Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes

Disorders that share genetic risk factors often are placed in closely related diagnostic categories and treated similarly. Until recently, evidence for shared genetic etiology derived from classical research strategies – coaggregation in family and twin studies. Accumulating sufficient numbers of families was often problematic. However, in the era of genome-wide genotyping, we can now directly estimate the degree of sharing of genetic risk factors between disorders. This strategy is practical even for very rare disorders, where it is infeasible to ascertain informative families. Importantly, the estimates of genetic correlations from genome-wide genotypes are derived using such distant relatives that contamination by shared environmental factors seems unlikely. However, any method that seeks to quantify the shared etiology of disorders assumes they can be distinguished diagnostically from one another without error. Here we investigate the impact of misdiagnosis on estimates of genetic correlation both from traditional family data and from genome-wide genotypes of case–control samples from unrelated individuals. Our analyses show similar results for levels of misdiagnosis in both types of data. In both scenarios, genetic variances and heritabilities tend to be slightly underestimated but genetic correlations are overestimated, sometimes substantially so. For example, two genetically distinct but equally heritable disorders each with prevalence 1%, can generate false-positive estimates of genetic correlations of >0.2 in the presence of 10% reciprocal misdiagnosis. Strategies for minimizing the effects of misdiagnosis in cross-disorder genetic studies are discussed

The distribution of genetic diversity in a Brassica oleracea gene bank collection related to the effects on diversity of regeneration, as measured with AFLPs

The ex situ conservation of plant genetic resources in gene banks involves the selection of accessions to be conserved and the maintenance of these accessions for current and future users. Decisions concerning both these issues require knowledge about the distribution of genetic diversity within and between accessions sampled from the gene pool, but also about the changes in variation of these samples as a result of regenerations. These issues were studied in an existing gene bank collection of a cross-pollinating crop using a selection of groups of very similar Dutch white cabbage accessions, and additional groups of reference material representing the Dutch, and the global white cabbage gene pool. Six accessions were sampled both before and after a standard regeneration. 30 plants of each of 50 accessions plus 6 regeneration populations included in the study were characterised with AFLPs, using scores for 103 polymorphic bands. It was shown that the genetic changes as a result of standard gene bank regenerations, as measured by AFLPs, are of a comparable magnitude as the differences between some of the more similar accessions. The observed changes are mainly due to highly significant changes in allele frequencies for a few fragments, whereas for the majority of fragments the alleles occur in similar frequencies before and after regeneration. It is argued that, given the changes of accessions over generations, accessions that display similar levels of differentiation may be combined safely

Biophysical and electrochemical studies of protein-nucleic acid interactions

This review is devoted to biophysical and electrochemical methods used for studying protein-nucleic acid (NA) interactions. The importance of NA structure and protein-NA recognition for essential cellular processes, such as replication or transcription, is discussed to provide background for description of a range of biophysical chemistry methods that are applied to study a wide scope of protein-DNA and protein-RNA complexes. These techniques employ different detection principles with specific advantages and limitations and are often combined as mutually complementary approaches to provide a complete description of the interactions. Electrochemical methods have proven to be of great utility in such studies because they provide sensitive measurements and can be combined with other approaches that facilitate the protein-NA interactions. Recent applications of electrochemical methods in studies of protein-NA interactions are discussed in detail

Comparison of anonymous and targeted molecular markers for the estimation of genetic diversity in ex situ conserved Lactuca

The anonymous marker systems microsatellites (simple sequence repeats), amplified fragment length polymorphisms and sequence-specific amplified polymorphisms were compared with the targeted marker systems sequence-related amplified polymorphisms, target region amplification polymorphisms and nucleotide binding site profiling for their ability to describe the genetic diversity in a selected set of 80 Lactuca accessions. The accessions were also described morphologically, and all characterisation methods were evaluated against the genetic diversity assessed by a panel of three crop experts. The morphological data showed a low level of association with the molecular data, and did not display a consistently better relationship with the experts’ assessments in comparison with the molecular data. In general, the diversity described by the targeted molecular markers did not differ markedly from that of the anonymous markers, resulting in only slight differences in performance when related to the expert-based assessments. It was argued that markers targeted to specific gene sequences may still behave as anonymous markers and that the type of marker system used is irrelevant when at low taxonomic levels a clear genetic structure is absent due to intensive breeding activities

How Humans Differ from Other Animals in Their Levels of Morphological Variation

Animal species come in many shapes and sizes, as do the individuals and populations that make up each species. To us, humans might seem to show particularly high levels of morphological variation, but perhaps this perception is simply based on enhanced recognition of individual conspecifics relative to individual heterospecifics. We here more objectively ask how humans compare to other animals in terms of body size variation. We quantitatively compare levels of variation in body length (height) and mass within and among 99 human populations and 848 animal populations (210 species). We find that humans show low levels of within-population body height variation in comparison to body length variation in other animals. Humans do not, however, show distinctive levels of within-population body mass variation, nor of among-population body height or mass variation. These results are consistent with the idea that natural and sexual selection have reduced human height variation within populations, while maintaining it among populations. We therefore hypothesize that humans have evolved on a rugged adaptive landscape with strong selection for body height optima that differ among locations

Accounting for a Quantitative Trait Locus for Plasma Triglyceride Levels: Utilization of Variants in Multiple Genes

For decades, research efforts have tried to uncover the underlying genetic basis of human susceptibility to a variety of diseases. Linkage studies have resulted in highly replicated findings and helped identify quantitative trait loci (QTL) for many complex traits; however identification of specific alleles accounting for linkage remains elusive. The purpose of this study was to determine whether with a sufficient number of variants a linkage signal can be fully explained.We used comprehensive fine-mapping using a dense set of single nucleotide polymorphisms (SNPs) across the entire quantitative trait locus (QTL) on human chromosome 7q36 linked to plasma triglyceride levels. Analyses included measured genotype and combined linkage association analyses.Screening this linkage region, we found an over representation of nominally significant associations in five genes (MLL3, DPP6, PAXIP1, HTR5A, INSIG1). However, no single genetic variant was sufficient to account for the linkage. On the other hand, multiple variants capturing the variation in these five genes did account for the linkage at this locus. Permutation analyses suggested that this reduction in LOD score was unlikely to have occurred by chance (p = 0.008).With recent findings, it has become clear that most complex traits are influenced by a large number of genetic variants each contributing only a small percentage to the overall phenotype. We found that with a sufficient number of variants, the linkage can be fully explained. The results from this analysis suggest that perhaps the failure to identify causal variants for linkage peaks may be due to multiple variants under the linkage peak with small individual effect, rather than a single variant of large effect

Quantifying Missing Heritability at Known GWAS Loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 X more heritability than GWAS-associated SNPs on average (P = 3.3 X 10[superscript -5]). For some diseases, this increase was individually significant:2.07 X for Multiple Sclerosis (MS) (P = 6.5 X 10 [superscript -9]) and for Crohn's Disease (CD) (P = 1.3 X 10[superscript -3]); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 X more MS heritability than known MS SNPs (P 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 X more heritability from all SNPs at GWAS loci (P = 2.3 X 10[superscript -6]) and more heritability from all autoimmune disease loci (P < 1 X 10[superscript -16]) compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.National Institutes of Health (U.S.) (Grant R03HG006731)National Institutes of Health (U.S.) (Fellowship F32GM106584

TEAD1 and c-Cbl are novel prostate basal cell markers that correlate with poor clinical outcome in prostate cancer

Prostate cancer is the most frequently diagnosed male cancer, and its clinical outcome is difficult to predict. The disease may involve the inappropriate expression of genes that normally control the proliferation of epithelial cells in the basal layer and their differentiation into luminal cells. Our aim was to identify novel basal cell markers and assess their prognostic and functional significance in prostate cancer. RNA from basal and luminal cells isolated from benign tissue by immunoguided laser-capture microdissection was subjected to expression profiling. We identified 112 and 267 genes defining basal and luminal populations, respectively. The transcription factor TEAD1 and the ubiquitin ligase c-Cbl were identified as novel basal cell markers. Knockdown of either marker using siRNA in prostate cell lines led to decreased cell growth in PC3 and disrupted acinar formation in a 3D culture system of RWPE1. Analyses of prostate cancer tissue microarray staining established that increased protein levels of either marker were associated with decreased patient survival independent of other clinicopathological metrics. These data are consistent with basal features impacting on the development and clinical course of prostate cancers

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts