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"Auer, Paul"
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Rare variant association studies: considerations, challenges and opportunities
Genome-wide association studies (GWASs) have successfully uncovered thousands of robust associations between common variants and complex traits and diseases. Despite these successes, much of the heritability of these traits remains unexplained. Because low-frequency and rare variants are not tagged by conventional genome-wide genotyping arrays, they may represent an important and understudied component of complex trait genetics. In contrast to common variant GWASs, there are many different types of study designs, assays and analytic techniques that can be utilized for rare variant association studies (RVASs). In this review, we briefly present the different technologies available to identify rare genetic variants, including novel exome arrays. We also compare the different study designs for RVASs and argue that the best design will likely be phenotype-dependent. We discuss the main analytical issues relevant to RVASs, including the different statistical methods that can be used to test genetic associations with rare variants and the various bioinformatic approaches to predicting
in silico
biological functions for variants. Finally, we describe recent rare variant association findings, highlighting the unexpected conclusion that most rare variants have modest-to-small effect sizes on phenotypic variation. This observation has major implications for our understanding of the genetic architecture of complex traits in the context of the unexplained heritability challenge.
Journal Article
Finding causal genes underlying risk for coronary artery disease
Previous genome-wide association studies of coronary artery disease (CAD) have discovered multiple susceptibility loci but have largely failed to uncover causal genes. A new study identifies hundreds of likely causal genes underlying the genetic risk for CAD.
Journal Article
Statistical Design and Analysis of RNA Sequencing Data
Next-generation sequencing technologies are quickly becoming the preferred approach for characterizing and quantifying entire genomes. Even though data produced from these technologies are proving to be the most informative of any thus far, very little attention has been paid to fundamental design aspects of data collection and analysis, namely sampling, randomization, replication, and blocking. We discuss these concepts in an RNA sequencing framework. Using simulations we demonstrate the benefits of collecting replicated RNA sequencing data according to well known statistical designs that partition the sources of biological and technical variation. Examples of these designs and their corresponding models are presented with the goal of testing differential expression.
Journal Article
Principles and methods for transferring polygenic risk scores across global populations
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.This Review summarizes the genetic and non-genetic factors that impact the transferability of polygenic risk scores (PRSs) across populations, highlighting the technical challenges of existing PRS construction methods for diverse ancestries and the emerging resources for more widespread use of PRSs.
Journal Article
Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI
Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women’s Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.
Most polygenic risk score (PRS) methods focus only on individuals with distinct primary continental ancestry, without accommodating recently-admixed individuals. Here, the authors develop a novel penalized regression-based PRS method specifically designed for admixed individuals.
Journal Article
Comparison of multiple imputation and other methods for the analysis of imputed genotypes
Background
Analysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype is unknown and genotypes are inferred with uncertainty using statistical models. Here, we present a novel method for integrating imputation uncertainty into statistical association tests using a fully conditional multiple imputation (MI) approach which is implemented using the Substantive Model Compatible Fully Conditional Specification (SMCFCS). We compared the performance of this method to an unconditional MI and two additional approaches that have been shown to demonstrate excellent performance: regression with dosages and a mixture of regression models (MRM).
Results
Our simulations considered a range of allele frequencies and imputation qualities based on data from the UK Biobank. We found that the unconditional MI was computationally costly and overly conservative across a wide range of settings. Analyzing data with Dosage, MRM, or MI SMCFCS resulted in greater power, including for low frequency variants, compared to unconditional MI while effectively controlling type I error rates. MRM andl MI SMCFCS are both more computationally intensive then using Dosage.
Conclusions
The unconditional MI approach for association testing is overly conservative and we do not recommend its use in the context of imputed genotypes. Given its performance, speed, and ease of implementation, we recommend using Dosage for imputed genotypes with MAF
≥
0.001 and Rsq
≥
0.3.
Journal Article
All your data (effectively) belong to us: data practices among direct-to-consumer genetic testing firms
Purpose:
Direct-to-consumer genetic testing (DTC-GT) has become a convenient method to help people to understand their genetic makeup. Owing in part to concerns regarding confidentiality, privacy, and secondary use of data, professional and government bodies created guidelines to promote transparency among these companies. Using a comprehensive and systematic approach, this study assessed DTC-GT company compliance with international transparency guidelines.
Methods:
A framework analysis was performed on 30 DTC-GT health and/or ancestry websites identified using a US-based online search strategy during the summer of 2015. A codebook was developed from a synthesis of relevant guidelines from seven DTC-GT guideline documents and applied to each website.
Results:
Although most companies met guidelines related to transparency regarding security protocols, storage procedures, and third-party disclosures, few met guidelines regarding sharing risks from data disclosures. Additionally, few companies disclosed how long data would be kept for services or research. Use of data for research was frequently mentioned only in privacy policies and terms of service documents, and only two-thirds of companies required an additional consent to use consumer data for health-related research.
Conclusion:
Our analysis shows that DTC-GT companies do not consistently meet international transparency guidelines related to confidentiality, privacy, and secondary use of data.
Genet Med
advance online publication 22 September 2016
Journal Article
Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality
Mitochondria carry their own circular genome and disruption of the mitochondrial genome is associated with various aging-related diseases. Unlike the nuclear genome, mitochondrial DNA (mtDNA) can be present at 1000 s to 10,000 s copies in somatic cells and variants may exist in a state of heteroplasmy, where only a fraction of the DNA molecules harbors a particular variant. We quantify mtDNA heteroplasmy in 194,871 participants in the UK Biobank and find that heteroplasmy is associated with a 1.5-fold increased risk of all-cause mortality. Additionally, we functionally characterize mtDNA single nucleotide variants (SNVs) using a constraint-based score, mitochondrial local constraint score sum (MSS) and find it associated with all-cause mortality, and with the prevalence and incidence of cancer and cancer-related mortality, particularly leukemia. These results indicate that mitochondria may have a functional role in certain cancers, and mitochondrial heteroplasmic SNVs may serve as a prognostic marker for cancer, especially for leukemia.
Mitochondrial DNA is known to exhibit heterogeneity of variants, even within a single cell. Here, the authors assessed this heteroplasmy of mitochondrial DNA within the UK Biobank cohort and showed that the presence of heteroplasmy and a functional score generated from heteroplasmic SNVs were associated with all-cause mortality and certain cancers.
Journal Article
Rare coding variants pinpoint genes that control human hematological traits
The identification of rare coding or splice site variants remains the most straightforward strategy to link genes with human phenotypes. Here, we analyzed the association between 137,086 rare (minor allele frequency (MAF) <1%) coding or splice site variants and 15 hematological traits in up to 308,572 participants. We found 56 such rare coding or splice site variants at P<5x10-8, including 31 that are associated with a blood-cell phenotype for the first time. All but one of these 31 new independent variants map to loci previously implicated in hematopoiesis by genome-wide association studies (GWAS). This includes a rare splice acceptor variant (rs146597587, MAF = 0.5%) in interleukin 33 (IL33) associated with reduced eosinophil count (P = 2.4x10-23), and lower risk of asthma (P = 2.6x10-7, odds ratio [95% confidence interval] = 0.56 [0.45-0.70]) and allergic rhinitis (P = 4.2x10-4, odds ratio = 0.55 [0.39-0.76]). The single new locus identified in our study is defined by a rare p.Arg172Gly missense variant (rs145535174, MAF = 0.05%) in plasminogen (PLG) associated with increased platelet count (P = 6.8x10-9), and decreased D-dimer concentration (P = 0.018) and platelet reactivity (P<0.03). Finally, our results indicate that searching for rare coding or splice site variants in very large sample sizes can help prioritize causal genes at many GWAS loci associated with complex human diseases and traits.
Journal Article