Genotype imputation

Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies

Genotype imputation has been widely utilized for two reasons in the analysis of genome-wide association studies (GWAS). One reason is to increase the power for association studies when causal single nucleotide polymorphisms are not collected in the GWAS. The second reason is to aid the interpretation of a GWAS result by predicting the association statistics at untyped variants. In this article, we show that prediction of association statistics at untyped variants that have an influence on the trait produces is overly conservative. Current imputation methods assume that none of the variants in a region (locus consists of multiple variants) affect the trait, which is often inconsistent with the observed data. In this article, we propose a new method, CAUSAL-Imp, which can impute the association statistics at untyped variants while taking into account variants in the region that may affect the trait. Our method builds on recent methods that impute the marginal statistics for GWAS by utilizing the fact that marginal statistics follow a multivariate normal distribution. We utilize both simulated and real data sets to assess the performance of our method. We show that traditional imputation approaches underestimate the association statistics for variants involved in the trait, and our results demonstrate that our approach provides less biased estimates of these association statistics.

Simulating the Large-Scale Erosion of Genomic Privacy Over Time

The dramatically decreasing costs of DNA sequencing have triggered more than a million humans to have their genotypes sequenced. Moreover, these individuals increasingly make their genomic data publicly available, thereby creating privacy threats for themselves and their relatives because of their DNA similarities. More generally, an entity that gains access to a significant fraction of sequenced genotypes might be able to infer even the genomes of unsequenced individuals. In this paper, we propose a simulation-based model for quantifying the impact of continuously sequencing and publicizing personal genomic data on a population's genomic privacy. Our simulation probabilistically models data sharing and takes into account events such as migration and interracial mating. We exemplarily instantiate our simulation with a sample population of 1,000 individuals and evaluate the privacy under multiple settings over 6,000 genomic variants and a subset of phenotype-related variants. Our findings demonstrate that an increasing sharing rate in the future entails a substantial negative effect on the privacy of all older generations. Moreover, we find that mixed populations face a less severe erosion of privacy over time than more homogeneous populations. Finally, we demonstrate that genomic-data sharing can be much more detrimental for the privacy of the phenotype-related variants.

Genetics of membranous nephropathy

An HLA-DR3 association with membranous nephropathy (MN) was described in 1979 and additional evidence for a genetic component to MN was suggested in 1984 in reports of familial MN. In 2009, a pathogenic autoantibody was identified against the phospholipase A2 receptor 1 (PLA2R1). Here we discuss the genetic studies that have proven the association of human leucocyte antigen class II and PLA2R1 variants and disease in MN. The common variants in PLA2R1 form a haplotype that is associated with disease incidence. The combination of the variants in both genes significantly increases the risk of disease by 78.5-fold. There are important genetic ethnic differences in MN. Disease outcome is difficult to predict and attempts to correlate the genetic association to outcome have so far not been helpful in a reproducible manner. The role of genetic variants may not only extend beyond the risk of disease development, but can also help us understand the underlying molecular biology of the PLA2R1 and its resultant pathogenicity. The genetic variants identified thus far have an association with disease and could therefore become useful biomarkers to stratify disease risk, as well as possibly identifying novel drug targets in the near future.

An imputation platform to enhance integration of rice genetic resources

As sequencing and genotyping technologies evolve, crop genetics researchers accumulate increasing numbers of genomic data sets from various genotyping platforms on different germplasm panels. Imputation is an effective approach to increase marker density of existing data sets toward the goal of integrating resources for downstream applications. While a number of imputation software packages are available, the limitations to utilization for the rice community include high computational demand and lack of a reference panel. To address these challenges, we develop the Rice Imputation Server, a publicly available web application leveraging genetic information from a globally diverse rice reference panel assembled here. This resource allows researchers to benefit from increased marker density without needing to perform imputation on their own machines. We demonstrate improvements that imputed data provide to rice genome-wide association (GWA) results of grain amylose content and show that the major functional nucleotide polymorphism is tagged only in the imputed data set.

SNP-skimming: A fast approach to map loci generating quantitative variation in natural populations

Genome-wide association mapping (GWAS) is a method to estimate the contribution of segregating genetic loci to trait variation. A major challenge for applying GWAS to nonmodel species has been generating dense genome-wide markers that satisfy the key requirement that marker data are error-free. Here, we present an approach to map loci within natural populations using inexpensive shallow genome sequencing. This "SNP-skimming" approach involves two steps: an initial genome-wide scan to identify putative targets followed by deep sequencing for confirmation of targeted loci. We apply our method to a test data set of floral dimension variation in the plant Penstemon virgatus, a member of a genus that has experienced dynamic floral adaptation that reflects repeated transitions in primary pollinator. The ability to detect SNPs that generate phenotypic variation depends on population genetic factors such as population allele frequency, effect size and epistasis, as well as sampling effects contingent on missing data and genotype uncertainty. However, both simulations and the Penstemon data suggest that the most significant tests from the initial SNP skim are likely to be true positives-loci with subtle but significant quantitative effects on phenotype. We discuss the promise and limitations of this method and consider optimal experimental design for a given sequencing effort. Simulations demonstrate that sampling a larger number of individual at the expense of average read depth per individual maximizes the power to detect loci.

The State of "Omics" Research for Farmed Penaeids: Advances in Research and Impediments to Industry Utilization

Elucidating the underlying genetic drivers of production traits in agricultural and aquaculture species is critical to efforts to maximize farming efficiency. "Omics" based methods (i.e., transcriptomics, genomics, proteomics, and metabolomics) are increasingly being applied to gain unprecedented insight into the biology of many aquaculture species. While the culture of penaeid shrimp has increased markedly, the industry continues to be impeded in many regards by disease, reproductive dysfunction, and a poor understanding of production traits. Extensive effort has been, and continues to be, applied to develop critical genomic resources for many commercially important penaeids. However, the industry application of these genomic resources, and the translation of the knowledge derived from "omics" studies has not yet been completely realized. Integration between the multiple "omics" resources now available (i.e., genome assemblies, transcriptomes, linkage maps, optical maps, and proteomes) will prove critical to unlocking the full utility of these otherwise independently developed and isolated resources. Furthermore, emerging "omics" based techniques are now available to address longstanding issues with completing keystone genome assemblies (e.g., through long-read sequencing), and can provide cost-effective industrial scale genotyping tools (e.g., through low density SNP chips and genotype-by-sequencing) to undertake advanced selective breeding programs (i.e., genomic selection) and powerful genome-wide association studies. In particular, this review highlights the status, utility and suggested path forward for continued development, and improved use of "omics" resources in penaeid aquaculture.

From genome-wide associations to candidate causal variants by statistical fine-mapping

Advancing from statistical associations of complex traits with genetic markers to understanding the functional genetic variants that influence traits is often a complex process. Fine-mapping can select and prioritize genetic variants for further study, yet the multitude of analytical strategies and study designs makes it challenging to choose an optimal approach. We review the strengths and weaknesses of different fine-mapping approaches, emphasizing the main factors that affect performance. Topics include interpreting results from genome-wide association studies (GWAS), the role of linkage disequilibrium, statistical fine-mapping approaches, trans-ethnic studies, genomic annotation and data integration, and other analysis and design issues.

Accuracy of genotype imputation in Labrador Retrievers

The dog is a valuable model species for the genetic analysis of complex traits, and the use of genotype imputation in dogs will be an important tool for future studies. It is of particular interest to analyse the effect of factors like single nucleotide polymorphism (SNP) density of genotyping arrays and relatedness between dogs on imputation accuracy due to the acknowledged genetic and pedigree structure of dog breeds. In this study, we simulated different genotyping strategies based on data from 1179 Labrador Retriever dogs. The study involved 5826 SNPs on chromosome 1 representing the high density (HighD) array; the low-density (LowD) array was simulated by masking different proportions of SNPs on the HighD array. The correlations between true and imputed genotypes for a realistic masking level of 87.5% ranged from 0.92 to 0.97, depending on the scenario used. A correlation of 0.92 was found for a likely scenario (10% of dogs genotyped using HighD, 87.5% of HighD SNPs masked in the LowD array), which indicates that genotype imputation in Labrador Retrievers can be a valuable tool to reduce experimental costs while increasing sample size. Furthermore, we show that genotype imputation can be performed successfully even without pedigree information and with low relatedness between dogs in the reference and validation sets. Based on these results, the impact of genotype imputation was evaluated in a genome-wide association analysis and genomic prediction in Labrador Retrievers.

Study protocol of the RaPS study: novel risk adapted prevention strategies for people with a family history of colorectal cancer

BACKGROUND

People aged 40-60 years with a family history (FH) of colorectal cancer (CRC) in 1st degree relatives (FDRs) have a 2- to 4-fold increased risk of CRC compared to the average risk population. Therefore, experts recommend starting CRC screening earlier for this high-risk group. However, information on prevalence of relevant colonoscopic findings in this group is sparse, and no risk adapted screening offers are implemented in the German health care system. For example, screening colonoscopy is uniformly offered from age 55 on, regardless of family history. Thus, we initiated a multicenter epidemiological study - the RaPS study (Risk adapted prevention strategies for colorectal cancer) - with the following aims: to determine the prevalence of having a FH of CRC in FDR in the German population aged 40-54 years; to investigate the prevalence of colorectal neoplasms among people with a FDR; and to develop risk-adapted prevention strategies for this high-risk group based on the collected information.

METHODS/DESIGN

A random sample of 160.000 persons from the general population aged 40-54 years from the catchment areas of three study centers in Germany (Dresden, Munich and Stuttgart) are contacted to assess FH of CRC by an online-questionnaire. Those with a FH of CRC in FDRs are invited to the study centers for individual consultation regarding CRC prevention. Participants are asked to donate blood and stool samples and medical records of colonoscopies will be obtained. Prevalence of CRC and its precursors will be evaluated. Furthermore, genetic, epigenetic and proteomic biomarkers in blood and microbiomic biomarkers in stool will be investigated. Risk markers and their eligibility for risk adapted screening offers will be examined.

DISCUSSION

This study will provide data on the prevalence of colorectal neoplasms among persons with a FH of CRC in the age group 40-54 years, which will enable us to derive evidence based screening strategies for this high-risk group.

TRIAL REGISTRATION

This trial was registered retrospectively in the German Clinical Trials Register (DRKS) on 29th of December 2016: German Clinical Trials Register DRKS-ID: DRKS00007842 .

The Influence of Dyslexia Candidate Genes on Reading Skill in Old Age

A number of candidate genes for reading and language impairment have been replicated, primarily in samples of children with developmental disability or delay, although these genes are also supported in adolescent population samples. The present study used a systematic approach to test 14 of these candidate genes for association with reading assessed in late adulthood (two cohorts with mean ages of 70 and 79 years). Gene-sets (14 candidates, axon-guidance and neuron migration pathways) and individual SNPs within each gene of interest were tested for association using imputed data referenced to the 1000 genomes European panel. Using the results from the genome-wide association (GWA) meta-analysis of the two cohorts (N = 1217), a competitive gene-set analysis showed that the candidate gene-set was associated with the reading index (p = .016) at a family wise error rate corrected significance level. Neither axon guidance nor neuron migration pathways were significant. Whereas individual SNP associations within CYP19A1, DYX1C1, CNTNAP2 and DIP2A genes (p < .05) did not reach corrected significance their allelic effects were in the same direction as past available reports. These results suggest that reading skill in normal adults shares the same genetic substrate as reading in adolescents, and clinically disordered reading, and highlights the utility of adult samples to increase sample sizes in the genetic study of developmental disorders.

Characterizing the effects of missing data and evaluating imputation methods for chemical prioritization applications using ToxPi

Background

The Toxicological Priority Index (ToxPi) is a method for prioritization and profiling of chemicals that integrates data from diverse sources. However, individual data sources ("assays"), such as in vitro bioassays or in vivo study endpoints, often feature sections of missing data, wherein subsets of chemicals have not been tested in all assays. In order to investigate the effects of missing data and recommend solutions, we designed simulation studies around high-throughput screening data generated by the ToxCast and Tox21 programs on chemicals highlighted by the Agency for Toxic Substances and Disease Registry's (ATSDR) Substance Priority List (SPL), which helps prioritize environmental research and remediation resources.

Results

Our simulations explored a wide range of scenarios concerning data (0-80% assay data missing per chemical), modeling (ToxPi models containing from 160-700 different assays), and imputation method (k-Nearest-Neighbor, Max, Mean, Min, Binomial, Local Least Squares, and Singular Value Decomposition). We find that most imputation methods result in significant changes to ToxPi score, except for datasets with a small number of assays. If we consider rank change conditional on these significant changes to ToxPi score, we find that ranks of chemicals in the minimum value imputation, SVD imputation, and kNN imputation sets are more sensitive to the score changes.

Conclusions

We found that the choice of imputation strategy exerted significant influence over both scores and associated ranks, and the most sensitive scenarios were those involving fewer assays plus higher proportions of missing data. By characterizing the effects of missing data and the relative benefit of imputation approaches across real-world data scenarios, we can augment confidence in the robustness of decisions regarding the health and ecological effects of environmental chemicals.

Genetic variants in sex hormone pathways and the risk of type 2 diabetes among African American, Hispanic American, and European American postmenopausal women in the US

BACKGROUND

Sex hormones are implicated in the development of diabetes. However, whether genetic variations in sex hormone pathways (SHPs) contribute to the risk of type 2 diabetes mellitus (T2DM) remains to be determined. This study investigated associations between genetic variations in all candidate genes in SHPs and T2DM risk among a cohort of women participating in the Women's Health Initiative (WHI).

METHODS

Single nucleotide polymorphisms (SNPs) located within 30 kb upstream and downstream of SHP genes were comprehensively examined in 8180 African American, 3498 Hispanic American, and 3147 European American women in the WHI. In addition, whether significant SNPs would be replicated in independent populations was examined.

RESULTS

After adjusting for age, region, and ancestry estimates and correcting for multiple testing, seven SNPs were significantly associated with the risk of T2DM among Hispanic American women were identified in the progesterone receptor (PGR) gene, with rs948516 showing the greatest significance (odds ratio 0.67; 95% confidence interval 0.57-0.78; P = 8.8 × 10^-7 ; false discovery rate, Q = 7.8 × 10^-4 ). These findings were not replicated in other ethnic groups in the WHI or in sex-combined analyses in replication studies.

CONCLUSION

Significant signals were identified implicating the PGR gene in T2DM development in Hispanic American women in the WHI, which are consistent with genome-wide association studies findings linking PGR to glucose homeostasis. Nevertheless, the PGR SNPs-T2DM association was not statistically significant in other ethnic populations. Further studies, especially sex-specific analyses, are needed to confirm the findings and clarify the role of SHPs in T2DM.

BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues

Background

Bisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to coverage depth variability. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features associated with predictive power.

Results

Here we describe BoostMe, a method for imputing low-quality DNA methylation estimates within whole-genome bisulfite sequencing (WGBS) data. BoostMe uses a gradient boosting algorithm, XGBoost, and leverages information from multiple samples for prediction. We find that BoostMe outperforms existing algorithms in speed and accuracy when applied to WGBS of human tissues. Furthermore, we show that imputation improves concordance between WGBS and the MethylationEPIC array at low WGBS depth, suggesting improved WGBS accuracy after imputation.

Conclusions

Our findings support the use of BoostMe as a preprocessing step for WGBS analysis.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4766-y) contains supplementary material, which is available to authorized users.

Genotype Imputation from Large Reference Panels

Genotype imputation has become a standard tool in genome-wide association studies because it enables researchers to inexpensively approximate whole-genome sequence data from genome-wide single-nucleotide polymorphism array data. Genotype imputation increases statistical power, facilitates fine mapping of causal variants, and plays a key role in meta-analyses of genome-wide association studies. Only variants that were previously observed in a reference panel of sequenced individuals can be imputed. However, the rapid increase in the number of deeply sequenced individuals will soon make it possible to assemble enormous reference panels that greatly increase the number of imputable variants. In this review, we present an overview of genotype imputation and describe the computational techniques that make it possible to impute genotypes from reference panels with millions of individuals.

Using Gene Genealogies to Localize Rare Variants Associated with Complex Traits in Diploid Populations

BACKGROUND AND AIMS

Many methods can detect trait association with causal variants in candidate genomic regions; however, a comparison of their ability to localize causal variants is lacking. We extend a previous study of the detection abilities of these methods to a comparison of their localization abilities.

METHODS

Through coalescent simulation, we compare several popular association methods. Cases and controls are sampled from a diploid population to mimic human studies. As benchmarks for comparison, we include two methods that cluster phenotypes on the true genealogical trees: a naive Mantel test considered previously in haploid populations and an extension that takes into account whether case haplotypes carry a causal variant. We first work through a simulated dataset to illustrate the methods. We then perform a simulation study to score the localization and detection properties.

RESULTS

In our simulations, the association signal was localized least precisely by the naive Mantel test and most precisely by its extension. Most other approaches had intermediate performance similar to the single-variant Fisher exact test.

CONCLUSIONS

Our results confirm earlier findings in haploid populations about potential gains in performance from genealogy-based approaches. They also highlight differences between haploid and diploid populations when localizing and detecting causal variants.

Genetic variant for behavioral regulation factor of executive function and its possible brain mechanism in attention deficit hyperactivity disorder

As a childhood-onset psychiatric disorder, attention deficit hyperactivity disorder (ADHD) is complicated by phenotypic and genetic heterogeneity. Lifelong executive function deficits in ADHD are described in many literatures and have been proposed as endophenotypes of ADHD. However, its genetic basis is still elusive. In this study, we performed a genome-wide association study of executive function, rated with Behavioral Rating Inventory of Executive Function (BRIEF), in ADHD children. We identified one significant variant (rs852004, P = 2.51e-08) for the overall score of BRIEF. The association analyses for each component of executive function found this locus was more associated with inhibit and monitor components. Further principle component analysis and confirmatory factor analysis provided an ADHD-specific executive function pattern including inhibit and monitor factors. SNP rs852004 was mainly associated with the Behavioral Regulation factor. Meanwhile, we found the significant locus was associated with ADHD symptom. The Behavioral Regulation factor mediated its effect on ADHD symptom. Functional magnetic resonance imaging (fMRI) analyses further showed evidence that this variant affected the activity of inhibition control related brain regions. It provided new insights for the genetic basis of executive function in ADHD.

Polygenic risk for psychiatric disorders correlates with executive function in typical development

Executive functions are a diverse and critical suite of cognitive abilities that are often disrupted in individuals with psychiatric disorders. Despite their moderate to high heritability, little is known about the molecular genetic factors that contribute to variability in executive functions and how these factors may be related to those that predispose to psychiatric disorders. We examined the relationship between polygenic risk scores built from large genome-wide association studies of psychiatric disorders and executive functioning in typically developing children. In our discovery sample (N = 417), consistent with previous reports on general cognitive abilities, polygenic risk for autism spectrum disorder was associated with better performance on the Dimensional Change Card Sort test from the NIH Cognition Toolbox, with the largest effect in the youngest children. Polygenic risk for major depressive disorder was associated with poorer performance on the Flanker test in the same sample. This second association replicated for performance on the Penn Conditional Exclusion Test in an independent cohort (N = 3681). Our results suggest that the molecular genetic factors contributing to variability in executive function during typical development are at least partially overlapping with those associated with psychiatric disorders, although larger studies and further replication are needed.

A new locus regulating MICALL2 expression was identified for association with executive inhibition in children with attention deficit hyperactivity disorder

Impaired executive inhibition is a core deficit of attention deficit hyperactivity disorder (ADHD), which is a common childhood-onset psychiatric disorder with high heritability. In this study, we performed a two-stage genome-wide association study of executive inhibition in ADHD in Han Chinese. We used the Stroop color-word interference test to evaluate executive inhibition. After quality control, 780 samples with phenotype and covariate data were included in the discovery stage, whereas 922 samples were included in the replication stage. We identified one new significant locus at 7p22.3 for the Stroop word interference time (rs11514810, P=3.42E-09 for discovery, P=0.01176 for replication and combined P=5.249E-09). Regulatory feature analysis and expression quantitative trait loci (eQTL) data showed that this locus contributes to MICALL2 expression in the human brain. Most genes in the network interacting with MICALL2 were associated with psychiatric disorders. Furthermore, hyperactive-impulsive-like behavior was induced by reducing the expression of the zebrafish gene that is homologous to MICALL2, which could be rescued by tomoxetine (atomoxetine), a clinical medication for ADHD. Our results suggested that MICALL2 is a new susceptibility gene for executive inhibition deficiency related to hyperactive-impulsive behavior in ADHD, further emphasizing the possible role of neurodevelopmental genes in the pathogenic mechanism of ADHD.

Genetic variants in mRNA untranslated regions

Genome Wide Association Studies (GWAS) have mapped thousands of genetic variants associated with complex disease risk and regulating quantitative traits, thus exploiting an unprecedented high-resolution genetic characterization of the human genome. A small fraction (3.7%) of the identified associations is located in untranslated regions (UTRs), and the molecular mechanism has been elucidated for few of them. Genetic variations at UTRs may modify regulatory elements affecting the interaction of the UTRs with proteins and microRNAs. The overall functional consequences include modulation of messenger RNA (mRNA) transcription, secondary structure, stability, localization, translation, and access to regulators like microRNAs (miRNAs) and RNA-binding proteins (RBPs). Alterations of these regulatory mechanisms are known to modify molecular pathways and cellular processes, potentially leading to disease processes. Here, we analyze some examples of genetic risk variants mapping in the UTR regulatory elements. We describe a recently identified genetic variant localized in the 3'UTR of the TNFSF13B gene, associated with autoimmunity risk and responsible of an increased stability and translation of TNFSF13B mRNA. We discuss how the correct use and interpretation of public GWAS repositories could lead to a better understanding of etiopathogenetic mechanisms and the generation of robust biological hypothesis as starting point for further functional studies. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Disease.

A Meta-Analysis of Genome-Wide Association Studies of Growth Differentiation Factor-15 Concentration in Blood

Blood levels of growth differentiation factor-15 (GDF-15), also known as macrophage inhibitory cytokine-1 (MIC-1), have been associated with various pathological processes and diseases, including cardiovascular disease and cancer. Prior studies suggest genetic factors play a role in regulating blood MIC-1/GDF-15 concentration. In the current study, we conducted the largest genome-wide association study (GWAS) to date using a sample of ∼5,400 community-based Caucasian participants, to determine the genetic variants associated with MIC-1/GDF-15 blood concentration. Conditional and joint (COJO), gene-based association, and gene-set enrichment analyses were also carried out to identify novel loci, genes, and pathways. Consistent with prior results, a locus on chromosome 19, which includes nine single nucleotide polymorphisms (SNPs) (top SNP, rs888663, p = 1.690 × 10^-35), was significantly associated with blood MIC-1/GDF-15 concentration, and explained 21.47% of its variance. COJO analysis showed evidence for two independent signals within this locus. Gene-based analysis confirmed the chromosome 19 locus association and in addition, a putative locus on chromosome 1. Gene-set enrichment analyses showed that the“COPI-mediated anterograde transport” gene-set was associated with MIC-1/GDF15 blood concentration with marginal significance after FDR correction (p = 0.067). In conclusion, a locus on chromosome 19 was associated with MIC-1/GDF-15 blood concentration with genome-wide significance, with evidence for a new locus (chromosome 1). Future studies using independent cohorts are needed to confirm the observed associations especially for the chromosomes 1 locus, and to further investigate and identify the causal SNPs that contribute to MIC-1/GDF-15 levels.

An international meta-analysis confirms the association of BNC2 with adolescent idiopathic scoliosis

Adolescent idiopathic scoliosis (AIS) is a common spinal deformity with the prevalence of approximately 3%. We previously conducted a genome-wide association study (GWAS) using a Japanese cohort and identified a novel locus on chromosome 9p22.2. However, a replication study using multi-population cohorts has not been conducted. To confirm the association of 9p22.2 locus with AIS in multi-ethnic populations, we conducted international meta-analysis using eight cohorts. In total, we analyzed 8,756 cases and 27,822 controls. The analysis showed a convincing evidence of association between rs3904778 and AIS. Seven out of eight cohorts had significant P value, and remaining one cohort also had the same trend as the seven. The combined P was 3.28 × 10⁻¹⁸ (odds ratio = 1.19, 95% confidence interval = 1.14–1.24). In silico analyses suggested that BNC2 is the AIS susceptibility gene in this locus.

Genetic Contributions to The Association Between Adult Height and Head and Neck Cancer: A Mendelian Randomization Analysis

With the aim to dissect the effect of adult height on head and neck cancer (HNC), we use the Mendelian randomization (MR) approach to test the association between genetic instruments for height and the risk of HNC. 599 single nucleotide polymorphisms (SNPs) were identified as genetic instruments for height, accounting for 16% of the phenotypic variation. Genetic data concerning HNC cases and controls were obtained from a genome-wide association study. Summary statistics for genetic association were used in complementary MR approaches: the weighted genetic risk score (GRS) and the inverse-variance weighted (IVW). MR-Egger regression was used for sensitivity analysis and pleiotropy evaluation. From the GRS analysis, one standard deviation (SD) higher height (6.9 cm; due to genetic predisposition across 599 SNPs) raised the risk for HNC (Odds ratio (OR), 1.14; 95% Confidence Interval (95%CI), 0.99-1.32). The association analyses with potential confounders revealed that the GRS was associated with tobacco smoking (OR = 0.80, 95% CI (0.69-0.93)). MR-Egger regression did not provide evidence of overall directional pleiotropy. Our study indicates that height is potentially associated with HNC risk. However, the reported risk could be underestimated since, at the genetic level, height emerged to be inversely associated with smoking.

Efficient genome-wide genotyping strategies and data integration in crop plants

Next-generation sequencing (NGS) has revolutionized plant and animal research by providing powerful genotyping methods. This review describes and discusses the advantages, challenges and, most importantly, solutions to facilitate data processing, the handling of missing data, and cross-platform data integration. Next-generation sequencing technologies provide powerful and flexible genotyping methods to plant breeders and researchers. These methods offer a wide range of applications from genome-wide analysis to routine screening with a high level of accuracy and reproducibility. Furthermore, they provide a straightforward workflow to identify, validate, and screen genetic variants in a short time with a low cost. NGS-based genotyping methods include whole-genome re-sequencing, SNP arrays, and reduced representation sequencing, which are widely applied in crops. The main challenges facing breeders and geneticists today is how to choose an appropriate genotyping method and how to integrate genotyping data sets obtained from various sources. Here, we review and discuss the advantages and challenges of several NGS methods for genome-wide genetic marker development and genotyping in crop plants. We also discuss how imputation methods can be used to both fill in missing data in genotypic data sets and to integrate data sets obtained using different genotyping tools. It is our hope that this synthetic view of genotyping methods will help geneticists and breeders to integrate these NGS-based methods in crop plant breeding and research.

Trans-ethnic analysis of metabochip data identifies two new loci associated with BMI

OBJECTIVE

Body mass index (BMI) is commonly used to assess obesity, which is associated with numerous diseases and negative health outcomes. BMI has been shown to be a heritable, polygenic trait, with close to 100 loci previously identified and replicated in multiple populations. We aim to replicate known BMI loci and identify novel associations in a trans-ethnic study population.

SUBJECTS

Using eligible participants from the Population Architecture using Genomics and Epidemiology consortium, we conducted a trans-ethnic meta-analysis of 102 514 African Americans, Hispanics, Asian/Native Hawaiian, Native Americans and European Americans. Participants were genotyped on over 200 000 SNPs on the Illumina Metabochip custom array, or imputed into the 1000 Genomes Project (Phase I). Linear regression of the natural log of BMI, adjusting for age, sex, study site (if applicable), and ancestry principal components, was conducted for each race/ethnicity within each study cohort. Race/ethnicity-specific, and combined meta-analyses used fixed-effects models.

RESULTS

We replicated 15 of 21 BMI loci included on the Metabochip, and identified two novel BMI loci at 1q41 (rs2820436) and 2q31.1 (rs10930502) at the Metabochip-wide significance threshold (P<2.5 × 10-7). Bioinformatic functional investigation of SNPs at these loci suggests a possible impact on pathways that regulate metabolism and adipose tissue.

CONCLUSION

Conducting studies in genetically diverse populations continues to be a valuable strategy for replicating known loci and uncovering novel BMI associations.

Endometriosis risk alleles at 1p36.12 act through inverse regulation of CDC42 and LINC00339

Genome-wide association studies (GWAS) have identified markers within the WNT4 region on chromosome 1p36.12 showing consistent and strong association with increasing endometriosis risk. Fine mapping using sequence and imputed genotype data has revealed strong candidates for the causal SNPs within these critical regions; however, the molecular pathogenesis of these SNPs is currently unknown. We used gene expression data collected from whole blood from 862 individuals and endometrial tissue from 136 individuals from independent populations of European descent to examine the mechanism underlying endometriosis susceptibility. Association mapping results from 7,090 individuals (2,594 cases and 4,496 controls) supported rs3820282 as the SNP with the strongest association for endometriosis risk (P = 1.84 × 10−5, OR = 1.244 (1.126-1.375)). SNP rs3820282 is a significant eQTL in whole blood decreasing expression of LINC00339 (also known as HSPC157) and increasing expression of CDC42 (P = 2.0 ×10−54 and 4.5x10−4 respectively). The largest effects were for two LINC00339 probes (P = 2.0 ×10−54; 1.0 × 10−34). The eQTL for LINC00339 was also observed in endometrial tissue (P = 2.4 ×10−8) with the same direction of effect for both whole blood and endometrial tissue. There was no evidence for eQTL effects for WNT4. Chromatin conformation capture provides evidence for risk SNPs interacting with the promoters of both LINC00339 and CDC4 and luciferase reporter assays suggest the risk SNP rs12038474 is located in a transcriptional silencer for CDC42 and the risk allele increases expression of CDC42. However, no effect of rs3820282 was observed in the LINC00339 expression in Ishikawa cells. Taken together, our results suggest that SNPs increasing endometriosis risk in this region act through CDC42, but further functional studies are required to rule out inverse regulation of both LINC00339 and CDC42.

Alterations in cholesterol metabolism-related genes in sporadic Alzheimer's disease

Genome-wide association studies have identified several cholesterol metabolism-related genes as top risk factors for late-onset Alzheimer's disease (LOAD). We hypothesized that specific genetic variants could act as disease-modifying factors by altering the expression of those genes. Targeted association studies were conducted with available genomic, transcriptomic, proteomic, and histopathological data from 3 independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Quebec Founder Population (QFP), and the United Kingdom Brain Expression Consortium (UKBEC). First, a total of 273 polymorphisms located in 17 cholesterol metabolism-related loci were screened for associations with cerebrospinal fluid LOAD biomarkers beta amyloid, phosphorylated tau, and tau (from the ADNI) and with amyloid plaque and tangle densities (from the QFP). Top polymorphisms were then contrasted with gene expression levels measured in 134 autopsied healthy brains (from the UKBEC). In the end, only SREBF2 polymorphism rs2269657 showed significant dual associations with LOAD pathological biomarkers and gene expression levels. Furthermore, SREBF2 expression levels measured in LOAD frontal cortices inversely correlated with age at death; suggesting a possible influence on survival rate.

Risk estimation model for nonalcoholic fatty liver disease in the Japanese using multiple genetic markers

The genetic factors affecting the natural history of nonalcoholic fatty liver disease (NAFLD), including the development of nonalcoholic steatohepatitis (NASH) and NASH-derived hepatocellular carcinoma (NASH-HCC), are still unknown. In the current study, we sought to identify genetic factors related to the development of NAFLD, NASH, and NASH-HCC, and to establish risk-estimation models for them. For these purposes, 936 histologically proven NAFLD patients were recruited, and genome-wide association (GWA) studies were conducted for 902, including 476 NASH and 58 NASH-HCC patients, against 7,672 general-population controls. Risk estimations for NAFLD and NASH were then performed using the SNPs identified as having significant associations in the GWA studies. We found that rs2896019 in PNPLA3 [p = 2.3x10^-31, OR (95%CI) = 1.85 (1.67–2.05)], rs1260326 in GCKR [p = 9.6x10^-10, OR (95%CI) = 1.38(1.25–1.53)], and rs4808199 in GATAD2A [p = 2.3x10^-8, OR (95%CI) = 1.37 (1.23–1.53)] were significantly associated with NAFLD. Notably, the number of risk alleles in PNPLA3 and GATAD2A was much higher in Matteoni type 4 (NASH) patients than in type 1, type 2, and type 3 NAFLD patients. In addition, we newly identified rs17007417 in DYSF [p = 5.2x10^-7, OR (95%CI) = 2.74 (1.84–4.06)] as a SNP associated with NASH-HCC. Rs641738 in TMC4, which showed association with NAFLD in patients of European descent, was not replicated in our study (p = 0.73), although the complicated LD pattern in the region suggests the necessity for further investigation. The genetic variants of PNPLA3, GCKR, and GATAD2A were then used to estimate the risk for NAFLD. The obtained Polygenic Risk Scores showed that the risk for NAFLD increased with the accumulation of risk alleles [AUC (95%CI) = 0.65 (0.63–0.67)]. Conclusions: We demonstrated that NASH is genetically and clinically different from the other NAFLD subgroups. We also established risk-estimation models for NAFLD and NASH using multiple genetic markers. These models can be used to improve the accuracy of NAFLD diagnosis and to guide treatment decisions for patients.

GWAS of epigenetic aging rates in blood reveals a critical role for TERT

DNA methylation age is an accurate biomarker of chronological age and predicts lifespan, but its underlying molecular mechanisms are unknown. In this genome-wide association study of 9907 individuals, we find gene variants mapping to five loci associated with intrinsic epigenetic age acceleration (IEAA) and gene variants in three loci associated with extrinsic epigenetic age acceleration (EEAA). Mendelian randomization analysis suggests causal influences of menarche and menopause on IEAA and lipoproteins on IEAA and EEAA. Variants associated with longer leukocyte telomere length (LTL) in the telomerase reverse transcriptase gene (TERT) paradoxically confer higher IEAA (P < 2.7 × 10-11). Causal modeling indicates TERT-specific and independent effects on LTL and IEAA. Experimental hTERT-expression in primary human fibroblasts engenders a linear increase in DNA methylation age with cell population doubling number. Together, these findings indicate a critical role for hTERT in regulating the epigenetic clock, in addition to its established role of compensating for cell replication-dependent telomere shortening.

Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy

Background

Accurate imputation plays a major role in genomic studies of livestock industries, where the number of genotyped or sequenced animals is limited by costs. This study explored methods to create an ideal reference population for imputation to Next Generation Sequencing data in cattle.

Methods

Methods for clustering of animals for imputation were explored, using 1000 Bull Genomes Project sequence data on 1146 animals from a variety of beef and dairy breeds. Imputation from 50 K to 777 K was first carried out to choose an ideal clustering method, using ADMIXTURE or PLINK clustering algorithms with either genotypes or reconstructed haplotypes.

Results

Due to efficiency, accuracy and ease of use, clustering with PLINK using haplotypes as quasi-genotypes was chosen as the most advantageous grouping method. It was found that using a clustered population slightly decreased computing time, while maintaining accuracy across the population. Although overall accuracy remained the same, a slight increase in accuracy was observed for groups of animals in some breeds (primarily purebred beef cattle from breeds with fewer sequenced animals) and for other groups, primarily crossbreed animals, a slight decrease in accuracy was observed. However, it was noted that some animals in each breed were poorly imputed across all methods. When imputed sequences were included in the reference population to aid imputation of poorly imputed animals, a small increase in overall accuracy was observed for nearly every individual in the population. Two models were created to predict imputation accuracy, a complete model using all information available including Euclidean distances from genotypes and haplotypes, pedigree information, and clustering groups and a simple model using only breed and an Euclidean distance matrix as predictors. Both models were successful in predicting imputation accuracy, with correlations between predicted and true imputation accuracy as measured by concordance rate of 0.87 and 0.83, respectively.

Conclusions

A clustering methodology can be very useful to subgroup cattle for efficient genotype imputation. In addition, accuracy of genotype imputation from medium to high-density Single Nucleotide Polymorphisms (SNP) chip panels to whole-genome sequence can be predicted well using a simple linear model defined in this study.

Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks

We examined common variation in asthma risk by conducting a meta-analysis of worldwide asthma genome-wide association studies (23,948 cases, 118,538 controls) from ethnically-diverse populations. We identified five new asthma loci, uncovered two additional novel associations at two known asthma loci, established asthma associations at two loci implicated previously in comorbidity of asthma plus hay fever, and confirmed nine known loci. Investigation of pleiotropy showed large overlaps in genetic variants with autoimmune and inflammatory diseases. Enrichment of asthma risk loci in enhancer marks, especially in immune cells, suggests a major role of these loci in the regulation of immune-related mechanisms.

Parent-progeny imputation from pooled samples for cost-efficient genotyping in plant breeding

The increased usage of whole-genome selection (WGS) and other molecular evaluation methods in plant breeding relies on the ability to genotype a very large number of untested individuals in each breeding cycle. Many plant breeding programs evaluate large biparental populations of homozygous individuals derived from homozygous parent inbred lines. This structure lends itself to parent-progeny imputation, which transfers the genotype scores of the parents to progeny individuals that are genotyped for a much smaller number of loci. Here we introduce a parent-progeny imputation method that infers individual genotypes from non-barcoded pooled samples of DNA of multiple individuals using a Hidden Markov Model (HMM). We demonstrate the method for pools of simulated maize double haploids (DH) from biparental populations, genotyped using a genotyping by sequencing (GBS) approach for 3,000 loci at 0.125x to 4x coverage. We observed high concordance between true and imputed marker scores and the HMM produced well-calibrated genotype probabilities that correctly reflected the uncertainty of the imputed scores. Genomic estimated breeding values (GEBV) calculated from the imputed scores closely matched GEBV calculated from the true marker scores. The within-population correlation between these sets of GEBV approached 0.95 at 1x and 4x coverage when pooling two or four individuals, respectively. Our approach can reduce the genotyping cost per individual by a factor up to the number of pooled individuals in GBS applications without the need for extra sequencing coverage, thereby enabling cost-effective large scale genotyping for applications such as WGS in plant breeding.

CELSR2 is a candidate susceptibility gene in idiopathic scoliosis

A Swedish pedigree with an autosomal dominant inheritance of idiopathic scoliosis was initially studied by genetic linkage analysis, prioritising genomic regions for further analysis. This revealed a locus on chromosome 1 with a putative risk haplotype shared by all affected individuals. Two affected individuals were subsequently exome-sequenced, identifying a rare, non-synonymous variant in the CELSR2 gene. This variant is rs141489111, a c.G6859A change in exon 21 (NM_001408), leading to a predicted p.V2287I (NP_001399.1) change. This variant was found in all affected members of the pedigree, but showed reduced penetrance. Analysis of tagging variants in CELSR1-3 in a set of 1739 Swedish-Danish scoliosis cases and 1812 controls revealed significant association (p = 0.0001) to rs2281894, a common synonymous variant in CELSR2. This association was not replicated in case-control cohorts from Japan and the US. No association was found to variants in CELSR1 or CELSR3. Our findings suggest a rare variant in CELSR2 as causative for idiopathic scoliosis in a family with dominant segregation and further highlight common variation in CELSR2 in general susceptibility to idiopathic scoliosis in the Swedish-Danish population. Both variants are located in the highly conserved GAIN protein domain, which is necessary for the auto-proteolysis of CELSR2, suggesting its functional importance.

Associations of adult genetic risk scores for adiposity with childhood abdominal, liver and pericardial fat assessed by magnetic resonance imaging

Background

Genome-wide association studies (GWAS) identified single nucleotide polymorphisms (SNPs) involved in adult fat distribution. Whether these SNPs also affect abdominal and organ-specific fat accumulation in children is unknown.

Methods

In a population-based prospective cohort study among 1 995 children (median age: 9.8 years, 95% range 9.4;10.8), We tested the associations of six genetic risk scores based on previously identified SNPs for childhood BMI, adult BMI, liver fat, WHR, pericardial fat mass, visceral- and subcutaneous adipose tissue ratio (VAT/SAT ratio), and four individual SAT and VAT associated SNPs, for association with SAT (N=1 746), VAT (N=1 742), VAT/SAT ratio (N=1 738), liver fat fraction (N=1 950), and pericardial fat mass (N=1 803) measured by Magnetic Resonance Imaging.

Results

Per additional risk allele in the childhood BMI genetic risk score, SAT increased 0.020 standard deviation scores (SDS), (95% confidence interval (CI) 0.009;0.031, p-value:3.28*10^-4) and VAT increased 0.021 SDS, 95% CI:0.009;0.032, p-value:4.68*10^-4). The adult BMI risk score was positively associated with SAT (0.022 SDS increase, CI:0.015;0.029, p-value:1.33*10^-9), VAT (0.017 SDS increase, CI:0.010;0.025, p-value:7.00*10^-6), and negatively with VAT/SAT ratio (-0.012 SDS decrease, CI:-0.019;-0.006, p-value:2.88*10^-4). The liver fat risk score was associated with liver fat fraction (0.121 SDS, CI:0.086;0.157, p-value:2.65*10^-11). Rs7185735 (SAT), was associated with SAT (0.151 SDS, CI:0.087;0.214, p-value:3.00*10^-6) and VAT/SAT ratio (-0.126 SDS, CI:-0.186;-0.065, p-value:4.70*10^-5). After stratification by sex the associations of the adult BMI risk score with SAT and VAT and of the liver fat risk score with liver fat fraction remained in both sexes. Associations of the childhood BMI risk score with SAT, and the adult BMI risk score with VAT/SAT ratio were present among boys only, whereas the association of the pericardial fat risk score with pericardial fat was present among girls only.

Conclusion

Genetic variants associated with BMI, body fat distribution, liver and pericardial fat already affect body fat distribution in childhood.

Gene expression and linkage analysis implicate CBLB as a mediator of rituximab resistance

Elucidating resistance mechanisms for therapeutic monoclonal antibodies (MAbs) is challenging, because they are difficult to study in non-human models. We therefore developed a strategy to genetically map in vitro drug sensitivity, identifying genes that alter responsiveness to rituximab, a therapeutic anti-CD20 MAb that provides significant benefit to patients with B-cell malignancies. We discovered novel loci with genome-wide mapping analyses and functionally validated one of these genes, CBLB, which causes rituximab resistance when knocked down in lymphoma cells. This study demonstrates the utility of genome-wide mapping to discover novel biological mechanisms of potential clinical advantage.

Admixture on the northern front: population genomics of range expansion in the white-footed mouse (Peromyscus leucopus) and secondary contact with the deer mouse (Peromyscus maniculatus)

Range expansion has genetic consequences expected to result in differentiated wave-front populations with low genetic variation and potentially introgression from a local species. The northern expansion of Peromyscus leucopus in southern Quebec provides an opportunity to test these predictions using population genomic tools. Our results show evidence of recent and post-glacial expansion. Genome-wide variation in P. leucopus indicates two post-glacial lineages are separated by the St. Lawrence River, with a more recent divergence of populations isolated by the Richelieu River. In two of three transects we documented northern populations with low diversity in at least one genetic measure, although most relationships were not significant. Consistent with bottlenecks and allele surfing during northward expansion, we document a northern-most population with low nucleotide diversity, divergent allele frequencies and the most private alleles, and observed heterozygosity indicates outcrossing. Ancestry proportions revealed putative hybrids of P. leucopus and P. maniculatus. A formal test for gene flow confirmed secondary contact, showing that a reticulate population phylogeny between P. maniculatus and P. leucopus was a better fit to the data than a bifurcating model without gene flow. Thus, we provide the first genomic evidence of gene flow between this pair of species in natural populations. Understanding the evolutionary consequences of secondary contact is an important conservation concern as climate-induced range expansions are expected to result in new hybrid zones between closely related species.

Shared genetic etiology of hypertension and stroke: evidence from bioinformatics analysis of genome-wide association studies

Hypertension is the most significant modifiable risk factor for cerebrovascular disease. It has been estimated that about 54% of strokes worldwide can be attributed to hypertension. However, there has not been a systematic study assessing the shared genetic susceptibility to hypertension and stroke on a genome-wide level. In this study, SNPs associated with essential hypertension and stroke were collected from the NHGRI-EBI GWAS catalog, and genotype imputation were conducted using information from the 1000 Genomes Project. Subsequently, the SNPs and the mapped genes were compared between the two diseases. Finally, functional clustering was performed, and the enriched GO terms and KEGG pathways were further compared between hypertension and stroke. Comparison of these two groups of SNPs and genes identified only one shared SNP (rs3184504) and 11 shared genes. After genotype imputation, 129 shared SNPs and 16 shared genes were identified. These genes were significantly enriched in 10 GO terms, which were mainly involved in lipoprotein and triglyceride metabolism. Additionally, KEGG analysis identified one pathway, glycerolipid metabolism, as being significantly enriched in both diseases. The present study strongly suggests that the gene network regulating lipid metabolism and blood circulation is the major shared genetic etiology of hypertension and stroke.

Genome-wide Association Study of Idiopathic Osteonecrosis of the Femoral Head

Idiopathic osteonecrosis of the femoral head (IONFH) is an ischemic disorder that causes bone necrosis of the femoral head, resulting in hip joint dysfunction. IONFH is a polygenic disease and steroid and alcohol have already known to increase its risk; however, the mechanism of IONFH remains to be elucidated. We performed a genome-wide association study using ~60,000 subjects and found two novel loci on chromosome 20q12 and 12q24. Big data analyses identified LINC01370 as a candidate susceptibility gene in the 20q12 locus. Stratified analysis by IONFH risk factors suggested that the 12q24 locus was associated with IONFH through drinking capacity. Our findings would shed new light on pathophysiology of IONFH.

Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle

Background

Our aim was to identify genomic regions via genome-wide association studies (GWAS) to improve the predictability of genetic merit in Holsteins for 10 calving and 28 body conformation traits. Animals were genotyped using the Illumina Bovine 50 K BeadChip and imputed to the Illumina BovineHD BeadChip (HD). GWAS were performed on 601,717 real and imputed single nucleotide polymorphism (SNP) genotypes using a single-SNP mixed linear model on 4841 Holstein bulls with breeding value predictions and followed by gene identification and in silico functional analyses. The association results were further validated using five scenarios with different numbers of SNPs.

Results

Seven hundred and eighty-two SNPs were significantly associated with calving performance at a genome-wise false discovery rate (FDR) of 5%. Most of these significant SNPs were on chromosomes 18 (71.9%), 17 (7.4%), 5 (6.8%) and 7 (2.4%) and mapped to 675 genes, among which 142 included at least one significant SNP and 532 were nearby one (100 kbp). For body conformation traits, 607 SNPs were significant at a genome-wise FDR of 5% and most of them were located on chromosomes 5 (30%), 18 (27%), 20 (13%), 6 (6%), 7 (5%), 14 (5%) and 13 (3%). SNP enrichment functional analyses for calving traits at a FDR of 1% suggested potential biological processes including musculoskeletal movement, meiotic cell cycle, oocyte maturation and skeletal muscle contraction. Furthermore, pathway analyses suggested potential pathways associated with calving performance traits including tight junction, oxytocin signaling, and MAPK signaling (P < 0.10). The prediction ability of the 1206 significant SNPs was between 78 and 83% of the prediction ability of the BovineSNP50 SNPs for calving performance traits and between 35 and 79% for body conformation traits.

Conclusions

Various SNPs that are significantly associated with calving performance are located within or nearby genes with potential roles in tight junction, oxytocin signaling, and MAPK signaling. Combining the significant SNPs or SNPs within or nearby gene(s) from the HD panel with the BovineSNP50 panel yielded a marginal increase in the accuracy of prediction of genomic estimated breeding values for all traits compared to the use of the BovineSNP50 panel alone.

Electronic supplementary material

The online version of this article (10.1186/s12711-017-0356-8) contains supplementary material, which is available to authorized users.

Peripheral Arterial Disease Genetics: Progress to Date and Challenges Ahead

PURPOSE OF REVIEW

In this paper, we review the progress made thus far in research related to the genetics of peripheral arterial disease (PAD) by detailing efforts to date in heritability, linkage analyses, and candidate gene studies. We further summarize more contemporary genome-wide association studies (GWAS) and epigenetic studies of PAD. Finally, we review current challenges and future avenues of advanced research in PAD genetics including whole genome sequencing studies.

RECENT FINDINGS

Studies have estimated the heritability of PAD to be moderate, though the contribution to this heritability that is independent of traditional cardiovascular risk factors remains unclear. Recent efforts have identified SNPs associated with PAD in GWAS analyses, but these have yet to be replicated in independent studies. Much remains to be discovered in the field of PAD genetics. An improved understanding of the genetic foundation for PAD will allow for earlier diagnosis of disease and a more complete pathophysiological understanding of the mechanisms of the disease leading to novel therapeutic interventions. Future avenues for success will likely arise from very large-scale GWAS, whole genome sequencing, and epigenetic studies involving very well-characterized cohorts.

Genomewide Association Scan of a Mortality Associated Endophenotype for a Long and Healthy Life in the Long Life Family Study

Background

Identification of genes or fundamental biological pathways that regulate aging phenotypes and longevity could lead to possible interventions to increase healthy longevity.

Methods

Using data from the Long Life Family Study, we performed genomewide association analyses on an endophenotype construct, LF1, comprising a linear combination of traits across health domains. LF1 primarily reflected traits from the pulmonary and physical activity domains.

Results

We detected a significant association between LF1 and a locus on chromosome 10p15 (p-value = 4.65 × 10-8) and suggestive evidence (p-value < 5 × 10-6) for association on chromosomes 1, 2, 8, 12, 15, 18, and 22. Using data from the Health, Aging and Body Composition Study, we subsequently replicated the association for the 1p13 region near the NBPF6 locus (p-value = 3.65 × 10-4).

Conclusions

Our analyses indicate that loci influencing a healthy aging endophenotype construct predominantly comprised of pulmonary and physical function domains may be located on chromosome 1p13 near the NBPF6 locus. Further investigation of this possible locus and other suggestive loci may reveal novel biological pathways that influence healthy aging.

A novel role for ciliary function in atopy: ADGRV1 and DNAH5 interactions

BACKGROUND

Atopy, an endotype underlying allergic diseases, has a substantial genetic component.

OBJECTIVE

Our goal was to identify novel genes associated with atopy in asthma-ascertained families.

METHODS

We implemented a 3-step analysis strategy in 3 data sets: the Epidemiological Study on the Genetics and Environment of Asthma (EGEA) data set (1660 subjects), the Saguenay-Lac-Saint-Jean study data set (1138 subjects), and the Medical Research Council (MRC) data set (446 subjects). This strategy included a single nucleotide polymorphism (SNP) genome-wide association study (GWAS), the selection of related gene pairs based on statistical filtering of GWAS results, and text-mining filtering using Gene Relationships Across Implicated Loci and SNP-SNP interaction analysis of selected gene pairs.

RESULTS

We identified the 5q14 locus, harboring the adhesion G protein-coupled receptor V1 (ADGRV1) gene, which showed genome-wide significant association with atopy (rs4916831, meta-analysis P value = 6.8 × 10^-9). Statistical filtering of GWAS results followed by text-mining filtering revealed relationships between ADGRV1 and 3 genes showing suggestive association with atopy (P ≤ 10^-4). SNP-SNP interaction analysis between ADGRV1 and these 3 genes showed significant interaction between ADGRV1 rs17554723 and 2 correlated SNPs (rs2134256 and rs1354187) within the dynein axonemal heavy chain 5 (DNAH5) gene (P_meta-int = 3.6 × 10^-5 and 6.1 × 10^-5, which met the multiple-testing corrected threshold of 7.3 × 10^-5). Further conditional analysis indicated that rs2134256 alone accounted for the interaction signal with rs17554723.

CONCLUSION

Because both DNAH5 and ADGRV1 contribute to ciliary function, this study suggests that ciliary dysfunction might represent a novel mechanism underlying atopy. Combining GWAS and epistasis analysis driven by statistical and knowledge-based evidence represents a promising approach for identifying new genes involved in complex traits.

Genome-wide association study with additional genetic and post-transcriptional analyses reveals novel regulators of plasma factor XI levels

Coagulation factor XI (FXI) has become increasingly interesting for its role in pathogenesis of thrombosis. While elevated plasma levels of FXI have been associated with venous thromboembolism and ischemic stroke, its deficiency is associated with mild bleeding. We aimed to determine novel genetic and post-transcriptional plasma FXI regulators.We performed a genome-wide association study (GWAS) for plasma FXI levels, using novel data imputed to the 1000 Genomes reference panel. Individual GWAS analyses, including a total of 16,169 European individuals from the ARIC, GHS, MARTHA and PROCARDIS studies, were meta-analysed and further replicated in 2,045 individuals from the F5L family, GAIT2 and MEGA studies. Additional association with activated partial thromboplastin time (aPTT) was tested for the top SNPs. In addition, a study on the effect of miRNA on FXI regulation was performed using in silico prediction tools and in vitro luciferase assays.Three loci showed robust, replicating association with circulating FXI levels: KNG1 (rs710446, P-value = 2.07 × 10-302), F11 (rs4253417, P-value  = 2.86 × 10-193), and a novel association in GCKR (rs780094, P-value  = 3.56 ×10-09), here for the first time implicated in FXI regulation. The two first SNPs (rs710446 and rs4253417) also associated with aPTT. Conditional and haplotype analyses demonstrated a complex association signal, with additional novel SNPs modulating plasma FXI levels in both the F11 and KNG1 loci. Finally, eight miRNAs were predicted to bind F11 mRNA. Over-expression of either miR-145 or miR-181 significantly reduced the luciferase activity in cells transfected with a plasmid containing FXI-3'UTR.These results should open the door to new therapeutic targets for thrombosis prevention.

Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels-1000 Genomes phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the population-based Nord Trøndelag Health Study (HUNT) from low-pass genome sequencing. We observed that the population-matched reference panel allowed for imputation of more population-specific variants with lower frequency (minor allele frequency (MAF) between 0.05% and 0.5%). The overall imputation accuracy from the population-specific panel was substantially higher than 1000G and was comparable with HRC, despite HRC being 15-fold larger. These results recapitulate the value of population-specific reference panels for genotype imputation. We also evaluated different strategies to utilize multiple sets of imputed genotypes to increase the power of association studies. We observed that testing association for all variants imputed from any panel results in higher power to detect association than the alternative strategy of including only one version of each genetic variant, selected for having the highest imputation quality metric. This was particularly true for lower frequency variants (MAF < 1%), even after adjusting for the additional multiple testing burden.

Pharmacogenetic meta-analysis of baseline risk factors, pharmacodynamic, efficacy and tolerability endpoints from two large global cardiovascular outcomes trials for darapladib

Darapladib, a lipoprotein-associated phospholipase A2 (Lp-PLA₂) inhibitor, failed to demonstrate efficacy for the primary endpoints in two large phase III cardiovascular outcomes trials, one in stable coronary heart disease patients (STABILITY) and one in acute coronary syndrome (SOLID-TIMI 52). No major safety signals were observed but tolerability issues of diarrhea and odor were common (up to 13%). We hypothesized that genetic variants associated with Lp-PLA₂ activity may influence efficacy and tolerability and therefore performed a comprehensive pharmacogenetic analysis of both trials. We genotyped patients within the STABILITY and SOLID-TIMI 52 trials who provided a DNA sample and consent (n = 13,577 and 10,404 respectively, representing 86% and 82% of the trial participants) using genome-wide arrays with exome content and performed imputation using a 1000 Genomes reference panel. We investigated baseline and change from baseline in Lp-PLA₂ activity, two efficacy endpoints (major coronary events and myocardial infarction) as well as tolerability parameters at genome-wide and candidate gene level using a meta-analytic approach. We replicated associations of published loci on baseline Lp-PLA₂ activity (APOE, CELSR2, LPA, PLA2G7, LDLR and SCARB1) and identified three novel loci (TOMM5, FRMD5 and LPL) using the GWAS-significance threshold P≤5E-08. Review of the PLA2G7 gene (encoding Lp-PLA₂) within these datasets identified V279F null allele carriers as well as three other rare exonic null alleles within various ethnic groups, however none of these variants nor any other loci associated with Lp-PLA₂ activity at baseline were associated with any of the drug response endpoints. The analysis of darapladib efficacy endpoints, despite low power, identified six low frequency loci with main genotype effect (though with borderline imputation scores) and one common locus (minor allele frequency 0.24) with genotype by treatment interaction effect passing the GWAS-significance threshold. This locus conferred risk in placebo subjects, hazard ratio (HR) 1.22 with 95% confidence interval (CI) 1.11–1.33, but was protective in darapladib subjects, HR 0.79 (95% CI 0.71–0.88). No major loci for tolerability were found. Thus, genetic analysis confirmed and extended the influence of lipoprotein loci on Lp-PLA₂ levels, identified some novel null alleles in the PLA2G7 gene, and only identified one potentially efficacious subgroup within these two large clinical trials.

LinkImputeR: user-guided genotype calling and imputation for non-model organisms

Background

Genomic studies such as genome-wide association and genomic selection require genome-wide genotype data. All existing technologies used to create these data result in missing genotypes, which are often then inferred using genotype imputation software. However, existing imputation methods most often make use only of genotypes that are successfully inferred after having passed a certain read depth threshold. Because of this, any read information for genotypes that did not pass the threshold, and were thus set to missing, is ignored. Most genomic studies also choose read depth thresholds and quality filters without investigating their effects on the size and quality of the resulting genotype data. Moreover, almost all genotype imputation methods require ordered markers and are therefore of limited utility in non-model organisms.

Results

Here we introduce LinkImputeR, a software program that exploits the read count information that is normally ignored, and makes use of all available DNA sequence information for the purposes of genotype calling and imputation. It is specifically designed for non-model organisms since it requires neither ordered markers nor a reference panel of genotypes. Using next-generation DNA sequence (NGS) data from apple, cannabis and grape, we quantify the effect of varying read count and missingness thresholds on the quantity and quality of genotypes generated from LinkImputeR. We demonstrate that LinkImputeR can increase the number of genotype calls by more than an order of magnitude, can improve genotyping accuracy by several percent and can thus improve the power of downstream analyses. Moreover, we show that the effects of quality and read depth filters can differ substantially between data sets and should therefore be investigated on a per-study basis.

Conclusions

By exploiting DNA sequence data that is normally ignored during genotype calling and imputation, LinkImputeR can significantly improve both the quantity and quality of genotype data generated from NGS technologies. It enables the user to quickly and easily examine the effects of varying thresholds and filters on the number and quality of the resulting genotype calls. In this manner, users can decide on thresholds that are most suitable for their purposes. We show that LinkImputeR can significantly augment the value and utility of NGS data sets, especially in non-model organisms with poor genomic resources.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3873-5) contains supplementary material, which is available to authorized users.