Molecular genetic epidemiology of human diseases: from patterns to predictions

Including diverse and admixed populations in genetic epidemiology research

The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.

Deep mutational scanning of the plasminogen activator inhibitor-1 functional landscape

The serine protease inhibitor (SERPIN) plasminogen activator inhibitor-1 (PAI-1) is a key regulator of the fibrinolytic system, inhibiting the serine proteases tissue- and urokinase-type plasminogen activator (tPA and uPA, respectively). Missense variants render PAI-1 non-functional through misfolding, leading to its turnover as a protease substrate, or to a more rapid transition to the latent/inactive state. Deep mutational scanning was performed to evaluate the impact of amino acid sequence variation on PAI-1 inhibition of uPA using an M13 filamentous phage display system. Error prone PCR was used to construct a mutagenized PAI-1 library encompassing ~ 70% of potential single amino acid substitutions. The relative effects of 27% of all possible missense variants on PAI-1 inhibition of uPA were determined using high-throughput DNA sequencing. 826 missense variants demonstrated conserved inhibitory activity while 1137 resulted in loss of PAI-1 inhibitory function. The least evolutionarily conserved regions of PAI-1 were also identified as being the most tolerant of missense mutations. The results of this screen confirm previous low-throughput mutational studies, including those of the reactive center loop. These data provide a powerful resource for explaining structure-function relationships for PAI-1 and for the interpretation of human genomic sequence variants.

IMHOTEP-a composite score integrating popular tools for predicting the functional consequences of non-synonymous sequence variants

The in silico prediction of the functional consequences of mutations is an important goal of human pathogenetics. However, bioinformatic tools that classify mutations according to their functionality employ different algorithms so that predictions may vary markedly between tools. We therefore integrated nine popular prediction tools (PolyPhen-2, SNPs&GO, MutPred, SIFT, MutationTaster2, Mutation Assessor and FATHMM as well as conservation-based Grantham Score and PhyloP) into a single predictor. The optimal combination of these tools was selected by means of a wide range of statistical modeling techniques, drawing upon 10 029 disease-causing single nucleotide variants (SNVs) from Human Gene Mutation Database and 10 002 putatively ‘benign’ non-synonymous SNVs from UCSC. Predictive performance was found to be markedly improved by model-based integration, whilst maximum predictive capability was obtained with either random forest, decision tree or logistic regression analysis. A combination of PolyPhen-2, SNPs&GO, MutPred, MutationTaster2 and FATHMM was found to perform as well as all tools combined. Comparison of our approach with other integrative approaches such as Condel, CoVEC, CAROL, CADD, MetaSVM and MetaLR using an independent validation dataset, revealed the superiority of our newly proposed integrative approach. An online implementation of this approach, IMHOTEP (‘Integrating Molecular Heuristics and Other Tools for Effect Prediction’), is provided at http://www.uni-kiel.de/medinfo/cgi-bin/predictor/.

Genomics of Immune Diseases and New Therapies

Genomic DNA sequencing technologies have been one of the great advances of the 21st century, having decreased in cost by seven orders of magnitude and opening up new fields of investigation throughout research and clinical medicine. Genomics coupled with biochemical investigation has allowed the molecular definition of a growing number of new genetic diseases that reveal new concepts of immune regulation. Also, defining the genetic pathogenesis of these diseases has led to improved diagnosis, prognosis, genetic counseling, and, most importantly, new therapies. We highlight the investigational journey from patient phenotype to treatment using the newly defined XMEN disease, caused by the genetic loss of the MAGT1 magnesium transporter, as an example. This disease illustrates how genomics yields new fundamental immunoregulatory insights as well as how research genomics is integrated into clinical immunology. At the end, we discuss two other recently described diseases, CHAI/LATAIE (CTLA-4 deficiency) and PASLI (PI3K dysregulation), as additional examples of the journey from unknown immunological diseases to new precision medicine treatments using genomics.

Pathway-driven discovery of epilepsy genes

Epilepsy genes deliver critical insights into the molecular control of brain synchronization and are revolutionizing our understanding and treatment of the disease. The epilepsy-associated genome is rapidly expanding, and two powerful complementary approaches, isolation of de novo exome variants in patients and targeted mutagenesis in model systems, account for the steep increase. In sheer number, the tally of genes linked to seizures will likely match that of cancer and exceed it in biological diversity. The proteins act within most intracellular compartments and span the molecular determinants of firing and wiring in the developing brain. Every facet of neurotransmission, from dendritic spine to exocytotic machinery, is in play, and defects of synaptic inhibition are over-represented. The contributions of somatic mutations and noncoding microRNAs are also being explored. The functional spectrum of established epilepsy genes and the arrival of rapid, precise technologies for genome editing now provide a robust scaffold to prioritize hypothesis-driven discovery and further populate this genetic proto-map. Although each gene identified offers translational potential to stratify patient care, the complexity of individual variation and covert actions of genetic modifiers may confound single-gene solutions for the clinical disorder. In vivo genetic deconstruction of epileptic networks, ex vivo validation of variant profiles in patient-derived induced pluripotent stem cells, in silico variant modeling and modifier gene discovery, now in their earliest stages, will help clarify individual patterns. Because seizures stand at the crossroads of all neuronal synchronization disorders in the developing and aging brain, the neurobiological analysis of epilepsy-associated genes provides an extraordinary gateway to new insights into higher cortical function.

Genes and Mutations Causing Autosomal Dominant Retinitis Pigmentosa

Retinitis pigmentosa (RP) has a prevalence of approximately one in 4000; 25%-30% of these cases are autosomal dominant retinitis pigmentosa (adRP). Like other forms of inherited retinal disease, adRP is exceptionally heterogeneous. Mutations in more than 25 genes are known to cause adRP, more than 1000 mutations have been reported in these genes, clinical findings are highly variable, and there is considerable overlap with other types of inherited disease. Currently, it is possible to detect disease-causing mutations in 50%-75% of adRP families in select populations. Genetic diagnosis of adRP has advantages over other forms of RP because segregation of disease in families is a useful tool for identifying and confirming potentially pathogenic variants, but there are disadvantages too. In addition to identifying the cause of disease in the remaining 25% of adRP families, a central challenge is reconciling clinical diagnosis, family history, and molecular findings in patients and families.

Living laboratory: whole-genome sequencing as a learning healthcare enterprise

With the proliferation of affordable large-scale human genomic data come profound and vexing questions about management of such data and their clinical uncertainty. These issues challenge the view that genomic research on human beings can (or should) be fully segregated from clinical genomics, either conceptually or practically. Here, we argue that the sharp distinction between clinical care and research is especially problematic in the context of large-scale genomic sequencing of people with suspected genetic conditions. Core goals of both enterprises (e.g. understanding genotype-phenotype relationships; generating an evidence base for genomic medicine) are more likely to be realized at a population scale if both those ordering and those undergoing sequencing for diagnostic reasons are routinely and longitudinally studied. Rather than relying on expensive and lengthy randomized clinical trials and meta-analyses, we propose leveraging nascent clinical-research hybrid frameworks into a broader, more permanent instantiation of exploratory medical sequencing. Such an investment could enlighten stakeholders about the real-life challenges posed by whole-genome sequencing, such as establishing the clinical actionability of genetic variants, returning 'off-target' results to families, developing effective service delivery models and monitoring long-term outcomes.