A unified framework for multi-locus association analysis of both common and rare variants

The cytoplasmic peptide:N-glycanase (NGLY1) - Structure, expression and cellular functions

NGLY1/Ngly1 is a cytosolic peptide:N-glycanase, i.e. de-N-glycosylating enzyme acting on N-glycoproteins in mammals, generating free, unconjugated N-glycans and deglycosylated peptides in which the N-glycosylated asparagine residues are converted to aspartates. This enzyme is known to be involved in the quality control system for the newly synthesized glycoproteins in the endoplasmic reticulum (ER). In this system, misfolded (glyco)proteins are retrotranslocated to the cytosol, where the 26S proteasomes play a central role in degrading the proteins: a process referred to as ER-associated degradation or ERAD in short. PNGase-mediated deglycosylation is believed to facilitate the efficient degradation of some misfolded glycoproteins. Human patients harboring mutations of NGLY1 gene (NGLY1-deficiency) have recently been discovered, clearly indicating the functional importance of this enzyme. This review summarizes the current state of our knowledge on NGLY1 and its gene product in mammalian cells.

Accounting for linkage disequilibrium in association analysis of diverse populations

The National Human Genome Research Institute's catalog of published genome-wide association studies (GWAS) lists over 10,000 genetic variants collectively associated with over 800 human diseases or traits. Most of these GWAS have been conducted in European-ancestry populations. Findings gleaned from these studies have led to identification of disease-associated loci and biologic pathways involved in disease etiology. In multiple instances, these genomic findings have led to the development of novel medical therapies or evidence for prescribing a given drug as the appropriate treatment for a given individual beyond phenotypic appearances or socially defined constructs of race or ethnicity. Such findings have implications for populations throughout the globe and GWAS are increasingly being conducted in more diverse populations. A major challenge for investigators seeking to follow up genomic findings between diverse populations is discordant patterns of linkage disequilibrium (LD). We provide an overview of common measures of LD and opportunities for their use in novel methods designed to address challenges associated with following up GWAS conducted in European-ancestry populations in African-ancestry populations or, more generally, between populations with discordant LD patterns. We detail the strengths and weaknesses associated with different approaches. We also describe application of these strategies in follow-up studies of populations with concordant LD patterns (replication) or discordant LD patterns (transferability) as well as fine-mapping studies. We review application of these methods to a variety of traits and diseases.

SITDEM: a simulation tool for disease/endpoint models of association studies based on single nucleotide polymorphism genotypes

The association analysis between single nucleotide polymorphisms (SNPs) and disease or endpoint in genome-wide association studies (GWAS) has been considered as a powerful strategy for investigating genetic susceptibility and for identifying significant biomarkers. The statistical analysis approaches with simulated data have been widely used to review experimental designs and performance measurements. In recent years, a number of authors have proposed methods for the simulation of biological data in the genomic field. However, these methods use large-scale genomic data as a reference to simulate experiments, which may limit the use of the methods in the case where the data in specific studies are not available. Few methods use experimental results or observed parameters for simulation. The goal of this study is to develop a Web application called SITDEM to simulate disease/endpoint models in three different approaches based on only parameters observed in GWAS. In our simulation, a key task is to compute the probability of genotypes. Based on that, we randomly sample simulation data. Simulation results are shown as a function of p-value against odds ratio or relative risk of a SNP in dominant and recessive models. Our simulation results show the potential of SITDEM for simulating genotype data. SITDEM could be particularly useful for investigating the relationship among observed parameters for target SNPs and for estimating the number of variables (SNPs) required to result in significant p-values in multiple comparisons. The proposed simulation tool is freely available at http://www.snpmodel.com.

Regional replication of association with refractive error on 15q14 and 15q25 in the Age-Related Eye Disease Study cohort

PURPOSE

Refractive error is a complex trait with multiple genetic and environmental risk factors, and is the most common cause of preventable blindness worldwide. The common nature of the trait suggests the presence of many genetic factors that individually may have modest effects. To achieve an adequate sample size to detect these common variants, large, international collaborations have formed. These consortia typically use meta-analysis to combine multiple studies from many different populations. This approach is robust to differences between populations; however, it does not compensate for the different haplotypes in each genetic background evidenced by different alleles in linkage disequilibrium with the causative variant. We used the Age-Related Eye Disease Study (AREDS) cohort to replicate published significant associations at two loci on chromosome 15 from two genome-wide association studies (GWASs). The single nucleotide polymorphisms (SNPs) that exhibited association on chromosome 15 in the original studies did not show evidence of association with refractive error in the AREDS cohort. This paper seeks to determine whether the non-replication in this AREDS sample may be due to the limited number of SNPs chosen for replication.

METHODS

We selected all SNPs genotyped on the Illumina Omni2.5v1_B array or custom TaqMan assays or imputed from the GWAS data, in the region surrounding the SNPs from the Consortium for Refractive Error and Myopia study. We analyzed the SNPs for association with refractive error using standard regression methods in PLINK. The effective number of tests was calculated using the Genetic Type I Error Calculator.

RESULTS

Although use of the same SNPs used in the Consortium for Refractive Error and Myopia study did not show any evidence of association with refractive error in this AREDS sample, other SNPs within the candidate regions demonstrated an association with refractive error. Significant evidence of association was found using the hyperopia categorical trait, with the most significant SNPs rs1357179 on 15q14 (p=1.69×10⁻³) and rs7164400 on 15q25 (p=8.39×10⁻⁴), which passed the replication thresholds.

CONCLUSIONS

This study adds to the growing body of evidence that attempting to replicate the most significant SNPs found in one population may not be significant in another population due to differences in the linkage disequilibrium structure and/or allele frequency. This suggests that replication studies should include less significant SNPs in an associated region rather than only a few selected SNPs chosen by a significance threshold.

HapFABIA: identification of very short segments of identity by descent characterized by rare variants in large sequencing data

Identity by descent (IBD) can be reliably detected for long shared DNA segments, which are found in related individuals. However, many studies contain cohorts of unrelated individuals that share only short IBD segments. New sequencing technologies facilitate identification of short IBD segments through rare variants, which convey more information on IBD than common variants. Current IBD detection methods, however, are not designed to use rare variants for the detection of short IBD segments. Short IBD segments reveal genetic structures at high resolution. Therefore, they can help to improve imputation and phasing, to increase genotyping accuracy for low-coverage sequencing and to increase the power of association studies. Since short IBD segments are further assumed to be old, they can shed light on the evolutionary history of humans. We propose HapFABIA, a computational method that applies biclustering to identify very short IBD segments characterized by rare variants. HapFABIA is designed to detect short IBD segments in genotype data that were obtained from next-generation sequencing, but can also be applied to DNA microarray data. Especially in next-generation sequencing data, HapFABIA exploits rare variants for IBD detection. HapFABIA significantly outperformed competing algorithms at detecting short IBD segments on artificial and simulated data with rare variants. HapFABIA identified 160 588 different short IBD segments characterized by rare variants with a median length of 23 kb (mean 24 kb) in data for chromosome 1 of the 1000 Genomes Project. These short IBD segments contain 752 000 single nucleotide variants (SNVs), which account for 39% of the rare variants and 23.5% of all variants. The vast majority—152 000 IBD segments—are shared by Africans, while only 19 000 and 11 000 are shared by Europeans and Asians, respectively. IBD segments that match the Denisova or the Neandertal genome are found significantly more often in Asians and Europeans but also, in some cases exclusively, in Africans. The lengths of IBD segments and their sharing between continental populations indicate that many short IBD segments from chromosome 1 existed before humans migrated out of Africa. Thus, rare variants that tag these short IBD segments predate human migration from Africa. The software package HapFABIA is available from Bioconductor. All data sets, result files and programs for data simulation, preprocessing and evaluation are supplied at http://www.bioinf.jku.at/research/short-IBD.

Meta-analysis methods for genome-wide association studies and beyond

Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.

Kernel machine SNP-set testing under multiple candidate kernels

Joint testing for the cumulative effect of multiple single-nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large-scale genetic association studies. The kernel machine (KM)-testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori because this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest P-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power vs. using the best candidate kernel.

Water uptake dynamics under progressive drought stress in diverse accessions of the OryzaSNP panel of rice (Oryza sativa)

In addition to characterising root architecture, evaluating root water uptake ability is important for understanding drought response. A series of three lysimeter studies were conducted using the OryzaSNP panel, which consists of 20 diverse rice (Oryza sativa L.) genotypes. Large genotypic differences in drought response were observed in this genotype panel in terms of plant growth and water uptake. Total water uptake and daily water uptake rates in the drought-stress treatment were correlated with root length density, especially at depths below 30cm. Patterns of water uptake among genotypes remained consistent throughout the stress treatments: genotypes that initially extracted more water were the same genotypes that extracted more water at the end of the study. These results suggest that response to drought by deep root growth, rather than a conservative soil water pattern, seems to be important for lowland rice. Genotypes in the O. sativa type aus group showed some of the greatest water uptake and root growth values. Since the OryzaSNP panel has been genotyped in detail with SNP markers, we expect that these results will be useful for understanding the genetics of rice root growth and function for water uptake in response to drought.

ARIEL and AMELIA: testing for an accumulation of rare variants using next-generation sequencing data

OBJECTIVES

There is increasing evidence that rare variants play a role in some complex traits, but their analysis is not straightforward. Locus-based tests become necessary due to low power in rare variant single-point association analyses. In addition, variant quality scores are available for sequencing data, but are rarely taken into account. Here, we propose two locus-based methods that incorporate variant quality scores: a regression-based collapsing approach and an allele-matching method.

METHODS

Using simulated sequencing data we compare 4 locus-based tests of trait association under different scenarios of data quality. We test two collapsing-based approaches and two allele-matching-based approaches, taking into account variant quality scores and ignoring variant quality scores. We implement the collapsing and allele-matching approaches accounting for variant quality in the freely available ARIEL and AMELIA software.

RESULTS

The incorporation of variant quality scores in locus-based association tests has power advantages over weighting each variant equally. The allele-matching methods are robust to the presence of both protective and risk variants in a locus, while collapsing methods exhibit a dramatic loss of power in this scenario.

CONCLUSIONS

The incorporation of variant quality scores should be a standard protocol when performing locus-based association analysis on sequencing data. The ARIEL and AMELIA software implement collapsing and allele-matching locus association analysis methods, respectively, that allow the incorporation of variant quality scores.