Interest of phenomic prediction as an alternative to genomic prediction in grapevine

Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. We found that the co-inertia between spectra and genomic data was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of -0.2, thus suggesting that phenomic prediction could be applied for any trait.

Synergistic epistasis of the deleterious effects of transposable elements

The replicative nature and generally deleterious effects of transposable elements (TEs) raise an outstanding question about how TE copy number is stably contained in host populations. Classic theoretical analyses predict that, when the decline in fitness due to each additional TE insertion is greater than linear, or when there is synergistic epistasis, selection against TEs can result in a stable equilibrium of TE copy number. While several mechanisms are predicted to yield synergistic deleterious effects of TEs, we lack empirical investigations of the presence of such epistatic interactions. Purifying selection with synergistic epistasis generates repulsion linkage between deleterious alleles. We investigated this population genetic signal in the likely ancestral Drosophila melanogaster population and found evidence supporting the presence of synergistic epistasis among TE insertions, especially TEs expected to exert large fitness impacts. Even though synergistic epistasis of TEs has been predicted to arise through ectopic recombination and TE-mediated epigenetic silencing mechanisms, we only found mixed support for the associated predictions. We observed signals of synergistic epistasis for a large number of TE families, which is consistent with the expectation that such epistatic interaction mainly happens among copies of the same family. Curiously, significant repulsion linkage was also found among TE insertions from different families, suggesting the possibility that synergism of TEs’ deleterious fitness effects could arise above the family level and through mechanisms similar to those of simple mutations. Our findings set the stage for investigating the prevalence and importance of epistatic interactions in the evolutionary dynamics of TEs.

CTNNA3 genetic polymorphism may be a new genetic signal of type 2 diabetes in the Chinese Han population: a case control study

Background Type 2 Diabetes (T2D) is the result of a combination of genes and environment. The identified genetic loci can only explain part of T2D risk. Our study is aimed to explore the association between CTNNA3 single nucleotide polymorphisms (SNPs) and T2D risk. Methods We conducted a 'case–control' study among 1002 Chinese Han participants. Four candidate SNPs of CTNNA3 were selected (rs10822745 C/T, rs7920624 A/T, rs2441727 A/G, rs7914287 A/G), and logistic regression analysis was used to evaluate the association between candidate SNPs and T2D risk. We used single factor analysis of variance to analyze the differences of clinical characteristics among different genotypes. In this study, haplotype analysis was conducted by plink1.07 and Haploview software and linkage disequilibrium (LD) was calculated. The interaction of candidate SNPs in T2D risk was evaluated by multi-factor dimensionality reduction (MDR). Finally, we conducted a false-positive report probability (FPRP) analysis to detect whether the significant findings were just chance or noteworthy observations. Results The results showed that CTNNA3-rs7914287 was a risk factor for T2D (‘T’: OR = 1.33, p = 0.003; ‘TT’: OR = 2.21, p = 0.001; ‘TT’ (recessive): OR = 2.09, p = 0.001; Log-additive: OR = 1.34, p = 0.003). The results of subgroup analysis showed that rs7914287 was significantly associated with the increased risk of T2D among participants who were older than 60 years, males, smoking, drinking, or BMI > 24. We also found that rs2441727 was associated with reducing the T2D risk among participants who were older than 60 years, smoking, or drinking. In addition, rs7914287 was associated with T2D patients with no retinal degeneration; rs10822745 and rs7920624 were associated with the course of T2D patients. High density lipoprotein levels had significant differences under different genotypes of rs10822745. Under the different genotypes of rs7914287, the levels of aspartate aminotransferase, alanine aminotransferase and gamma-glutamyltransferase were also significantly different. Conclusion We found that CTNNA3 genetic polymorphisms can be used as a new genetic signal of T2D risk in Chinese Han population. Especially, CTNNA3-rs7914287 showed an outstanding and significant association with T2D risk in both overall analysis and subgroup analysis.

Wavelet Screening: a novel approach to analyzing GWAS data

Background Traditional methods for single-variant genome-wide association study (GWAS) incur a substantial multiple-testing burden because of the need to test for associations with a vast number of single-nucleotide polymorphisms (SNPs) simultaneously. Further, by ignoring more complex joint effects of nearby SNPs within a given region, these methods fail to consider the genomic context of an association with the outcome. Results To address these shortcomings, we present a more powerful method for GWAS, coined ‘Wavelet Screening’ (WS), that greatly reduces the number of tests to be performed. This is achieved through the use of a sliding-window approach based on wavelets to sequentially screen the entire genome for associations. Wavelets are oscillatory functions that are useful for analyzing the local frequency and time behavior of signals. The signals can then be divided into different scale components and analyzed separately. In the current setting, we consider a sequence of SNPs as a genetic signal, and for each screened region, we transform the genetic signal into the wavelet space. The null and alternative hypotheses are modeled using the posterior distribution of the wavelet coefficients. WS is enhanced by using additional information from the regression coefficients and by taking advantage of the pyramidal structure of wavelets. When faced with more complex genetic signals than single-SNP associations, we show via simulations that WS provides a substantial gain in power compared to both the traditional GWAS modeling and another popular regional association test called SNP-set (Sequence) Kernel Association Test (SKAT). To demonstrate feasibility, we applied WS to a large Norwegian cohort (N=8006) with genotypes and information available on gestational duration. Conclusions WS is a powerful and versatile approach to analyzing whole-genome data and lends itself easily to investigating various omics data types. Given its broader focus on the genomic context of an association, WS may provide additional insight into trait etiology by revealing genes and loci that might have been missed by previous efforts.

Protocol for safe, affordable, and reproducible isolation and quantitation of SARS-CoV-2 RNA from wastewater

The following protocol describes our workflow for processing wastewater with the goal of detecting the genetic signal of SARS-CoV-2. The steps include pasteurization, virus concentration, RNA extraction, and quantification by RT-qPCR. We include auxiliary steps that provide new users with tools and strategies that will help troubleshoot key steps in the process. This protocol is one of the safest, cheapest, and most reproducible approaches for the detection of SARS-CoV-2 RNA in wastewater. Owing to a pasteurization step, it is safe for use in a BSL2 facility. In addition to making the protocol safe for the personnel involved, pasteurization had the added benefit of increasing the SARS-CoV-2 genetic signal. Furthermore, the RNA obtained using this protocol can be sequenced using both Sanger and Illumina sequencing technologies. The protocol was adopted by the New York City Department of Environmental Protection in August 2020 to monitor SARS-CoV-2 prevalence in wastewater in all five boroughs of the city. In the future, this protocol could be used to detect a variety of other clinically relevant viruses in wastewater and serve as a foundation of a wastewater surveillance strategy for monitoring community spread of known and emerging viral pathogens.

Clinical autism subscales have common genetic liability that is heritable, pleiotropic, and generalizable to the general population

The complexity of autism's phenotypic spectra is well-known, yet most genetic research uses case-control status as the target trait. It is unclear whether clinical autism instruments such as the Social Communication Questionnaire (SCQ), Repetitive Behaviors Scale-Revised (RBS-R), and Developmental Coordination Disorder Questionnaire (DCDQ) are more genetically informative than case-control. We employed the SPARK autism cohort (N = 6,449) to illuminate the genetic etiology of these twelve subscales. In comparison to the heritability of autism case-control at 0.12, the RBS-R subscales were increased, ranging from 0.18 to 0.30 (all p < 0.05). Heritability of the DCDQ subscales ranged from 0.07 to 0.09 and the SCQ subscales from 0 to 0.09 (all p > 0.05). We also found evidence for genetic correlations among the RBS-R, SCQ, and DCDQ. GWAS followed by projection of polygenic scores (PGS) into ABCD revealed significant associations with CBCL social and thought problems, while the autism case-control PGS did not significantly associate. In phenotypic correlation analyses, the autism case-control PGS did not predict the subscales in SPARK, and sex-stratified correlations showed no effect in males and a surprising negative effect in females. Notably, other PGS did predict the subscales, with the strongest being educational attainment negatively correlated, while ADHD and major depression were positively correlated. Overall, our analyses suggest that clinical subscales are more genetically powerful than case-control, and that of the three instruments investigated, the RBS-R shows the greatest evidence of common genetic signal in both autistic and general population samples.

Genetically increased circulating FUT3 level leads to reduced risk of Idiopathic Pulmonary Fibrosis: a Mendelian Randomisation Study

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal fibrotic interstitial lung disease. Few circulating biomarkers have been identified to have causal effects on IPF.To identify candidate IPF-influencing circulating proteins, we undertook an efficient screen of circulating proteins by applying a two-sample Mendelian randomisation (MR) approach with existing publicly available data. For instruments we used genetic determinants of circulating proteins which reside cis to the encoded gene (cis-SNPs), identified by two genome-wide association studies (GWASs) in European individuals (3301 and 3200 subjects). We then applied MR methods to test if the levels of these circulating proteins influenced IPF susceptibility in the largest IPF GWAS (2668 cases and 8591 controls). We validated the MR results using colocalization analyses to ensure that both the circulating proteins and IPF shared a common genetic signal.MR analyses of 834 proteins found that a one sd increase in circulating FUT3 and FUT5 was associated with a reduced risk of IPF (OR: 0.81, 95%CI: 0.74–0.88, p=6.3×10−7, and OR: 0.76, 95%CI: 0.68–0.86, p=1.1×10−5). Sensitivity analyses including multiple-cis SNPs provided similar estimates both for FUT3 (inverse variance weighted [IVW] OR: 0.84, 95%CI: 0.78–0.91, p=9.8×10−6, MR-Egger OR: 0.69, 95%CI: 0.50–0.97, p=0.03) and FUT5 (IVW OR: 0.84, 95%CI: 0.77–0.92, p=1.4×10−4, MR-Egger OR: 0.59, 95%CI: 0.38–0.90, p=0.01) FUT3 and FUT5 signals colocalized with IPF signals, with posterior probabilities of a shared genetic signal of 99.9% and 97.7%. Further transcriptomic investigations supported the protective effects of FUT3 for IPF.An efficient MR scan of 834 circulating proteins provided evidence that genetically increased circulating FUT3 level is associated with reduced risk of IPF.

Synergistic epistasis of the deleterious effects of transposable elements

The replicative nature and generally deleterious effects of transposable elements (TEs) give rise to an outstanding question about how TE copy number is stably contained in host populations. Classic theoretical analyses predict that, when the decline in fitness due to each additional TE insertion is greater than linear, or when there is synergistic epistasis, selection against TEs can result in a stable equilibrium of TE copy number. While several mechanisms are predicted to yield synergistic deleterious effects of TEs, we lack empirical investigations of the presence of such epistatic interactions. Purifying selection with synergistic epistasis generates repulsion linkage between deleterious alleles and, accordingly, an underdispersed distribution for the number of deleterious mutations among individuals. We investigated this population genetic signal in an African Drosophila melanogaster population and found evidence for synergistic epistasis among TE insertions, especially those expected to have large fitness impacts. Curiously, even though ectopic recombination has long been predicted to generate nonlinear fitness decline with increased TE copy number, TEs predicted to suffer higher rates of ectopic recombination are not more likely to be underdispersed. On the other hand, underdispersed TE families are more likely to show signatures of deleterious epigenetic effects and stronger ping-pong signals of piRNA amplification, a hypothesized source from which synergism of TE-mediated epigenetic effects arises. Our findings set the stage for investigating the importance of epistatic interactions in the evolutionary dynamics of TEs.

Deep genetic affinity between coastal Pacific and Amazonian natives evidenced by Australasian ancestry

Different models have been proposed to elucidate the origins of the founding populations of America, along with the number of migratory waves and routes used by these first explorers. Settlements, both along the Pacific coast and on land, have been evidenced in genetic and archeological studies. However, the number of migratory waves and the origin of immigrants are still controversial topics. Here, we show the Australasian genetic signal is present in the Pacific coast region, indicating a more widespread signal distribution within South America and implicating an ancient contact between Pacific and Amazonian dwellers. We demonstrate that the Australasian population contribution was introduced in South America through the Pacific coastal route before the formation of the Amazonian branch, likely in the ancient coastal Pacific/Amazonian population. In addition, we detected a significant amount of interpopulation and intrapopulation variation in this genetic signal in South America. This study elucidates the genetic relationships of different ancestral components in the initial settlement of South America and proposes that the migratory route used by migrants who carried the Australasian ancestry led to the absence of this signal in the populations of Central and North America.

Shared genetic architecture across psychiatric disorders

Psychiatric disorders overlap substantially at the genetic level, with family-based methods long pointing toward transdiagnostic risk pathways. Psychiatric genomics has progressed rapidly in the last decade, shedding light on the biological makeup of cross-disorder risk at multiple levels of analysis. Over a hundred genetic variants have been identified that affect multiple disorders, with many more to be uncovered as sample sizes continue to grow. Cross-disorder mechanistic studies build on these findings to cluster transdiagnostic variants into meaningful categories, including in what tissues or when in development these variants are expressed. At the upper-most level, methods have been developed to estimate the overall shared genetic signal across pairs of traits (i.e. single-nucleotide polymorphism-based genetic correlations) and subsequently model these relationships to identify overarching, genomic risk factors. These factors can subsequently be associated with external traits (e.g. functional imaging phenotypes) to begin to understand the makeup of these transdiagnostic risk factors. As psychiatric genomic efforts continue to expand, we can begin to gain even greater insight by including more fine-grained phenotypes (i.e. symptom-level data) and explicitly considering the environment. The culmination of these efforts will help to inform bottom-up revisions of our current nosology.