An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people

CACNA1C rs1006737, Threatening Life Events, and Gene-Environment Interaction Predict Major Depressive Disorder

INTRODUCTION

CACNA1C rs1006737 is a novel variant in discovery of replicable associations in major depressive disorder (MDD). However, there have been no specific studies considered effect of environmental pathogens to date examining its clinical significance. In this study we investigated the interaction effect between CACNA1C rs1006737 polymorphism and threatening life events (TLEs) in MDD and carried out a meta-analysis of published findings.

METHODS

A total of 1,177 consecutive participants were genotyped. Information on exposure to TLEs, socio-demographic data, and history of psychological problems among first-degree relatives was collected. MDD was diagnosed according to the Chinese version of the 24-item Hamilton Rating Scale for Depression.

RESULTS

There was a significant interaction effect between CACNA1C rs1006737 polymorphism and TLEs in MDD. A dose-response relationship was found between CACNA1C rs1006737 genotypes and TLEs in MDD. The results of the meta-analysis showed that CACNA1C rs1006737 genotypes interacted with TLEs in MDD.

CONCLUSION

CACNA1C rs1006737 genotype and previous exposure to TLEs interact to influence the risk of developing MDD. We propose that CACNA1C rs1006737 may represent a target for novel pharmacological therapies to prevent or treat MDD.

Genome-Wide Association Studies to Improve Wood Properties: Challenges and Prospects

Wood formation is an excellent model system for quantitative trait analysis due to the strong associations between the transcriptional and metabolic traits that contribute to this complex process. Investigating the genetic architecture and regulatory mechanisms underlying wood formation will enhance our understanding of the quantitative genetics and genomics of complex phenotypic variation. Genome-wide association studies (GWASs) represent an ideal statistical strategy for dissecting the genetic basis of complex quantitative traits. However, elucidating the molecular mechanisms underlying many favorable loci that contribute to wood formation and optimizing GWAS design remain challenging in this omics era. In this review, we summarize the recent progress in GWAS-based functional genomics of wood property traits in major timber species such as Eucalyptus, Populus, and various coniferous species. We discuss several appropriate experimental designs for extensive GWAS in a given undomesticated tree population, such as omics-wide association studies and high-throughput phenotyping technologies. We also explain why more attention should be paid to rare allelic and major structural variation. Finally, we explore the potential use of GWAS for the molecular breeding of trees. Such studies will help provide an integrated understanding of complex quantitative traits and should enable the molecular design of new cultivars.

Genetic Analysis of Rare Human Variants of Regulators of G Protein Signaling Proteins and Their Role in Human Physiology and Disease

Regulators of G protein signaling (RGS) proteins modulate the physiologic actions of many neurotransmitters, hormones, and other signaling molecules. Human RGS proteins comprise a family of 20 canonical proteins that bind directly to G protein-coupled receptors/G protein complexes to limit the lifetime of their signaling events, which regulate all aspects of cell and organ physiology. Genetic variations account for diverse human traits and individual predispositions to disease. RGS proteins contribute to many complex polygenic human traits and pathologies such as hypertension, atherosclerosis, schizophrenia, depression, addiction, cancers, and many others. Recent analysis indicates that most human diseases are due to extremely rare genetic variants. In this study, we summarize physiologic roles for RGS proteins and links to human diseases/traits and report rare variants found within each human RGS protein exome sequence derived from global population studies. Each RGS sequence is analyzed using recently described bioinformatics and proteomic tools for measures of missense tolerance ratio paired with combined annotation-dependent depletion scores, and protein post-translational modification (PTM) alignment cluster analysis. We highlight selected variants within the well-studied RGS domain that likely disrupt RGS protein functions and provide comprehensive variant and PTM data for each RGS protein for future study. We propose that rare variants in functionally sensitive regions of RGS proteins confer profound change-of-function phenotypes that may contribute, in newly appreciated ways, to complex human diseases and/or traits. This information provides investigators with a valuable database to explore variation in RGS protein function, and for targeting RGS proteins as future therapeutic targets.

Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk

Multiple sclerosis is a complex neurological disease, with ∼20% of risk heritability attributable to common genetic variants, including >230 identified by genome-wide association studies. Multiple strands of evidence suggest that much of the remaining heritability is also due to additive effects of common variants rather than epistasis between these variants or mutations exclusive to individual families. Here, we show in 68,379 cases and controls that up to 5% of this heritability is explained by low-frequency variation in gene coding sequence. We identify four novel genes driving MS risk independently of common-variant signals, highlighting key pathogenic roles for regulatory T cell homeostasis and regulation, IFNγ biology, and NFκB signaling. As low-frequency variants do not show substantial linkage disequilibrium with other variants, and as coding variants are more interpretable and experimentally tractable than non-coding variation, our discoveries constitute a rich resource for dissecting the pathobiology of MS.

Assessment of coding region variants in Kuwaiti population: implications for medical genetics and population genomics

Consanguineous populations of the Arabian Peninsula have been underrepresented in global efforts that catalogue human exome variability. We sequenced 291 whole exomes of unrelated, healthy native Arab individuals from Kuwait to a median coverage of 45X and characterised 170,508 single-nucleotide variants (SNVs), of which 21.7% were ‘personal’. Up to 12% of the SNVs were novel and 36% were population-specific. Half of the SNVs were rare and 54% were missense variants. The study complemented the Greater Middle East Variome by way of reporting many additional Arabian exome variants. The study corroborated Kuwaiti population genetic substructures previously derived using genome-wide genotype data and illustrated the genetic relatedness among Kuwaiti population subgroups, Middle Eastern, European and Ashkenazi Jewish populations. The study mapped 112 rare and frequent functional variants relating to pharmacogenomics and disorders (recessive and common) to the phenotypic characteristics of Arab population. Comparative allele frequency data and carrier distributions of known Arab mutations for 23 disorders seen among Arabs, of putative OMIM-listed causal mutations for 12 disorders observed among Arabs but not yet characterized for genetic basis in Arabs, and of 17 additional putative mutations for disorders characterized for genetic basis in Arab populations are presented for testing in future Arab studies.

Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions

PURPOSE

Biomedical databases combining electronic medical records and phenotypic and genomic data constitute a powerful resource for the personalization of treatment. To leverage the wealth of information provided, algorithms are required that systematically translate the contained information into treatment recommendations based on existing genotype-phenotype associations.

METHODS

We developed and tested algorithms for translation of preexisting genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations. We compared the results obtained by genome sequencing, exome sequencing, and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia.

RESULTS

Our most striking result was that the performance of genotyping arrays is similar to that of genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions. Interestingly, 99.8% of all assessed individuals had a genotype associated with increased risks to at least one medication, and thereby the implementation of pharmacogenetic recommendations based on genotyping affects at least 50 daily drug doses per 1000 inhabitants.

CONCLUSION

We find that microarrays are a cost-effective solution for creating preemptive pharmacogenetic reports, and with slight modifications, existing databases can be applied for automated pharmacogenetic decision support for clinicians.

Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance via mean imputed r2 at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts vs. single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1%) by 0.5-3.1% for an array of one million sites and 0.7-7.1% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

Project MinE: study design and pilot analyses of a large-scale whole-genome sequencing study in amyotrophic lateral sclerosis

The most recent genome-wide association study in amyotrophic lateral sclerosis (ALS) demonstrates a disproportionate contribution from low-frequency variants to genetic susceptibility to disease. We have therefore begun Project MinE, an international collaboration that seeks to analyze whole-genome sequence data of at least 15 000 ALS patients and 7500 controls. Here, we report on the design of Project MinE and pilot analyses of successfully sequenced 1169 ALS patients and 608 controls drawn from the Netherlands. As has become characteristic of sequencing studies, we find an abundance of rare genetic variation (minor allele frequency < 0.1%), the vast majority of which is absent in public datasets. Principal component analysis reveals local geographical clustering of these variants within The Netherlands. We use the whole-genome sequence data to explore the implications of poor geographical matching of cases and controls in a sequence-based disease study and to investigate how ancestry-matched, externally sequenced controls can induce false positive associations. Also, we have publicly released genome-wide minor allele counts in cases and controls, as well as results from genic burden tests.

Global genetic diversity of human apolipoproteins and effects on cardiovascular disease risk

Abnormal plasma apolipoprotein levels are consistently implicated in CVD risk. Although 30% to 60% of their interindividual variability is genetic, common genetic variants explain only 10% to 20% of these differences. Rare genetic variants may be major sources of the missing heritability, yet quantitative evaluations of their contribution to phenotypic variability are lacking. Here, we analyzed whole-genome and whole-exome sequencing data from 138,632 individuals across seven major human populations to present a systematic overview of genetic apolipoprotein variability. We provide population-specific frequencies of 38 clinically important apolipoprotein alleles and identify further 6,875 genetic variants, 33% of which are novel and 98.7% of which are rare with minor allele frequencies <1%. We predicted the functional impact of rare variants and found that their relative importance differed drastically between genes and among ethnicities. Importantly, we validated the clinical relevance of multiple variants with predicted effects by leveraging association data from the CARDIoGRAM (Coronary Artery Disease Genomewide Replication and Meta-analysis) and Global Lipids Genetics consortia. Overall, we provide a consolidated overview of population-specific apolipoprotein genetics as a valuable data resource for scientists and clinicians, estimate the importance of rare genetic variants for the missing heritability of apolipoprotein-associated disease traits, and pinpoint multiple novel apolipoprotein variants with putative population-specific impacts on serum lipid levels.

Genomic history of the Sardinian population

The population of the Mediterranean island of Sardinia has made important contributions to genome-wide association studies of complex disease traits and, based on ancient DNA (aDNA) studies of mainland Europe, Sardinia is hypothesized to be a unique refuge for early Neolithic ancestry. To provide new insights on the genetic history of this flagship population, we analyzed 3,514 whole-genome sequenced individuals from Sardinia. We find Sardinian samples show elevated levels of shared ancestry with Basque individuals, especially samples from the more historically isolated regions of Sardinia. Our analysis also uniquely illuminates how levels of genetic similarity with mainland aDNA samples varies subtly across the island. Together, our results indicate within-island sub-structure and sex-biased processes have substantially impacted the genetic history of Sardinia. These results give new insight to the demography of ancestral Sardinians and help further the understanding of sharing of disease risk alleles between Sardinia and mainland populations.

Extremely rare variants reveal patterns of germline mutation rate heterogeneity in humans

A detailed understanding of the genome-wide variability of single-nucleotide germline mutation rates is essential to studying human genome evolution. Here, we use ~36 million singleton variants from 3560 whole-genome sequences to infer fine-scale patterns of mutation rate heterogeneity. Mutability is jointly affected by adjacent nucleotide context and diverse genomic features of the surrounding region, including histone modifications, replication timing, and recombination rate, sometimes suggesting specific mutagenic mechanisms. Remarkably, GC content, DNase hypersensitivity, CpG islands, and H3K36 trimethylation are associated with both increased and decreased mutation rates depending on nucleotide context. We validate these estimated effects in an independent dataset of ~46,000 de novo mutations, and confirm our estimates are more accurate than previously published results based on ancestrally older variants without considering genomic features. Our results thus provide the most refined portrait to date of the factors contributing to genome-wide variability of the human germline mutation rate.

An optimized prediction framework to assess the functional impact of pharmacogenetic variants

Prediction of phenotypic consequences of mutations constitutes an important aspect of precision medicine. Current computational tools mostly rely on evolutionary conservation and have been calibrated on variants associated with disease, which poses conceptual problems for assessment of variants in poorly conserved pharmacogenes. Here, we evaluated the performance of 18 current functionality prediction methods leveraging experimental high-quality activity data from 337 variants in genes involved in drug metabolism and transport and found that these models only achieved probabilities of 0.1–50.6% to make informed conclusions. We therefore developed a functionality prediction framework optimized for pharmacogenetic assessments that significantly outperformed current algorithms. Our model achieved 93% for both sensitivity and specificity for both loss-of-function and functionally neutral variants, and we confirmed its superior performance using cross validation analyses. This novel model holds promise to improve the translation of personal genetic information into biological conclusions and pharmacogenetic recommendations, thereby facilitating the implementation of Next-Generation Sequencing data into clinical diagnostics.

Type 2 diabetes-associated variants of the MT2 melatonin receptor affect distinct modes of signaling

Melatonin is produced during the night and regulates sleep and circadian rhythms. Loss-of-function variants in MTNR1B, which encodes the melatonin receptor MT₂, a G protein-coupled receptor (GPCR), are associated with an increased risk of type 2 diabetes (T2D). To identify specific T2D-associated signaling pathway(s), we profiled the signaling output of 40 MT₂ variants by monitoring spontaneous (ligand-independent) and melatonin-induced activation of multiple signaling effectors. Genetic association analysis showed that defects in the melatonin-induced activation of Gα_i1 and Gα_z proteins and in spontaneous β-arrestin2 recruitment to MT₂ were the most statistically significantly associated with an increased T2D risk. Computational variant impact prediction by in silico evolutionary lineage analysis strongly correlated with the measured phenotypic effect of each variant, providing a predictive tool for future studies on GPCR variants. Together, this large-scale functional study provides an operational framework for the postgenomic analysis of the multiple GPCR variants present in the human population. The association of T2D risk with signaling pathway-specific defects opens avenues for pathway-specific personalized therapeutic intervention and reveals the potential relevance of MT₂ function during the day, when melatonin is undetectable, but spontaneous activity of the receptor occurs.

Functional annotation of genomic variants in studies of late-onset Alzheimer's disease

Motivation

Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied.

Results

In this work, we outline an annotation process motivated by the Alzheimer's Disease Sequencing Project, illustrate the impact of including tissue-specific transcript sets and sources of gene regulatory information and assess the potential impact of changing genomic builds on the annotation process. While these factors only impact a small proportion of total variant annotations (∼5%), they influence the potential analysis of a large fraction of genes (∼25%).

Availability and implementation

Individual variant annotations are available via the NIAGADS GenomicsDB, at https://www.niagads.org/genomics/ tools-and-software/databases/genomics-database. Annotations are also available for bulk download at https://www.niagads.org/datasets. Annotation processing software is available at http://www.icompbio.net/resources/software-and-downloads/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Melatonin receptors: molecular pharmacology and signalling in the context of system bias

Melatonin, N-acetyl-5-methoxytryptamine, an evolutionally old molecule, is produced by the pineal gland in vertebrates, and it binds with high affinity to melatonin receptors, which are members of the GPCR family. Among the multiple effects attributed to melatonin, we will focus here on those that are dependent on the activation of the two mammalian MT1 and MT2 melatonin receptors. We briefly summarize the latest developments on synthetic melatonin receptor ligands, including multi-target-directed ligands, and the characterization of signalling-biased ligands. We discuss signalling pathways activated by melatonin receptors that appear to be highly cell- and tissue-dependent, emphasizing the impact of system bias on the functional outcome. Different proteins have been demonstrated to interact with melatonin receptors, and thus, we postulate that part of this system bias has its molecular basis in differences of the expression of receptor-associated proteins including heterodimerization partners. Finally, bias at the level of the receptor, by the expression of genetic receptor variants, will be discussed to show how a modified receptor function can have an effect on the risk for common diseases like type 2 diabetes in humans. LINKED ARTICLES: This article is part of a themed section on Recent Developments in Research of Melatonin and its Potential Therapeutic Applications. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.16/issuetoc.

CaMKK2 kinase domain interacts with the autoinhibitory region through the N-terminal lobe including the RP insert

BACKGROUND

Calcium/calmodulin-dependent protein kinase kinase 2 (CaMKK2), a member of the Ca⁠²⁺/calmodulin-dependent kinase (CaMK) family, functions as an upstream activator of CaMKI, CaMKIV and AMP-activated protein kinase. Thus, CaMKK2 is involved in the regulation of several key physiological and pathophysiological processes. Previous studies have suggested that Ca²⁺/CaM binding may cause unique conformational changes in the CaMKKs compared with other CaMKs. However, the underlying mechanistic details remain unclear.

METHODS

In this study, hydrogen-deuterium exchange coupled to mass spectrometry, time-resolved fluorescence spectroscopy, small-angle x-ray scattering and chemical cross-linking were used to characterize Ca²⁺/CaM binding-induced structural changes in CaMKK2.

RESULTS

Our data suggest that: (i) the CaMKK2 kinase domain interacts with the autoinhibitory region (AID) through the N-terminal lobe of the kinase domain including the RP insert, a segment important for targeting downstream substrate kinases; (ii) Ca²⁺/CaM binding affects the structure of several regions surrounding the ATP-binding pocket, including the activation segment; (iii) although the CaMKK2:Ca²⁺/CaM complex shows high conformational flexibility, most of its molecules are rather compact; and (iv) AID-bound Ca²⁺/CaM transiently interacts with the CaMKK2 kinase domain.

CONCLUSIONS

Interactions between the CaMKK2 kinase domain and the AID differ from those of other CaMKs. In the absence of Ca²⁺/CaM binding the autoinhibitory region inhibits CaMKK2 by both blocking access to the RP insert and by affecting the structure of the ATP-binding pocket.

GENERAL SIGNIFICANCE

Our results corroborate the hypothesis that Ca²⁺/CaM binding causes unique conformational changes in the CaMKKs relative to other CaMKs.

An efficient algorithm for generating the internal branches of a Kingman coalescent

Coalescent simulations are a widely used approach for simulating sample genealogies, but can become computationally burdensome in large samples. Methods exist to analytically calculate a sample's expected frequency spectrum without simulating full genealogies. However, statistics that rely on the distribution of the length of internal coalescent branches, such as the probability that two mutations of equal size arose on the same genealogical branch, have previously required full coalescent simulations to estimate. Here, we present a sampling method capable of efficiently generating limited portions of sample genealogies using a series of analytic equations that give probabilities for the number, start, and end of internal branches conditional on the number of final samples they subtend. These equations are independent of the coalescent waiting times and need only be calculated a single time, lending themselves to efficient computation. We compare our method with full coalescent simulations to show the resulting distribution of branch lengths and summary statistics are equivalent, but that for many conditions our method is at least 10 times faster.

Integrating rare genetic variants into pharmacogenetic drug response predictions

BACKGROUND

Variability in genes implicated in drug pharmacokinetics or drug response can modulate treatment efficacy or predispose to adverse drug reactions. Besides common genetic polymorphisms, recent sequencing projects revealed a plethora of rare genetic variants in genes encoding proteins involved in drug metabolism, transport, and response.

RESULTS

To understand the global importance of rare pharmacogenetic gene variants, we mapped the variability in 208 pharmacogenes by analyzing exome sequencing data from 60,706 unrelated individuals and estimated the importance of rare and common genetic variants using a computational prediction framework optimized for pharmacogenetic assessments. Our analyses reveal that rare pharmacogenetic variants were strongly enriched in mutations predicted to cause functional alterations. For more than half of the pharmacogenes, rare variants account for the entire genetic variability. Each individual harbored on average a total of 40.6 putatively functional variants, rare variants accounting for 10.8% of these. Overall, the contribution of rare variants was found to be highly gene- and drug-specific. Using warfarin, simvastatin, voriconazole, olanzapine, and irinotecan as examples, we conclude that rare genetic variants likely account for a substantial part of the unexplained inter-individual differences in drug metabolism phenotypes.

CONCLUSIONS

Combined, our data reveal high gene and drug specificity in the contributions of rare variants. We provide a proof-of-concept on how this information can be utilized to pinpoint genes for which sequencing-based genotyping can add important information to predict drug response, which provides useful information for the design of clinical trials in drug development and the personalization of pharmacological treatment.

Patterns of Genetic Coding Variation in a Native American Population before and after European Contact

The effects of European colonization on the genomes of Native Americans may have produced excesses of potentially deleterious features, mainly due to the severe reductions in population size and corresponding losses of genetic diversity. This assumption, however, neither considers actual genomic patterns that existed before colonization nor does it adequately capture the effects of admixture. In this study, we analyze the whole-exome sequences of modern and ancient individuals from a Northwest Coast First Nation, with a demographic history similar to other indigenous populations from the Americas. We show that in approximately ten generations from initial European contact, the modern individuals exhibit reduced levels of novel and low-frequency variants, a lower proportion of potentially deleterious alleles, and decreased heterozygosity when compared to their ancestors. This pattern can be explained by a dramatic population decline, resulting in the loss of potentially damaging low-frequency variants, and subsequent admixture. We also find evidence that the indigenous population was on a steady decline in effective population size for several thousand years before contact, which emphasizes regional demography over the common conception of a uniform expansion after entry into the Americas. This study examines the genomic consequences of colonialism on an indigenous group and describes the continuing role of gene flow among modern populations.

Sequence Analysis of Drug Target Genes with Suicidal Behavior in Bipolar Disorder Patients

Background

A number of genes have been implicated in recent genome-wide association studies of suicide attempt in bipolar disorder. More focused investigation of genes coding for protein targets of existing drugs may lead to drug repurposing for the treatment and/or prevention of suicide.

Methods

We analyzed 2,457 DNA variants across 197 genes of interest to GlaxoSmithKline across the pipeline in our sample of European patients suffering from bipolar disorder (N = 219). We analyzed these variants for a possible association with the suicide severity score (ranging from suicidal ideation/plan to serious suicide attempt) from the Schedule for Clinical Assessment in Neuropsychiatry. We conducted tests of individual variants and gene-based tests.

Results

We found a number of DNA variants in the transforming growth factor beta receptor 1 gene (TGFBR1) to be suggestively associated with suicide severity scores (p < 0.005). The gene-based tests also pointed to TGFBR1 to be associated with suicide severity (p = 0.0001). However, these findings were not replicated in an independent bipolar disorder sample.

Conclusions

We report no significant association between DNA sequences of drug target genes and suicidal behavior. Additional larger sequencing studies could further interrogate associations between variants in drug target genes and suicidal behavior.

A Powerful Test for SNP Effects on Multivariate Binary Outcomes using Kernel Machine Regression

Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a SNP-set on multiple, possibly correlated, binary responses. We develop a score-based test using a nonparametric modeling framework that jointly models the global effect of the marker set. We account for the nonlinear effects and potentially complicated interaction between markers using reproducing kernels. Our testing procedure only requires estimation under the null hypothesis and we use multivariate generalized estimating equations (GEEs) to estimate the model components to account for the correlation among the outcomes. We evaluate finite sample performance of our test via simulation study and demonstrated our methods using the CATIE antibody study data and the CoLaus Study data.

Impact of Genetic Variation on CRISPR-Cas Targeting

The CRISPR-CRISPR-associated (Cas) nuclease system offers the ability to perform unprecedented functional genetic experiments and the promise of therapy for a variety of genetic disorders. The understanding of factors contributing to CRISPR targeting efficacy and specificity continues to evolve. As CRISPR systems rely on Watson-Crick base pairing to ultimately mediate genomic cleavage, it logically follows that genetic variation would affect CRISPR targeting by increasing or decreasing sequence homology at on-target and off-target sites or by altering protospacer adjacent motifs. Numerous efforts have been made to document the extent of human genetic variation, which can serve as resources to understand and mitigate the effect of genetic variation on CRISPR targeting. Here, we review efforts to elucidate the effect of human genetic variation on CRISPR targeting at on-target and off-target sites with considerations for laboratory experiments and clinical translation of CRISPR-based therapies.

Population Diversity in Pharmacogenetics: A Latin American Perspective

Pharmacogenetics/pharmacogenomics (PGx) relies on human genetic diversity. In this review we initially examine the PGx implications of human demographic history and genetic diversity, and highlight results from recent studies on the worldwide distribution of common and rare variants in pharmacogenes. The abundance of rare variants implies that a substantial effort will be required to identify their putative functional effects and to develop reliable algorithms for PGx-guided prescription. Furthermore, variants in all pharmacogenes relevant to a drug treatment must be considered. This implies a shift of the current paradigm of PGx-informed prescription based on genotyping a few common variants in selected genes toward comprehensive sequencing approaches. The following sections deal with the impact of population admixture on PGx diversity focusing on Latin America, where a kaleidoscopic combination of individual proportions of Native American, European, and sub-Saharan African ancestries prevails. We illustrate this diversity by contrasting Brazil and Mexico, the two most populous countries in Latin America, and show that population average admixture proportions are not predictive of the corresponding proportions at the individual level. As a consequence of admixture, the genetic differentiation of common pharmacogenetic variants in Latin Americans is much attenuated in comparison to their most relevant ancestral populations. Finally, we review data for tacrolimus and warfarin to illustrate the opportunities and challenges presented by Latin American populations for PGx studies and clinical implementation.

Population genomic analysis of elongated skulls reveals extensive female-biased immigration in Early Medieval Bavaria

Many modern European states trace their roots back to a period known as the Migration Period that spans from Late Antiquity to the early Middle Ages. We have conducted the first population-level analysis of people from this era, generating genomic data from 41 graves from archaeological sites in present-day Bavaria in southern Germany mostly dating to around 500 AD. While they are predominantly of northern/central European ancestry, we also find significant evidence for a nonlocal genetic provenance that is highly enriched among resident Early Medieval women, demonstrating artificial skull deformation. We infer that the most likely origin of the majority of these women was southeastern Europe, resolving a debate that has lasted for more than half a century.

Modern European genetic structure demonstrates strong correlations with geography, while genetic analysis of prehistoric humans has indicated at least two major waves of immigration from outside the continent during periods of cultural change. However, population-level genome data that could shed light on the demographic processes occurring during the intervening periods have been absent. Therefore, we generated genomic data from 41 individuals dating mostly to the late 5th/early 6th century AD from present-day Bavaria in southern Germany, including 11 whole genomes (mean depth 5.56×). In addition we developed a capture array to sequence neutral regions spanning a total of 5 Mb and 486 functional polymorphic sites to high depth (mean 72×) in all individuals. Our data indicate that while men generally had ancestry that closely resembles modern northern and central Europeans, women exhibit a very high genetic heterogeneity; this includes signals of genetic ancestry ranging from western Europe to East Asia. Particularly striking are women with artificial skull deformations; the analysis of their collective genetic ancestry suggests an origin in southeastern Europe. In addition, functional variants indicate that they also differed in visible characteristics. This example of female-biased migration indicates that complex demographic processes during the Early Medieval period may have contributed in an unexpected way to shape the modern European genetic landscape. Examination of the panel of functional loci also revealed that many alleles associated with recent positive selection were already at modern-like frequencies in European populations ∼1,500 years ago.

Gene-by-environment interactions in urban populations modulate risk phenotypes

Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual’s response to environmental challenges.

Individuals with different genotypes may respond differently to environmental variation. Here, Favé et al. find substantial impacts of different environment exposures on the transcriptome and clinical endophenotypes when controlling for genetic ancestry by analyzing data from ∼1000 individuals from a founder population in Quebec.

Allelic decomposition and exact genotyping of highly polymorphic and structurally variant genes

High-throughput sequencing provides the means to determine the allelic decomposition for any gene of interest-the number of copies and the exact sequence content of each copy of a gene. Although many clinically and functionally important genes are highly polymorphic and have undergone structural alterations, no high-throughput sequencing data analysis tool has yet been designed to effectively solve the full allelic decomposition problem. Here we introduce a combinatorial optimization framework that successfully resolves this challenging problem, including for genes with structural alterations. We provide an associated computational tool Aldy that performs allelic decomposition of highly polymorphic, multi-copy genes through using whole or targeted genome sequencing data. For a large diverse sequencing data set, Aldy identifies multiple rare and novel alleles for several important pharmacogenes, significantly improving upon the accuracy and utility of current genotyping assays. As more data sets become available, we expect Aldy to become an essential component of genotyping toolkits.

Meta-Analysis of Common and Rare Variants

Meta-analysis is a statistical technique that is widely used for improving the power to detect associations, by synthesizing data from independent studies, and is extensively used in the genomic analyses of complex traits. Estimates from different studies are combined and the results effectively provide the power of a much larger study. Meta-analysis also has the potential of discovering heterogeneity in the effects among the different studies. This chapter provides an overview of the methods used for meta-analysis of common and rare single variants and also for gene/region-based analyses; common variants are mainly identified via genome-wide association studies (GWAS) and rare variants through various types of sequencing experiments.

The global spectrum of protein-coding pharmacogenomic diversity

Differences in response to medications have a strong genetic component. By leveraging publically available data, the spectrum of such genomic variation can be investigated extensively. Pharmacogenomic variation was extracted from the 1000 Genomes Project Phase 3 data (2504 individuals, 26 global populations). A total of 12 084 genetic variants were found in 120 pharmacogenes, with the majority (90.0%) classified as rare variants (global minor allele frequency <0.5%), with 52.9% being singletons. Common variation clustered individuals into continental super-populations and 23 pharmacogenes contained highly differentiated variants (FST>0.5) for one or more super-population comparison. A median of three clinical variants (PharmGKB level 1A/B) was found per individual, and 55.4% of individuals carried loss-of-function variants, varying by super-population (East Asian 60.9%>African 60.1%>South Asian 60.3%>European 49.3%>Admixed 39.2%). Genome sequencing can therefore identify clinical pharmacogenomic variation, and future studies need to consider rare variation to understand the spectrum of genetic diversity contributing to drug response.

Assessing Rare Variation in Complex Traits

While genome-wide association studies have been very successful in identifying associations of common genetic variants with many different traits, the rarer frequency spectrum of the genome has not yet been comprehensively explored. Technological developments increasingly lift restrictions to access rare genetic variation. Dense reference panels enable improved genotype imputation for rarer variants in studies using DNA microarrays. Moreover, the decreasing cost of next generation sequencing makes whole exome and genome sequencing increasingly affordable for large samples. Large-scale efforts based on sequencing, such as ExAC, 100,000 Genomes, and TopMed, are likely to significantly advance this field.The main challenge in evaluating complex trait associations of rare variants is statistical power. The choice of population should be considered carefully because allele frequencies and linkage disequilibrium structure differ between populations. Genetically isolated populations can have favorable genomic characteristics for the study of rare variants.One strategy to increase power is to assess the combined effect of multiple rare variants within a region, known as aggregate testing. A  range of methods have been developed for this. Model performance depends on the genetic architecture of the region of interest.

On the substructure controls in rare variant analysis: Principal components or variance components?

Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS including stratified populations, admixed populations, and spatially distributed nongenetic risk; we investigate which types of variants (e.g., common, less frequent, rare, or all variants) should be used to effectively control for confounding effects. We found that (i) PC-based methods can account for confounding effects in most scenarios except for admixture, although the number of sufficient PCs depends on the PS complexity and the type of variants used. (ii) PCs based on all variants (i.e., common + less frequent + rare) tend to require equal or fewer sufficient PCs and often achieve higher power than PCs based on other variant types. (iii) VC-based methods can effectively adjust for confounding in all scenarios (even for admixture), though the type of variants should be used to construct VC may vary. (iv) VC based on all variants works consistently in all scenarios, though its power may be sometimes lower than VC based on other variant types. Given that the best-performed method and which variants to use depend on the underlying unknown confounding mechanisms, a robust strategy is to perform SKAT analyses using VC-based methods based on all variants.

Genetic variation in human drug-related genes

Background

Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classic pharmacokinetic genes is rather well understood, the role of genetic variation in drug targets is typically less studied.

Methods

Based on 60,706 human exomes from the ExAC dataset, we performed an in-depth computational analysis of the prevalence of functional variants in 806 drug-related genes, including 628 known drug targets. We further computed the likelihood of 1236 FDA-approved drugs to be affected by functional variants in their targets in the whole ExAC population as well as different geographic sub-populations.

Results

We find that most genetic variants in drug-related genes are very rare (f < 0.1%) and thus will likely not be observed in clinical trials. Furthermore, we show that patient risk varies for many drugs and with respect to geographic ancestry. A focused analysis of oncological drug targets indicates that the probability of a patient carrying germline variants in oncological drug targets is, at 44%, high enough to suggest that not only somatic alterations but also germline variants carried over into the tumor genome could affect the response to antineoplastic agents.

Conclusions

This study indicates that even though many variants are very rare and thus likely not observed in clinical trials, four in five patients are likely to carry a variant with possibly functional effects in a target for commonly prescribed drugs. Such variants could potentially alter drug efficacy.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0502-5) contains supplementary material, which is available to authorized users.

Identification of potential genetic causal variants for rheumatoid arthritis by whole-exome sequencing

Rheumatoid arthritis (RA) is a highly prevalent chronic autoimmune disease. However, genetic and environmental factors involved in RA pathogenesis are still remained largely unknown. To identify the genetic causal variants underlying pathogenesis and disease progression of RA patients, we undertook the first comprehensive whole-exome sequencing (WES) study in a total of 124 subjects including 58 RA cases and 66 healthy controls in Han Chinese population. We identified 378 novel genes that were enriched with deleterious variants in RA patients using a gene burden test. The further functional effects of associated genetic genes were classified and assessed, including 21 newly identified genes that were involved in the extracellular matrix (ECM)-receptor interaction, protein digestion and absorption, focal adhesion and glycerophospholipid metabolism pathways relevant to RA pathogenesis. Moreover, six pathogenic variants were investigated and structural analysis predicted their potentially functional alteration by homology modeling. Importantly, five novel and rare homozygous variants (NCR3LG1, RAP1GAP, CHCHD5, HIPK2 and DIAPH2) were identified, which may exhibit more functional impact on RA pathogenesis. Notably, 7 genes involved in the olfactory transduction pathway were enriched and associated with RA disease progression. Therefore, we performed an efficient and powerful technique WES in Chinese RA patients and identified novel, rare and common disease causing genes that alter innate immunity pathways and contribute to the risk of RA. Findings in this study may provide potential diagnostic tools and therapeutic strategies for RA patients.

Histamine Receptor 3 negatively regulates oligodendrocyte differentiation and remyelination

BACKGROUND

Agents promoting oligodendrocyte precursor cell differentiation have the potential to restore halted and/or delayed remyelination in patients with multiple sclerosis. However, few therapeutic targets have been identified. The objective of this study was to identify novel targets for promotion of remyelination and characterize their activity in vitro and in vivo.

METHODS

A high-content screening assay with differentiation of primary rat oligodendrocyte precursor cells was used to screen GSK-proprietary annotated libraries for remyelination-promoting compounds. Compounds were further validated in vitro and in vivo models; clinical relevance of target was confirmed in human post-mortem brain sections from patients with MS.

RESULTS

Of ~1000 compounds screened, 36 promoted oligodendrocyte precursor cell differentiation in a concentration-dependent manner; seven were histamine receptor-3 (H3R) antagonists. Inverse agonists of H3R but not neutral antagonists promoted oligodendrocyte precursor cell (OPC) differentiation. H3R was expressed throughout OPC differentiation; H3R expression was transiently upregulated on Days 3-5 and subsequently downregulated. H3R gene knockdown in OPCs increased the expression of differentiation markers and the number of mature oligodendrocytes. Overexpression of full-length H3R reduced differentiation marker expression and the number of mature cells. H3R inverse agonist GSK247246 reduced intracellular cyclic AMP (cAMP) and downstream cAMP response element-binding protein (CREB) phosphorylation in a dose-dependent manner. Histone deacetylase (HDAC-1) and Hes-5 were identified as key downstream targets of H3R during OPC differentiation. In the mouse cuprizone/rapamycin model of demyelination, systemic administration of brain-penetrable GSK247246 enhanced remyelination and subsequently protected axons. Finally, we detected high H3R expression in oligodendroglial cells from demyelination lesions in human samples of patients with MS, and validated a genetic association between an exonic single nucleotide polymorphism in HRH3 and susceptibility to multiple sclerosis.

CONCLUSIONS

From phenotypic screening to human genetics, we provide evidence for H3R as a novel therapeutic target to promote remyelination in patients with multiple sclerosis.

Relaxed Selection During a Recent Human Expansion

Humans have colonized the planet through a series of range expansions, which deeply impacted genetic diversity in newly settled areas and potentially increased the frequency of deleterious mutations on expanding wave fronts. To test this prediction, we studied the genomic diversity of French Canadians who colonized Quebec in the 17th century. We used historical information and records from ∼4000 ascending genealogies to select individuals whose ancestors lived mostly on the colonizing wave front and individuals whose ancestors remained in the core of the settlement. Comparison of exomic diversity reveals that: (i) both new and low-frequency variants are significantly more deleterious in front than in core individuals, (ii) equally deleterious mutations are at higher frequencies in front individuals, and (iii) front individuals are two times more likely to be homozygous for rare very deleterious mutations present in Europeans. These differences have emerged in the past six to nine generations and cannot be explained by differential inbreeding, but are consistent with relaxed selection mainly due to higher rates of genetic drift on the wave front. Demographic inference and modeling of the evolution of rare variants suggest lower effective size on the front, and lead to an estimation of selection coefficients that increase with conservation scores. Even though range expansions have had a relatively limited impact on the overall fitness of French Canadians, they could explain the higher prevalence of recessive genetic diseases in recently settled regions of Quebec.

Comparison of Single Genome and Allele Frequency Data Reveals Discordant Demographic Histories

Inference of demographic history from genetic data is a primary goal of population genetics of model and nonmodel organisms. Whole genome-based approaches such as the pairwise/multiple sequentially Markovian coalescent methods use genomic data from one to four individuals to infer the demographic history of an entire population, while site frequency spectrum (SFS)-based methods use the distribution of allele frequencies in a sample to reconstruct the same historical events. Although both methods are extensively used in empirical studies and perform well on data simulated under simple models, there have been only limited comparisons of them in more complex and realistic settings. Here we use published demographic models based on data from three human populations (Yoruba, descendants of northwest-Europeans, and Han Chinese) as an empirical test case to study the behavior of both inference procedures. We find that several of the demographic histories inferred by the whole genome-based methods do not predict the genome-wide distribution of heterozygosity, nor do they predict the empirical SFS. However, using simulated data, we also find that the whole genome methods can reconstruct the complex demographic models inferred by SFS-based methods, suggesting that the discordant patterns of genetic variation are not attributable to a lack of statistical power, but may reflect unmodeled complexities in the underlying demography. More generally, our findings indicate that demographic inference from a small number of genomes, routine in genomic studies of nonmodel organisms, should be interpreted cautiously, as these models cannot recapitulate other summaries of the data.

Direct estimation of de novo mutation rates in a chimpanzee parent-offspring trio by ultra-deep whole genome sequencing

Mutations generate genetic variation and are a major driving force of evolution. Therefore, examining mutation rates and modes are essential for understanding the genetic basis of the physiology and evolution of organisms. Here, we aim to identify germline de novo mutations through the whole-genome surveyance of Mendelian inheritance error sites (MIEs), those not inherited through the Mendelian inheritance manner from either of the parents, using ultra-deep whole genome sequences (>150-fold) from a chimpanzee parent-offspring trio. We identified such 889 MIEs and classified them into four categories based on the pattern of inheritance and the sequence read depth: [i] de novo single nucleotide variants (SNVs), [ii] copy number neutral inherited variants, [iii] hemizygous deletion inherited variants, and [iv] de novo copy number variants (CNVs). From de novo SNV candidates, we estimated a germline de novo SNV mutation rate as 1.48 × 10^-8 per site per generation or 0.62 × 10^-9 per site per year. In summary, this study demonstrates the significance of ultra-deep whole genome sequencing not only for the direct estimation of mutation rates but also for discerning various mutation modes including de novo allelic conversion and de novo CNVs by identifying MIEs through the transmission of genomes from parents to offspring.

The impact of rare variation on gene expression across tissues

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Characterization of ADME gene variation in 21 populations by exome sequencing

OBJECTIVE

Proteins involving absorption, distribution, metabolism, and excretion (ADME) play a critical role in drug pharmacokinetics. The type and frequency of genetic variation in the ADME genes differ among populations. The aim of this study was to systematically investigate common and rare ADME coding variation in diverse ethnic populations by exome sequencing.

MATERIALS AND METHODS

Data derived from commercial exome capture arrays and next-generation sequencing were used to characterize coding variation in 298 ADME genes in 251 Northeast Asians and 1181 individuals from the 1000 Genomes Project.

RESULTS

Approximately 75% of the ADME coding sequence was captured at high quality across the joint samples harboring more than 8000 variants, with 49% of individuals carrying at least one 'knockout' allele. ADME genes carried 50% more nonsynonymous variation than non-ADME genes (P=8.2×10) and showed significantly greater levels of population differentiation (P=7.6×10). Out of the 2135 variants identified that were predicted to be deleterious, 633 were not on commercially available ADME or general-purpose genotyping arrays. Forty deleterious variants within important ADME genes, with frequencies of at least 2% in at least one population, were identified as candidates for future pharmacogenetic studies.

CONCLUSION

Exome sequencing was effective in accurately genotyping most ADME variants important for pharmacogenetic research, in addition to identifying rare or potentially de novo coding variants that may be clinically meaningful. Furthermore, as a class, ADME genes are more variable and less sensitive to purifying selection than non-ADME genes.

A Bivariate Hypothesis Testing Approach for Mapping the Trait-Influential Gene

The linkage disequilibrium (LD) based quantitative trait loci (QTL) model involves two indispensable hypothesis tests: the test of whether or not a QTL exists, and the test of the LD strength between the QTaL and the observed marker. The advantage of this two-test framework is to test whether there is an influential QTL around the observed marker instead of just having a QTL by random chance. There exist unsolved, open statistical questions about the inaccurate asymptotic distributions of the test statistics. We propose a bivariate null kernel (BNK) hypothesis testing method, which characterizes the joint distribution of the two test statistics in two-dimensional space. The power of this BNK approach is verified by three different simulation designs and one whole genome dataset. It solves a few challenging open statistical questions, closely separates the confounding between ‘linkage’ and ‘QTL effect’, makes a fine genome division, provides a comprehensive understanding of the entire genome, overcomes limitations of traditional QTL approaches, and connects traditional QTL mapping with the newest genotyping technologies. The proposed approach contributes to both the genetics literature and the statistics literature, and has a potential to be extended to broader fields where a bivariate test is needed.

Genetic variants affecting equivalent protein family positions reflect human diversity

Members of diverse protein families often perform overlapping or redundant functions meaning that different variations within them could reflect differences between individual organisms. We investigated likely functional positions within aligned protein families that contained a significant enrichment of nonsynonymous variants in genomes of healthy individuals. We identified more than a thousand enriched positions across hundreds of family alignments with roles indicative of mammalian individuality, including sensory perception and the immune system. The most significant position is the Arginine from the Olfactory receptor “DRY” motif, which has more variants in healthy individuals than all other positions in the proteome. Odorant binding data suggests that these variants lead to receptor inactivity, and they are mostly mutually exclusive with other loss-of-function (stop/frameshift) variants. Some DRY Arginine variants correlate with smell preferences in sub-populations and all 2,504 humans studied contain a unique spectrum of active and inactive receptors. The many other variant enriched positions, across hundreds of other families might also provide insights into individual differences.

Analysis of population-specific pharmacogenomic variants using next-generation sequencing data

Functional rare variants in drug-related genes are believed to be highly differentiated between ethnic- or racial populations. However, knowledge of population differentiation (PD) of rare single-nucleotide variants (SNVs), remains widely lacking, with the highest fixation indices, (F_st values), from both rare and common variants annotated to specific genes, having only been marginally used to understand PD at the gene level. In this study, we suggest a new, gene-based PD method, PD of Rare and Common variants (PDRC), for analyzing rare variants, as inspired by Generalized Cochran-Mantel-Haenszel (GCMH) statistics, to identify highly population-differentiated drug response-related genes (“pharmacogenes”). Through simulation studies, we reveal that PDRC adequately summarizes rare and common variants, due to PD, over a specific gene. We also applied the proposed method to a real whole-exome sequencing dataset, consisting of 10,000 datasets, from the Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) initiative, and 3,000 datasets from the Genetics of Type 2 diabetes (Go-T2D) repository. Among the 48 genes annotated with Very Important Pharmacogenetic summaries (VIPgenes), in the PharmGKB database, our PD method successfully identified candidate genes with high PD, including ACE, CYP2B6, DPYD, F5, MTHFR, and SCN5A.

Naturally Segregating Variation at Ugt86Dd Contributes to Nicotine Resistance in Drosophila melanogaster

Identifying the sequence polymorphisms underlying complex trait variation is a key goal of genetics research, since knowing the precise causative molecular events allows insight into the pathways governing trait variation. Genetic analysis of complex traits in model systems regularly starts by constructing QTL maps, but generally fails to identify causative sequence polymorphisms. Previously we mapped a series of QTL contributing to resistance to nicotine in a Drosophila melanogaster multiparental mapping resource and here use a battery of functional tests to resolve QTL to the molecular level. One large-effect QTL resided over a cluster of UDP-glucuronosyltransferases, and quantitative complementation tests using deficiencies eliminating subsets of these detoxification genes revealed allelic variation impacting resistance. RNAseq showed that Ugt86Dd had significantly higher expression in genotypes that are more resistant to nicotine, and anterior midgut-specific RNA interference (RNAi) of this gene reduced resistance. We discovered a segregating 22-bp frameshift deletion in Ugt86Dd, and accounting for the InDel during mapping largely eliminates the QTL, implying the event explains the bulk of the effect of the mapped locus. CRISPR/Cas9 editing of a relatively resistant genotype to generate lesions in Ugt86Dd that recapitulate the naturally occurring putative loss-of-function allele, leads to a large reduction in resistance. Despite this major effect of the deletion, the allele appears to be very rare in wild-caught populations and likely explains only a small fraction of the natural variation for the trait. Nonetheless, this putatively causative coding InDel can be a launchpad for future mechanistic exploration of xenobiotic detoxification.

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study

The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.

Brief Report: Whole-Exome Sequencing for Identification of Potential Causal Variants for Diffuse Cutaneous Systemic Sclerosis

OBJECTIVE

Scleroderma is a genetically complex autoimmune disease with substantial phenotypic heterogeneity. Previous genome-wide association studies have identified common genetic variants associated with disease risk, but these studies are not designed to capture rare or potential causal variants. Our goal was to identify rare as well as common genetic variants in patients with diffuse cutaneous systemic sclerosis (dcSSc) through whole-exome sequencing (WES) in order to identify potential causal variants.

METHODS

We generated WES data for 32 dcSSc patients with or without interstitial lung disease (ILD) and for 17 healthy "in-house" controls. Variants were annotated and filtered by quality, minor allele frequency, and deleterious effects on gene function. We applied a gene burden test to identify novel dcSSc and dcSSc-associated ILD candidate genes that were enriched with deleterious variants in cases compared to in-house controls as well as controls from the 1000 Genomes Project (n = 130).

RESULTS

We identified 70 genes that were enriched with deleterious variants in dcSSc patients. Two of them (BANK1 and TERT) were in pathways previously implicated in SSc or ILD pathogenesis or known susceptibility loci. Newly identified genes (COL4A3, COL4A4, COL5A2, COL13A1, and COL22A1) were significantly enriched in the extracellular matrix-related pathway, which is relevant to the fibrotic features of dcSSc, and in the DNA repair pathway (XRCC4).

CONCLUSION

This study demonstrates the value of WES for the identification of novel gene variants and pathways that may contribute to scleroderma risk and/or severity. The candidate genes we discovered are potential targets for in-depth functional studies.

Comparison of single-marker and multi-marker tests in rare variant association studies of quantitative traits

In genetic association studies of rare variants, low statistical power and potential violations of established estimator properties are among the main challenges of association tests. Multi-marker tests (MMTs) have been proposed to target these challenges, but any comparison with single-marker tests (SMTs) has to consider that their aim is to identify causal genomic regions instead of variants. Valid power comparisons have been performed for the analysis of binary traits indicating that MMTs have higher power, but there is a lack of conclusive studies for quantitative traits. The aim of our study was therefore to fairly compare SMTs and MMTs in their empirical power to identify the same causal loci associated with a quantitative trait. The results of extensive simulation studies indicate that previous results for binary traits cannot be generalized. First, we show that for the analysis of quantitative traits, conventional estimation methods and test statistics of single-marker approaches have valid properties yielding association tests with valid type I error, even when investigating singletons or doubletons. Furthermore, SMTs lead to more powerful association tests for identifying causal genes than MMTs when the effect sizes of causal variants are large, and less powerful tests when causal variants have small effect sizes. For moderate effect sizes, whether SMTs or MMTs have higher power depends on the sample size and percentage of causal SNVs. For a more complete picture, we also compare the power in studies of quantitative and binary traits, and the power to identify causal genes with the power to identify causal rare variants. In a genetic association analysis of systolic blood pressure in the Genetic Analysis Workshop 19 data, SMTs yielded smaller p-values compared to MMTs for most of the investigated blood pressure genes, and were least influenced by the definition of gene regions.