MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS

The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes.     

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which can carry as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG’s applications to Alzheimer’s disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.     

Eleven years of sexual discovery

A report on Novartis Foundation Symposium 244 "The Genetics and Biology of Sex Determination", London, UK, 1-3 May 2001.     

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions (“Flexible cFDR”). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.     

Five years of Homo floresiensis

Researchers have hypothesized that the degree to which an individual's actual behavior approximates the culturally valued lifestyle encoded in the dominant cultural model has consequences for physical and mental health. We contribute to this line of research by analyzing data from a longitudinal study composed of five annual surveys (2002–2006 inclusive) from 791 adults in one society of foragers–farmers in the Bolivian Amazon, the Tsimane'. We estimate the association between a standard measure of individual achievement of the cultural model, cultural consonance, and three indicators of body morphology. Drawing on research suggesting that in societies in the early stages of economic development an increase in socioeconomic status is associated with an increase in mean body mass, we expect to find a positive association between cultural consonance and three anthropometric measures. We found the expected positive association between cultural consonance and anthropometric measures—especially for men—only when using ordinary least square (OLS) regression models, but not when using fixed‐effects regression models. The real magnitude of the association was low. The comparison of estimates from OLS and fixed‐effect regression models suggests that previous findings on the effects of cultural consonance on body morphology using cross‐sectional data should be read with caution because the association might be largely explained by fixed characteristics of individuals not accounted in OLS models. Am J Phys Anthropol 143:167–174, 2010. © 2010 Wiley‐Liss, Inc.     

Evaluation of Five Candidate Genes from GWAS for Association with Oligozoospermia in a Han Chinese Population

Background

Oligozoospermia is one of the severe forms of idiopathic male infertility. However, its pathology is largely unknown, and few genetic factors have been defined. Our previous genome-wide association study (GWAS) has identified four risk loci for non-obstructive azoospermia (NOA).

Objective

To investigate the potentially functional genetic variants (including not only common variants, but also less-common and rare variants) of these loci on spermatogenic impairment, especially oligozoospermia.

Design, Setting, and Participants

A total of 784 individuals with oligozoospermia and 592 healthy controls were recruited to this study from March 2004 and January 2011.

Measurements

We conducted a two-stage study to explore the association between oligozoospermia and new makers near NOA risk loci. In the first stage, we used next generation sequencing (NGS) in 96 oligozoospermia cases and 96 healthy controls to screen oligozoospermia-susceptible genetic variants. Next, we validated these variants in a large cohort containing 688 cases and 496 controls by SNPscan for high-throughput Single Nucleotide Polymorphism (SNP) genotyping.

Results and Limitations

Totally, we observed seven oligozoospermia associated variants (rs3791185 and rs2232015 in PRMT6, rs146039840 and rs11046992 in Sox5, rs1129332 in PEX10, rs3197744 in SIRPA, rs1048055 in SIRPG) in the first stage. In the validation stage, rs3197744 in SIRPA and rs11046992 in Sox5 were associated with increased risk of oligozoospermia with an odds ratio (OR) of 4.62 (P  =  0.005, 95%CI 1.58-13.4) and 1.82 (P  =  0.005, 95%CI 1.01-1.64), respectively. Further investigation in larger populations and functional characterizations are needed to validate our findings.

Conclusions

Our study provides evidence of independent oligozoospermia risk alleles driven by variants in the potentially functional regions of genes discovered by GWAS. Our findings suggest that integrating sequence data with large-scale genotyping will serve as an effective strategy for discovering risk alleles in the future.     

Five thousand years of livestock in Britain

The introduction of livestock, including the rabbit and the fallow deer, to Britain is reviewed, and a summary is given of the history of husbandry arid selective breeding. The feeding or stock on foliage and its importance for survival through the winter is described. The meaning of domestication is discussed and ii is argued that there should be no separation between the old established breeds of livestock, park deer, and feral populations including Chillingham rattle, Soay sheep, and Bagot goats; all belong to the assemblage of large mammals that has created the landscape of the British Isles.     

Five years of FISH-BOL: brief status report

The Fish Barcode of Life Initiative (FISH-BOL) is a concerted global research project launched in 2005, with the goal to collect and assemble standardized DNA barcode sequences and associated voucher provenance data in a curated reference sequence library to aid the molecular identification of all fish species. This article is a detailed progress report (July 2010) on the number of fish species that have been assigned a DNA barcode. Of the approximately 31,000 currently known fish species, 25% have been processed successfully, with at least one species from 89% of all families barcoded; in this report we give a progress overview by taxonomy and geographic region. Using standard analytical protocols, differences in the barcoding completion rate between orders and families are observed, suggesting a potential PCR amplification bias. Overall, between 3 and 9% of the species analyzed failed to yield a "BARCODE compliant" sequence, depending upon how the data are filtered. When species with only a single representative specimen are included, the failure rate was 9%. This might derive from several sources such as mismatched primers and degraded DNA templates. In an attempt to account for the latter, when the analysis is restricted to species with at least two specimens examined, the observed failure rate is significantly lower (3%), suggesting that template quality is a source of concern for FISH-BOL. We, therefore, conclude that using a standard protocol with several specimens per species and PCR primer cocktails is an efficient and successful approach because failures were evenly distributed among orders and families. Only six orders with low species numbers (Pristiformes, Torpediniformes, Albuliformes, Batrachoidiformes, Gobiesociformes, and Petromyzontiformes) showed failure rates between 10 and 33%. Besides outlining an overarching approach for FISH-BOL data curation, the goal of the present article is to give guidance in directing sampling campaigns toward neglected or underrepresented families in order to complete the FISH-BOL campaign most efficiently.     

Endothelin: 20 years from discovery to therapy

Since its identification as an endothelial cell-derived vasoconstrictor peptide in 1988, endothelin-1, the predominant member of the endothelin peptide family, has received considerable interest in basic medical science and in clinical medicine, which is reflected by more than 20 000 scientific publications on endothelin research in the past 20 years. The story of endothelin is unique as the gene sequences of endothelin receptors and the first receptor antagonists became available within only 4 years of the identification of the peptide sequence.The first clinical study in patients with congestive heart failure was published only 3 years thereafter. Yet, despite convincing experimental evidence of a pathogenetic role for endothelin in development, cell function, and disease, many initial clinical studies on endothelin antagonism were negative. In many of these studies, study designs or patient selection were inadequate. Today, for diseases such as pulmonary hypertension, endothelin antagonist treatment has become reality in clinical medicine, and ongoing clinical studies are evaluating additional indications, such as renal disease and cancer. Twenty years after the discovery of endothelin, its inhibitors have finally arrived in the clinical arena and are now providing us with new options to treat disease and prolong the lives of patients. Possible future indications include resistant arterial hypertension, proteinuric renal disease, cancer, and connective tissue diseases.     

Meiosis in plants: ten years of gene discovery

Plants have always been at the forefront of genetic and cytogenetic studies, but it was only following the explosion of genomic tools linked to the development of Arabidopsis thaliana as a model, that the first genes involved in plant meiosis were cloned in the late 1990s. Since then, in less than 10 years, close to fifty plant meiotic genes have been functionally characterized, mainly in Arabidopsis but also in rice and maize. In this review, we give an overview of this decade of discovery, with emphasis on the strategies that have been used for meiotic gene identification. We also highlight particularly interesting breakthroughs that these mutant and gene screens made possible.     

Reducing GWAS Complexity

Genome-wide association studies (GWAS) have revealed numerous genomic 'hits' associated with complex phenotypes. In most cases these hits, along with surrogate genetic variation as measure by numerous single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium, are not in coding genes making assignment of functionality or causality intractable. Here we propose that fine-mapping along with the matching of risk SNPs at chromatin biofeatures lessen this complexity by reducing the number of candidate functional/causal SNPs. For example, we show here that only on average 2 SNPs per prostate cancer risk locus are likely candidates for functionality/causality; we further propose that this manageable number should be taken forward in mechanistic studies. The candidate SNPs can be looked up for each prostate cancer risk region in 2 recent publications in 2015^1,2 Han Y, Hazelett DJ, Wiklund F, Schumacher FR, Stram DO, Berndt SI, Wang Z, Rand KA, Hoover RN, Machiela MJ, et al. Integration of Multiethnic Fine-mapping and Genomic Annotation to Prioritize Candidate Functional SNPs at Prostate Cancer Susceptibility Regions. Hum Mol Genet 2015; 24(19):5603–18. Amin Al Olama A, Dadaev T, Hazelett DJ, Li Q, Leongamornlert D, Saunders EJ, Stephens S, Cieza-Borrella C, Whitmore I, Benlloch Garcia S, et al. Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans. Hum Mol Genet 2015; 24(19):5589–602.  from our groups.