No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (β = 16.1, CI(β) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (β = 4.86,CI(β) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest

Detectable clonal mosaicism and its relationship to aging and cancer

In an analysis of 31,717 cancer cases and 26,136 cancer-free controls from 13 genome-wide association studies, we observed large chromosomal abnormalities in a subset of clones in DNA obtained from blood or buccal samples. We observed mosaic abnormalities, either aneuploidy or copy-neutral loss of heterozygosity, of >2 Mb in size in autosomes of 517 individuals (0.89%), with abnormal cell proportions of between 7% and 95%. In cancer-free individuals, frequency increased with age, from 0.23% under 50 years to 1.91% between 75 and 79 years (P = 4.8 × 10(-8)). Mosaic abnormalities were more frequent in individuals with solid tumors (0.97% versus 0.74% in cancer-free individuals; odds ratio (OR) = 1.25; P = 0.016), with stronger association with cases who had DNA collected before diagnosis or treatment (OR = 1.45; P = 0.0005). Detectable mosaicism was also more common in individuals for whom DNA was collected at least 1 year before diagnosis with leukemia compared to cancer-free individuals (OR = 35.4; P = 3.8 × 10(-11)). These findings underscore the time-dependent nature of somatic events in the etiology of cancer and potentially other late-onset diseases

Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

J. Lönnqvist on työryhmän Psychiat Genomics Consortium jäsen.Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on similar to 150,000 individuals give a higher accuracy than LDSC estimates based on similar to 400,000 individuals (from combinedmeta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.Peer reviewe

Advanced satellite imagery to classify sugarcane crop characteristics

Techniques that provide a rapid and widespread assessment of crop properties equip industry decision makers with knowledge to improve their farming environment, both tactically and strategically. An interdisciplinary approach that links the fields of hyperspectral remote sensing, statistical data mining and sugarcane systems was undertaken to establish new relationships to determine variety type and crop age of sugarcane plants. In contrast to commonly used sensors such as those occupied by Landsat satellites, images captured by hyperspectral sensors can provide a more detailed assessment of crop status. Appropriate statistical analysis methods are needed to decode the multifaceted information recorded in these hyperspectral images. A range of statistical approaches have been applied for analysis of an EO-1 hyperion hyperspectral image from a major sugarcane growing region in Australia. Two relatively new classification methods - support vector machines and random forests - demonstrated superior performance in classifying sugarcane variety and crop cycle, e.g. the number of times that the plant has grown back after harvest, when compared against traditional statistical methods. Assignment results were further enhanced when classifications of pixels within sugarcane paddocks were aggregated to paddock classifications using paddock boundary information. Whilst the analysis methods of the hyperspectral data have been tested for the classification of variety and crop cycle, the potential application arenas for this type of imagery is both extensive and relatively unexplored. This type of data coupled with appropriate analysis methods will play a vital role in futuristic sustainable agriculture practices as this imagery becomes more accessible and as land managers and researchers become more aware of the types of decisions that hyperspectral remote sensing data can aid

The relationships between plasma and red cell vitamin B2 and B6 concentrations and the systemic and local inflammatory responses in patients with colorectal cancer

B vitamins have been implicated in cancer pathogenesis. It is therefore of interest that plasma B6 falls as part of the systemic inflammatory response (SIR), whereas red cell concentrations do not. The modified Glasgow Prognostic Score (mGPS) is a validated inflammation-based prognostic score that consists of a combination of albumin and C-reactive protein concentrations. The aim of this study was to examine the relationships between the concentrations of plasma and red cell vitamin B concentrations, the local and systemic inflammatory response in patients with colorectal cancer. Preoperative venous blood of 108 patients with colorectal cancer were analyzed for C-reactive protein, albumin, flavin adenine dinucleotide (FAD), and pyridoxal phosphate (PLP), and lymphocyte counts. Pathological slides were retrieved for assessment of inflammatory cell infiltration. Increasing mGPS was associated with lower plasma PLP concentrations (P < 0.01) but not plasma and red cell FAD and red cell PLP concentrations. Increasing tumor stage was associated with the presence of venous invasion (P < 0.01) and low-grade inflammatory cell infiltrate (P < 0.05) but not the SIR, FAD, or PLP concentrations. A low-grade inflammatory cell infiltrate was not significantly associated with any other parameter. The presence of a SIR was associated with lower concentrations of plasma PLP but not red cell PLP concentrations in patients with colorectal cancer. Neither FAD and PLP were associated with the tumor inflammatory cell infiltrate

A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework

Background: There is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr). Results: We propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power. Conclusions: With our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS