequently reside on CEHs within the MHC.

Design/Methods

Autosomal SNPs (441,547) from 11,376 MS cases and 18,872 controls in the WTCCC dataset were phased. The most significant MS associated SNP haplotype was composed of 11 SNPs in the MHC Class II region surrounding the HLA-DRB1 gene. We also phased alleles at the HLA-A, HLA-C, HLA-B, HLA-DRB1, and HLA-DQB1 loci. This data was used to probe the relationship between CEHs and MS susceptibility.

Results

We phased a total of 59,884 extended haplotypes (HLA-A, HLA-C, HLA-B, HLA-DRB1, HLA-DQB1 and SNP haplotypes) from 29,942 individuals. Of these, 10,078 unique extended haplotypes were identified. The 10 most common CEHs accounted for 22% (13,302) of the total. By contrast, the 8,446 least common extended haplotypes also accounted for approximately 20% (12,298) of the total. This extreme frequency-disparity among extended haplotypes necessarily complicates interpretation of reported disease-associations with specific HLA alleles. In particular, the HLA motif HLA-DRB1*15:01~HLA-DQB1*06:02 is strongly associated with MS risk. Nevertheless, although this motif is almost always found on the a1 SNP haplotype, it can rarely be found on others (e.g., a27 and a36), and, in these cases, it seems to have no apparent disease-association (OR = 0.7; CI = 0.3–1.3 and OR = 0.7; CI = 0.2–2.2, respectively). Furthermore, single copy carriers of the a1 SNP-haplotype without this HLA motif still have an increased disease risk (OR = 2.2; CI = 1.2–3.8). In addition, even among the set of CEHs, which carry the Class II motif of HLA-DRB1*15:01~HLA-DQB1*06:02~a1, different CEHs have differing strengths in their MS-associations.

Conclusions

The MHC in diverse human populations consists, primarily, of a very small collection of very highly-selected CEHs. Our findings suggest that the MS-association with the HLA-DRB1*15:01~HLA-DQB1*06:02 haplotype may be due primarily to the combined attributes of the CEHs on which this particular HLA-motif often resides.

A non-threshold region-specific method for detecting rare variants in complex diseases

A region-specific method, NTR (non-threshold rare) variant detection method, was developed—it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases.

Yeast genetic interaction screen of human genes associated with amyotrophic lateral sclerosis: identification of MAP2K5 kinase as a potential drug target

To understand disease mechanisms, a large-scale analysis of human–yeast genetic interactions was performed. Of 1305 human disease genes assayed, 20 genes exhibited strong toxicity in yeast. Human–yeast genetic interactions were identified by en masse transformation of the human disease genes into a pool of 4653 homozygous diploid yeast deletion mutants with unique barcode sequences, followed by multiplexed barcode sequencing to identify yeast toxicity modifiers. Subsequent network analyses focusing on amyotrophic lateral sclerosis (ALS)-associated genes, such as optineurin (OPTN) and angiogenin (ANG), showed that the human orthologs of the yeast toxicity modifiers of these ALS genes are enriched for several biological processes, such as cell death, lipid metabolism, and molecular transport. When yeast genetic interaction partners held in common between human OPTN and ANG were validated in mammalian cells and zebrafish, MAP2K5 kinase emerged as a potential drug target for ALS therapy. The toxicity modifiers identified in this study may deepen our understanding of the pathogenic mechanisms of ALS and other devastating diseases.

Socioeconomic Status Interacts with the Genetic Effect of a Chromosome 9p21.3 Common Variant to Influence Coronary Artery Calcification and Incident Coronary Events in the Heinz Nixdorf Recall Study (Risk Factors, Evaluation of Coronary Calcium, and Lifestyle)

Background—

Genetic variants of a locus within the chromosome 9p21.3 region are consistently associated with coronary artery disease and coronary artery calcification (CAC). The aim of this study was to examine whether a 9p21.3 common variant interacts with socioeconomic status (SES) to influence CAC and incident coronary events in a population-based cohort.

Methods and Results—

9p21.3 single nucleotide polymorphism rs2891168 was genotyped in 4116 participants of the Heinz Nixdorf Recall study. SES indicators (education and income) and CAC were assessed at baseline. Incident coronary events were ascertained over a median follow-up of 9.3 years. Multiple regression models were fitted to estimate genetic effects on log e (CAC+1) and incident coronary events. Genetic effects were highest in the lower income tertile with a 53.1% (95% confidence interval, 30.6%–79.6%; P =1.8×10 – 7 ) increase in CAC and a hazard ratio of 1.44 (95% confidence interval, 1.01–2.07; P =0.049) for incident coronary events per additional risk allele. After including genotype×SES interaction terms in the regression models, genotype×income interactions were observed for CAC (exp[β g×income ]=0.85 [95% confidence interval, 0.74–0.98; P g×income =0.02] per 1000€/mo increase and additional risk allele) and for incident coronary events (hazard ratio g×income =0.69 [95% confidence interval, 0.48–0.98; P g×income =0.04] per 1000€/mo increase and additional risk allele). No interaction was observed using education as SES indicator.

Conclusions—

A 9p21.3 common variant seems to interact with SES to influence CAC and incident coronary events in a population-based cohort. This supports the hypothesis that better material, psychosocial, and lifestyle conditions enable higher SES groups to reduce the expression of their genetic susceptibility to coronary artery disease.

Big genomics and clinical data analytics strategies for precision cancer prognosis

The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others. In addition, we compared several multivariate prognostic models based on PSVM with classical machine learning techniques such as K-nearest-neighbor, support vector machine, random forest, neural networks and logistic regression. Our results revealed that negative log-rank p-values provides more robust weight values as opposed to the use of other quantities such as hazard ratios, fold change, or a combination of those factors. PSVM, together with the classical machine learning classifiers were combined in an ensemble (multi-test) voting system, which collectively provides a more precise and reproducible patient stratification. The use of the multi-test system approach, rather than the search for the ideal classification/prediction method, might help to address limitations of the individual classification algorithm in specific situation.

The nature of genetic susceptibility to multiple sclerosis: constraining the possibilities

BACKGROUND

Epidemiological observations regarding certain population-wide parameters (e.g., disease-prevalence, recurrence-risk in relatives, gender predilections, and the distribution of common genetic-variants) place important constraints on the possibilities for the genetic-basis underlying susceptibility to multiple sclerosis (MS).

METHODS

Using very broad range-estimates for the different population-wide epidemiological parameters, a mathematical model can help elucidate the nature and the magnitude of these constraints.

RESULTS

For MS no more than 8.5 % of the population can possibly be in the "genetically-susceptible" subset (defined as having a life-time MS-probability at least as high as the overall population average). Indeed, the expected MS-probability for this subset is more than 12 times that for every other person of the population who is not in this subset. Moreover, provided that those genetically susceptible persons (genotypes), who carry the well-established MS susceptibility allele (DRB1*1501), are equally or more likely to get MS than those susceptible persons, who don't carry this allele, then at least 84 % of MS-cases must come from this "genetically susceptible" subset. Finally, because men, compared to women, are at least as likely (and possibly more likely) to be susceptible, it can be demonstrated that women are more responsive to the environmental factors that are involved in MS-pathogenesis (whatever these are) and, thus, susceptible women are more likely actually to develop MS than susceptible men. Finally, in contrast to genetic susceptibility, more than 70 % of men (and likely also women) must have an environmental experience (including all of the necessary factors), which is sufficient to produce MS in a susceptible individual.

CONCLUSIONS

As a result, because of these constraints, it is possible to distinguish two classes of persons, indicating either that MS can be caused by two fundamentally different pathophysiological mechanisms or that the large majority of the population is at no risk of the developing this disease regardless of their environmental experience. Moreover, although environmental-factors would play a critical role in both mechanisms (if both exist), there is no reason to expect that these factors are the same (or even similar) between the two.

A multifaceted 'omics' approach for addressing the challenge of antimicrobial resistance

The inappropriate use of antibiotics has severe global health and economic consequences, including the emergence of antibiotic-resistant bacteria. A major driver of antibiotic misuse is the inability to accurately distinguish between bacterial and viral infections based on currently available diagnostic solutions. A multifaceted 'omics' approach that integrates personalized patient data such as genetic predisposition to infections (genomics), natural microbiota composition and immune response to infection (proteomics and transcriptomics) together with comprehensive pathogen profiling has the potential to help physicians improve their antimicrobial prescribing practices. In this respect, the EU has funded a multidisciplinary project (TAILORED-Treatment) that will develop novel omics-based personalized treatment schemes that have the potential to reduce antibiotic consumption, and help limiting the spread of antibiotic resistance.

The epidemiology of multiple sclerosis: insights to a causal cascade

MS-pathogenesis involves both genetic-susceptibility and environmental determinants. Three (or more) sequential environmental-factors are implicated. The first acts near birth, the second acts during childhood/adolescence, and the third acts subsequently. Two candidate factors (vitamin D deficiency and Epstein-Barr viral infection) seem particularly well-suited to the first two environmental-events but other factors (e.g., obesity and smoking behavior) seem also to be involved in the causal scheme. MS-pathogenesis can be modeled by incorporating both the environmental and genetic-factors into a causal scheme, which can then help to explain some of the changes in MS-epidemiology (e.g., increasing disease-prevalence, changing sex-ratio, and regional-variations in monozygotic-twin-concordance-rates), which have been taking place recently. This model suggests that genetic-susceptibility is overwhelmingly the most important determinant of MS and that, at least, 92.5% of individuals (and likely much more) are, essentially, incapable of developing MS, regardless of their specific environmental-exposures. Nevertheless, the genetics is complex and the contribution of any specific gene to MS-susceptibility seems to be quite modest. Thus, even for the DRB1*1501 allele (the strongest known MS-susceptibility marker), most carriers are not in the genetically-susceptible group. Moreover, 45-50% of individuals with MS lack this allele entirely and some of the haplotypes that carry this allele don't also confer any disease-risk. Finally, because the prevalence of genetic-susceptibility seems to be so similar throughout North America and Europe, and despite the crucial importance of a person's genetic make-up to disease pathogenesis, it is the environmental-factors, which largely responsible for the observed regional variations in disease-characteristics. Thus, despite MS being more common in women, men are more likely to be genetically-susceptible. This apparent paradox seems to relate to the fact that women are much more responsive than men to the recent changes in environmental-exposure (whatever these have been). These gender-differences may help to explain changes in the sex-ratio and the increasing disease-prevalence, which have both been observed recently. The potential importance of these conclusions regarding the role of environment in MS-pathogenesis is that they open the door to the possibility of pursuing strategies for primary primary disease prevention in the future.

Systems Genetic Validation of the SNP-Metabolite Association in Rice Via Metabolite-Pathway-Based Phenome-Wide Association Scans

In the post-GWAS (Genome-Wide Association Scan) era, the interpretation of GWAS results is crucial to screen for highly relevant phenotype-genotype association pairs. Based on the single genotype-phenotype association test and a pathway enrichment analysis, we propose a Metabolite-pathway-based Phenome-Wide Association Scan (M-PheWAS) to analyze the key metabolite-SNP pairs in rice and determine the regulatory relationship by assessing similarities in the changes of enzymes and downstream products in a pathway. Two SNPs, sf0315305925 and sf0315308337, were selected using this approach, and their molecular function and regulatory relationship with Enzyme EC:5.5.1.6 and with flavonoids, a significant downstream regulatory metabolite product, were demonstrated. Moreover, a total of 105 crucial SNPs were screened using M-PheWAS, which may be important for metabolite associations.

Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray

The decreasing cost of performing genome-wide association studies has made genomics widely accessible. However, there is a paucity of guidance for best practice in conducting such analyses. For the results of a study to be valid and replicable, multiple biases must be addressed in the course of data preparation and analysis. In addition, standardizing methods across small, independent studies would increase comparability and the potential for effective meta-analysis. This article provides a discussion of important aspects of quality control, imputation and analysis of genome-wide data from a low-coverage microarray, as well as a straight-forward guide to performing a genome-wide association study. A detailed protocol is provided online, with example scripts available at https://github.com/JoniColeman/gwas_scripts.

The ABCC6 Transporter as a Paradigm for Networking from an Orphan Disease to Complex Disorders

The knowledge on the genetic etiology of complex disorders largely results from the study of rare monogenic disorders. Often these common and rare diseases show phenotypic overlap, though monogenic diseases generally have a more extreme symptomatology. ABCC6, the gene responsible for pseudoxanthoma elasticum, an autosomal recessive ectopic mineralization disorder, can be considered a paradigm gene with relevance that reaches far beyond this enigmatic orphan disease. Indeed, common traits such as chronic kidney disease or cardiovascular disorders have been linked to the ABCC6 gene. While during the last decade the awareness of the wide ramifications of ABCC6 has increased significantly, the gene itself and the transmembrane transporter it encodes have not unveiled all of the mysteries that surround them. To gain more insights, multiple approaches are being used including next-generation sequencing, computational methods, and various "omics" technologies. Much effort is made to place the vast amount of data that is gathered in an integrated system-biological network; the involvement of ABCC6 in common disorders provides a good view on the wide implications and potential of such a network. In this review, we summarize the network approaches used to study ABCC6 and the role of this gene in several complex diseases.

Haplotype-based approach to known MS-associated regions increases the amount of explained risk

Genome-wide association studies (GWAS), using single nucleotide polymorphisms (SNPs), have yielded 110 non-human leucocyte antigen genomic regions that are associated with multiple sclerosis (MS). Despite this large number of associations, however, only 28% of MS-heritability can currently be explained. Here we compare the use of multi-SNP-haplotypes to the use of single-SNPs as alternative methods to describe MS genetic risk. SNP-haplotypes (of various lengths from 1 up to 15 contiguous SNPs) were constructed at each of the 110 previously identified, MS-associated, genomic regions. Even after correcting for the larger number of statistical comparisons made when using the haplotype-method, in 32 of the regions, the SNP-haplotype based model was markedly more significant than the single-SNP based model. By contrast, in no region was the single-SNP based model similarly more significant than the SNP-haplotype based model. Moreover, when we included the 932 MS-associated SNP-haplotypes (that we identified from 102 regions) as independent variables into a logistic linear model, the amount of MS-heritability, as assessed by Nagelkerke's R-squared, was 38%, which was considerably better than 29%, which was obtained by using only single-SNPs. This study demonstrates that SNP-haplotypes can be used to fine-map the genetic associations within regions of interest previously identified by single-SNP GWAS. Moreover, the amount of the MS genetic risk explained by the SNP-haplotype associations in the 110 MS-associated genomic regions was considerably greater when using SNP-haplotypes than when using single-SNPs. Also, the use of SNP-haplotypes can lead to the discovery of new regions of interest, which have not been identified by a single-SNP GWAS.