Biogeographical Ancestry Analyses Using the ForenSeqTM DNA Signature Prep Kit and Multiple Prediction Tools

The inference of biogeographical ancestry (BGA) can assist in police investigations of serious crime cases and help to identify missing people and victims of mass disasters. In this study, we evaluated the typing performance of 56 ancestry-informative SNPs in 177 samples using the ForenSeq™ DNA Signature Prep Kit on the MiSeq FGx system. Furthermore, we compared the prediction accuracy of the tools Universal Analysis Software v1.2 (UAS), the FROG-kb, and GenoGeographer when inferring the ancestry of 503 Europeans, 22 non-Europeans, and 5 individuals with co-ancestry. The kit was highly sensitive with complete aiSNP profiles in samples with as low as 250pg input DNA. However, in line with others, we observed low read depth and occasional drop-out in some SNPs. Therefore, we suggest not using less than the recommended 1ng of input DNA. FROG-kb and GenoGeographer accurately predicted both Europeans (99.6% and 91.8% correct, respectively) and non-Europeans (95.4% and 90.9% correct, respectively). The UAS was highly accurate when predicting Europeans (96.0% correct) but performed poorer when predicting non-Europeans (40.9% correct). None of the tools were able to correctly predict individuals with co-ancestry. Our study demonstrates that the use of multiple prediction tools will increase the prediction accuracy of BGA inference in forensic casework.

Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls

BACKGROUND

Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated.

METHODS

The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways.

RESULTS

Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified.

CONCLUSIONS

Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby.

PLAIN LANGUAGE SUMMARY

The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.

Including genomic information in the genetic evaluation of production and reproduction traits in South African Merino sheep

Genomic selection (GS) has become common in sheep breeding programmes in Australia, New Zealand, France and Ireland but requires validation in South Africa (SA). This study aimed to compare the predictive ability, bias and dispersion of pedigree BLUP (ABLUP) and single-step genomic BLUP (ssGBLUP) for production and reproduction traits in South African Merinos. Animals in this study originated from five research and five commercial Merino flocks and included between 54,072 and 79,100 production records for weaning weight (WW), yearling weight (YW), fibre diameter (FD), clean fleece weight (CFW) and staple length (SL). For reproduction traits, the dataset included 58,744 repeated records from 17,268 ewes for the number of lambs born (NLB), number of lambs weaned (NLW) and the total weight weaned (TWW). The single-step relationship matrix, H, was calculated using PreGS90 software combining 2811 animals with medium density (~50 K) genotypes and the pedigree of 88,600 animals. Assessment was based on single-trait analysis using ASREML V4.2 software. The accuracy of prediction was evaluated according to the 'LR-method' by a cross-validation design. Validation candidates were assigned according to Scenario I: born after a certain time point; and Scenario II: born in a particular flock. In Scenario I, the genotyping rate for the reference population between 2006 and the 2013 cut-off point approached 7% of animals with phenotypes and 20% of their sires. For reproduction traits, about 20% of ewes born between 2006 and 2011 cut-off were genotyped, along with 15% of their sires. Genotyping rates in the validation set of Scenario I were 3.7% (production) and 11.4% (reproduction) for candidates and 35% of their sires. Sires were allowed to have progeny in both the reference and validation set. In Scenario I, ssGBLUP increased the accuracy of prediction for all traits except NLB, ranging between +8% (0.62-0.67) for FD and +44% (0.36-0.52) for WW. This showed a promising gain in accuracy despite a modestly sized reference population. In the 'across flock validation' (Scenario II), overall accuracy was lower, but with greater differences between ABLUP and ssGBLUP ranging between +17% (0.12-0.14) for TWW and +117% (0.18-0.39) for WW. There was little indication of severe bias, but some traits were prone to over dispersion and the use of genomic information did not improve this. These results were the first to validate the benefit of genomic information in South African Merinos. However, because production traits are moderately heritable and easy to measure at an early age, future research should be aimed at best exploiting GS methods towards genetic prediction of sex-limited and/or lowly heritable traits such as NLW. GS methods should be combined with dedicated efforts to increase genetic links between sectors and improved phenotyping by commercial flocks.

mtPGS: Leverage multiple correlated traits for accurate polygenic score construction

Accurate polygenic scores (PGSs) facilitate the genetic prediction of complex traits and aid in the development of personalized medicine. Here, we develop a statistical method called multi-trait assisted PGS (mtPGS), which can construct accurate PGSs for a target trait of interest by leveraging multiple traits relevant to the target trait. Specifically, mtPGS borrows SNP effect size similarity information between the target trait and its relevant traits to improve the effect size estimation on the target trait, thus achieving accurate PGSs. In the process, mtPGS flexibly models the shared genetic architecture between the target and the relevant traits to achieve robust performance, while explicitly accounting for the environmental covariance among them to accommodate different study designs with various sample overlap patterns. In addition, mtPGS uses only summary statistics as input and relies on a deterministic algorithm with several algebraic techniques for scalable computation. We evaluate the performance of mtPGS through comprehensive simulations and applications to 25 traits in the UK Biobank, where in the real data mtPGS achieves an average of 0.90%-52.91% accuracy gain compared to the state-of-the-art PGS methods. Overall, mtPGS represents an accurate, fast, and robust solution for PGS construction in biobank-scale datasets.

PGS-server: accuracy, robustness and transferability of polygenic score methods for biobank scale studies

Polygenic scores (PGS) are important tools for carrying out genetic prediction of common diseases and disease related complex traits, facilitating the development of precision medicine. Unfortunately, despite the critical importance of PGS and the vast number of PGS methods recently developed, few comprehensive comparison studies have been performed to evaluate the effectiveness of PGS methods. To fill this critical knowledge gap, we performed a comprehensive comparison study on 12 different PGS methods through internal evaluations on 25 quantitative and 25 binary traits within the UK Biobank with sample sizes ranging from 147 408 to 336 573, and through external evaluations via 25 cross-study and 112 cross-ancestry analyses on summary statistics from multiple genome-wide association studies with sample sizes ranging from 1415 to 329 345. We evaluate the prediction accuracy, computational scalability, as well as robustness and transferability of different PGS methods across datasets and/or genetic ancestries, providing important guidelines for practitioners in choosing PGS methods. Besides method comparison, we present a simple aggregation strategy that combines multiple PGS from different methods to take advantage of their distinct benefits to achieve stable and superior prediction performance. To facilitate future applications of PGS, we also develop a PGS webserver (http://www.pgs-server.com/) that allows users to upload summary statistics and choose different PGS methods to fit the data directly. We hope that our results, method and webserver will facilitate the routine application of PGS across different research areas.

Genetic prediction of complex traits with polygenic scores: a statistical review

Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.

Genetics, its role in preventing the pandemic of coronary artery disease

Epidemiologists have claimed for decades that about 50% of predisposition for coronary artery disease (CAD) is genetic. Advances in technology made possible the discovery of hundreds of genetic risk variants predisposing to CAD. Multiple clinical trials have shown that cardiac events can be prevented by drugs to lower plasma low-density lipoprotein cholesterol (LDL-C). A major barrier to primary prevention is the lack of markers to identify those individuals at risk prior to the development of symptoms of the disease. Conventional risk factors are age-dependent, occurring mostly in the sixth or seventh decade, which is less than desirable for early primary prevention. A polygenic risk score, derived from the number of genetic risk variants predisposing to CAD inherited by an individual, has been evaluated in over 1 million individuals. The risk for CAD is stratified into high, intermediate, and low. Polygenic risk scores derived from retrospective genotyping of several clinical trials evaluating the effect of statin therapy or PCSK9 inhibitors show the genetic risk is reduced 40%-50% by decreasing plasma LDL-C. Prospective randomized placebo-controlled clinical trials document a 40%-50% reduction in cardiac events in individuals at high genetic risk associated with favorable lifestyle changes and increased physical activity. The polygenic risk score is not age-dependent and remains the same throughout life. Thus, the GRS is superior to conventional risk factors in identifying asymptomatic individuals at risk for CAD early in life for primary prevention. These results indicate clinical embracement of the GRS in primary prevention would be a paradigm shift in the treatment of the number one killer, CAD.

Categorization of birth weight phenotypes for inclusion in genetic evaluations using a deep neural network

Birth weight (BW) serves as a valuable indicator of the economically relevant trait of calving ease (CE), and erroneous data collection for BW could impact genetic evaluations for CE. The objective of the current study was to evaluate the use of deep neural networks (DNNs) for classifying contemporary groups (CGs) based on the method used to generate BW phenotypes. CGs (n = 120,000,000) ranging between 10 and 250 animals were simulated assuming 12 data collection and CG formation scenarios that could impact CG phenotypic variance, including weights recorded with a digital scale (REAL), hoof tape (TAPE), erroneous data collection (DIRTY), and those that were fabricated (FAB). The performance of eight activation functions (AFs; ReLu, Sigmoid, Exponential, ReLu6, Softmax, Softplus, Leaky ReLu, and Tanh) was evaluated. Four hidden layers were used with seven different scenarios relative to the number of neurons. Simulations were replicated 10 times. In general, accuracy (proportion of correct predictions) across AF and numbers of neurons were similar, with mean correlations ranging between 0.91 and 0.99. The AF ReLu, Sigmoid, Exponential, and ReLu6 had the greatest consistency (mean pair-wise correlation among replicates) with an average correlation of greater than 0.85. Independent of the number of neurons used, the sigmoid function produced the highest accuracy (0.99) and consistency (0.93). The model with the greatest accuracy and consistency was then applied to real BW data supplied by the American Hereford Association. In the real data, the lowest phenotypic variance was for FAB CG (2.65 kg2), REAL CG had the largest (15.84 kg2), and TAPE CG was intermediate (6.84 kg2). To investigate the potential impact of FAB data on routine genetic evaluations, CGs classified as FAB in 90% or more of the replicates were removed from the evaluation for CE, and the rank of resulting genetic predictions were compared with the case where records were not removed. The removal of FAB CG had a moderate impact on the prediction of CE expected progeny differences, primarily for animals with intermediate to high accuracy. The results suggest that a well-trained DNN can be effectively used to classify data based on quality metrics prior to the inclusion in routine genetic evaluation.

Inheritance of Rootstock Effects in Avocado (Persea americana Mill.) cv. Hass

Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique tree phenotype. However, the interconnection of both genotypes obscures individual contributions to phenotypic variation (rootstock-mediated heritability), hampering tree breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as a model fruit tree. We characterized 240 diverse rootstocks from 8 avocado cv. Hass orchards with similar management in three regions of the province of Antioquia, northwest Andes of Colombia, using 13 microsatellite markers simple sequence repeats (SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, biomass/reproductive, and fruit yield and quality traits) in the scions for 3 years (2015-2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a "genetic prediction" model to calculate narrow-sense heritabilities (h 2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (r) that oscillated between 0.58 and 0.73 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e., total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), whereas the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock-scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in "Hass" avocado. They also reinforce the utility of polymorphic SSRs for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit tree crop. Ultimately, our work highlights the importance of considering the rootstock-scion interaction to broaden the genetic basis of fruit tree breeding programs while enhancing our understanding of the consequences of grafting.

Prediction of colorectal cancer risk based on profiling with common genetic variants

Increasing numbers of common genetic variants associated with colorectal cancer (CRC) have been identified. Our study aimed to determine whether risk prediction based on common genetic variants might enable stratification for CRC risk. Meta-analysis of 11 genome-wide association studies comprising 16 871 cases and 26 328 controls was performed to capture CRC susceptibility variants. Genetic prediction models with several candidate polygenic risk scores (PRSs) were generated from Scottish CRC case-control studies (6478 cases and 11 043 controls) and the score with the best performance was then tested in UK Biobank (UKBB) (4800 cases and 20 287 controls). A weighted PRS of 116 CRC single nucleotide polymorphisms (wPRS116 ) was found with the best predictive performance, reporting a c-statistics of 0.60 and an odds ratio (OR) of 1.46 (95% confidence interval [CI] = 1.41-1.50, per SD increase) in Scottish data set. The predictive performance of this wPRS116 was consistently validated in UKBB data set with c-statistics of 0.61 and an OR of 1.49 (95% CI = 1.44-1.54, per SD increase). Modeling the levels of PRS with age and sex in the general UK population shows that employing genetic risk profiling can achieve a moderate degree of risk discrimination that could be helpful to identify a subpopulation with higher CRC risk due to genetic susceptibility.

Mutations primarily alter the inclusion of alternatively spliced exons

Genetic analyses and systematic mutagenesis have revealed that synonymous, non-synonymous and intronic mutations frequently alter the inclusion levels of alternatively spliced exons, consistent with the concept that altered splicing might be a common mechanism by which mutations cause disease. However, most exons expressed in any cell are highly-included in mature mRNAs. Here, by performing deep mutagenesis of highly-included exons and by analysing the association between genome sequence variation and exon inclusion across the transcriptome, we report that mutations only very rarely alter the inclusion of highly-included exons. This is true for both exonic and intronic mutations as well as for perturbations in trans. Therefore, mutations that affect splicing are not evenly distributed across primary transcripts but are focussed in and around alternatively spliced exons with intermediate inclusion levels. These results provide a resource for prioritising synonymous and other variants as disease-causing mutations.

Robust Huber-LASSO for improved prediction of protein, metabolite and gene expression levels relying on individual genotype data

Least absolute shrinkage and selection operator (LASSO) regression is often applied to select the most promising set of single nucleotide polymorphisms (SNPs) associated with a molecular phenotype of interest. While the penalization parameter λ restricts the number of selected SNPs and the potential model overfitting, the least-squares loss function of standard LASSO regression translates into a strong dependence of statistical results on a small number of individuals with phenotypes or genotypes divergent from the majority of the study population—typically comprised of outliers and high-leverage observations.

Robust methods have been developed to constrain the influence of divergent observations and generate statistical results that apply to the bulk of study data, but they have rarely been applied to genetic association studies. In this article, we review, for newcomers to the field of robust statistics, a novel version of standard LASSO that utilizes the Huber loss function. We conduct comprehensive simulations and analyze real protein, metabolite, mRNA expression and genotype data to compare the stability of penalization, the cross-iteration concordance of the model, the false-positive and true-positive rates and the prediction accuracy of standard and robust Huber-LASSO.

Although the two methods showed controlled false-positive rates ≤2.1% and similar true-positive rates, robust Huber-LASSO outperformed standard LASSO in the accuracy of predicted protein, metabolite and gene expression levels using individual SNP data. The conducted simulations and real-data analyses show that robust Huber-LASSO represents a valuable alternative to standard LASSO in genetic studies of molecular phenotypes.

A Between Ethnicities Comparison of Chronic Obstructive Pulmonary Disease Genetic Risk

Heterogeneity of lung function levels and risk for developing chronic obstructive pulmonary disease (COPD) among people exposed to the same environmental risk factors, such as cigarette smoking, suggest an important role of genetic factors in COPD susceptibility. To investigate the possible role of different genetic factors in COPD susceptibility across ethnicities. We used a population-stratified analysis for: (i) identifying ethnic-specific genetic susceptibility loci, (ii) developing ethnic-specific polygenic risk prediction models using those SNPs, and (iii) validating the models with an independent dataset. We elucidated substantial differences in SNP heritability and susceptibility loci for the disease across ethnicities. Furthermore, the application of three ethnic-specific prediction models to an independent dataset showed that the best performance is achieved when the prediction model is applied to a dataset with the matched ethnic sample. Our study validates the necessity of considering ethnic differences in COPD risk; understanding these differences might help in preventing COPD and developing therapeutic strategies.

Random regression of Hereford percentage intramuscular fat on geographical coordinates

Accounting for genotype-environment interactions may improve genetic prediction and parameter estimation. The objective was to use random regression analyses to estimate variances and thereby heritability for intramuscular fat (IMF) across longitude and latitude coordinates within the continental United States. Records from the American Hereford Association (n = 169,440) were used. Analyses were first conducted using the continental United States in its entirety, and then as subdivided into two or four regions. Data were analyzed with an animal model, and linear and quadratic random regressions of additive genetic merit on longitude or latitude as covariate (separately). Subdivided data were analyzed with linear random regressions unique to regions. Regions were North and South separated at 40°N latitude, or West and East separated at 99°W longitude using longitude or latitude as covariate, respectively. Further subdivision to four regions included additional boundaries of 44.46° and 36.46°N latitude and 104.55° and 92.22°W longitude. The estimated heritability of IMF from the traditional model was 0.19 ± 0.004. Without regional subdivision of data, quadratic random regression had the best fit for the data based on likelihood ratio tests using longitude or latitude as covariate (P < 0.01). Estimates of heritability from quadratic random regression on latitude ranged from 0.12 in the South to a high of 0.27 at the extreme Northern latitude. Estimates of heritability from quadratic random regression on longitude ranged from 0.17 in the middle of the parameter space (corresponding to the central United States) to 0.37; higher estimates were noted at the extremes, that is, the far West and East longitudes. Random regression analyses of data divided into regions were conducted with a linear coefficient, as increasing to a quadratic polynomial was never accomplished. Results from random regression on latitude in the East region were similar to results from analyses without regions (h2 ranged from 0.09 to 0.32); however, estimates of heritability in the West region had a lower range from South to North (0.14 to 0.27). Estimates of heritability from random regression on longitude with data divided into two regions were similar to those from analyses that did not include region. Estimates in the South region were somewhat lower and had a lower range (0.15 to 0.31) than those from the North region (0.19 to 0.47). When data were further subdivided, estimation of only a subset of covariances among random regression coefficients was possible, that is, within-region covariances of intercept and linear terms (latitude); those and covariances between all linear random regression coefficients were estimated when longitude was the covariate. Results from random regression analyses of data with four regions modeled produced very high estimates of heritability in low latitudes in the furthest West and high latitudes in the furthest East region, with approximate difference of 0.3 and 0.2 between estimates in the two West regions and the two East regions, respectively. Results from random regression on longitude indicated higher estimates of heritability in North region, especially at the furthest East longitudes of the most Northern region. There appeared to be substantial additive genetic variance differences, as well as estimates of heritability, that correspond to different geographical environments as modeled by random regressions on within-region latitude or longitude coordinates.

Direct-to-consumer genetic testing

Direct-to-consumer (DTC) genetic testing is a controversial issue although Korean Government is considering to expand DTC genetic testing. Preventing the exaggeration and abusing of DTC genetic testing is an important task considering the early history of DTC genetic testing in Korea. And the DTC genetic testing performance or method has been rarely reported to the scientific and/or medical community and reliability of DTC genetic testing needs to be assessed. Law enforcement needs to improve these issues. Also principle of transparency needs to be applied.

Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

Geographic Variation and Bias in the Polygenic Scores of Complex Diseases and Traits in Finland

Polygenic scores (PSs) are becoming a useful tool to identify individuals with high genetic risk for complex diseases, and several projects are currently testing their utility for translational applications. It is also tempting to use PSs to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how the population genetic properties of the training and target samples affect the geographic distribution of PSs. Here, we evaluate geographic differences, and related biases, of PSs in Finland in a geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PSs for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waist-hip ratio (WHR), body-mass index (BMI), and height, but not for Crohn disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PSs and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulations of geographic differences for CAD, WHR, BMI, and height, suggesting bias arising from the population's genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PSs are for small biases even within relatively homogeneous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PSs is essential for translational applications of PSs.

Combinatorial Genetics Reveals a Scaling Law for the Effects of Mutations on Splicing

Despite a wealth of molecular knowledge, quantitative laws for accurate prediction of biological phenomena remain rare. Alternative pre-mRNA splicing is an important regulated step in gene expression frequently perturbed in human disease. To understand the combined effects of mutations during evolution, we quantified the effects of all possible combinations of exonic mutations accumulated during the emergence of an alternatively spliced human exon. This revealed that mutation effects scale non-monotonically with the inclusion level of an exon, with each mutation having maximum effect at a predictable intermediate inclusion level. This scaling is observed genome-wide for cis and trans perturbations of splicing, including for natural and disease-associated variants. Mathematical modeling suggests that competition between alternative splice sites is sufficient to cause this non-linearity in the genotype-phenotype map. Combining the global scaling law with specific pairwise interactions between neighboring mutations allows accurate prediction of the effects of complex genotype changes involving >10 mutations.

A genetic risk score and number of myopic parents independently predict myopia

PURPOSE

To investigate whether a genetic risk score (GRS) improved performance of predicting refractive error compared to knowing a child's number of myopic parents (NMP) alone.

METHODS

This was a retrospective analysis of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort study. Refractive error was assessed longitudinally between age 7-15 using non-cycloplegic autorefraction. Genetic variants (n = 149) associated with refractive error from a Consortium for Refractive Error And Myopia (CREAM) genome-wide association study were used to calculate a GRS for each child. Using refractive error at ages 7 and 15 years as the outcome variable, coefficient of determination (R² ) values were calculated via linear regression models for the predictors: NMP, GRS and a combined model.

RESULTS

Number of myopic parents was weakly predictive of refractive error in children aged 7 years, R²  = 3.0% (95% CI 1.8-4.1%, p < 0.0001) and aged 15 years, R²  = 4.8% (3.1-6.5%, p < 0.0001). The GRS was also weakly predictive; age 7 years, R²  = 1.1% (0.4-1.9%, p < 0.0001) and 15 years R²  = 2.6% (1.3-3.9%, p < 0.0001). Combining the 2 variables gave larger R² values at age 7, R²  = 3.7% (2.5-5.0%, p < 0.0001) and 15, R²  = 7.0% (5.0-9.0%, p < 0.0001). The combined model improved performance at both ages (both p < 0.0001).

CONCLUSION

A GRS improved the ability to detect children at risk of myopia independently of knowing the NMP. We speculate this may be because NMP captures information concerning environmental risk factors for myopia. Nevertheless, further gains are required to make such predictive tests worthwhile in the clinical environment.

Predicting loneliness with polygenic scores of social, psychological and psychiatric traits

Loneliness is a heritable trait that accompanies multiple disorders. The association between loneliness and mental health indices may partly be due to inherited biological factors. We constructed polygenic scores for 27 traits related to behavior, cognition and mental health and tested their prediction for self-reported loneliness in a population-based sample of 8798 Dutch individuals. Polygenic scores for major depressive disorder (MDD), schizophrenia and bipolar disorder were significantly associated with loneliness. Of the Big Five personality dimensions, polygenic scores for neuroticism and conscientiousness also significantly predicted loneliness, as did the polygenic scores for subjective well-being, tiredness and self-rated health. When including all polygenic scores simultaneously into one model, only 2 major depression polygenic scores remained as significant predictors of loneliness. When controlling only for these 2 MDD polygenic scores, only neuroticism and schizophrenia remain significant. The total variation explained by all polygenic scores collectively was 1.7%. The association between the propensity to feel lonely and the susceptibility to psychiatric disorders thus pointed to a shared genetic etiology. The predictive power of polygenic scores will increase as the power of the genome-wide association studies on which they are based increases and may lead to clinically useful polygenic scores that can inform on the genetic predisposition to loneliness and mental health.

Nationwide Genomic Study in Denmark Reveals Remarkable Population Homogeneity

Denmark has played a substantial role in the history of Northern Europe. Through a nationwide scientific outreach initiative, we collected genetic and anthropometrical data from ∼800 high school students and used them to elucidate the genetic makeup of the Danish population, as well as to assess polygenic predictions of phenotypic traits in adolescents. We observed remarkable homogeneity across different geographic regions, although we could still detect weak signals of genetic structure reflecting the history of the country. Denmark presented genomic affinity with primarily neighboring countries with overall resemblance of decreasing weight from Britain, Sweden, Norway, Germany, and France. A Polish admixture signal was detected in Zealand and Funen, and our date estimates coincided with historical evidence of Wend settlements in the south of Denmark. We also observed considerably diverse demographic histories among Scandinavian countries, with Denmark having the smallest current effective population size compared to Norway and Sweden. Finally, we found that polygenic prediction of self-reported adolescent height in the population was remarkably accurate (R² = 0.639 ± 0.015). The high homogeneity of the Danish population could render population structure a lesser concern for the upcoming large-scale gene-mapping studies in the country.

Smooth-Threshold Multivariate Genetic Prediction with Unbiased Model Selection

We develop a new genetic prediction method, smooth-threshold multivariate genetic prediction, using single nucleotide polymorphisms (SNPs) data in genome-wide association studies (GWASs). Our method consists of two stages. At the first stage, unlike the usual discontinuous SNP screening as used in the gene score method, our method continuously screens SNPs based on the output from standard univariate analysis for marginal association of each SNP. At the second stage, the predictive model is built by a generalized ridge regression simultaneously using the screened SNPs with SNP weight determined by the strength of marginal association. Continuous SNP screening by the smooth thresholding not only makes prediction stable but also leads to a closed form expression of generalized degrees of freedom (GDF). The GDF leads to the Stein's unbiased risk estimation (SURE), which enables data-dependent choice of optimal SNP screening cutoff without using cross-validation. Our method is very rapid because computationally expensive genome-wide scan is required only once in contrast to the penalized regression methods including lasso and elastic net. Simulation studies that mimic real GWAS data with quantitative and binary traits demonstrate that the proposed method outperforms the gene score method and genomic best linear unbiased prediction (GBLUP), and also shows comparable or sometimes improved performance with the lasso and elastic net being known to have good predictive ability but with heavy computational cost. Application to whole-genome sequencing (WGS) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) exhibits that the proposed method shows higher predictive power than the gene score and GBLUP methods.

Genetic prediction in the Genetic Analysis Workshop 18 sequencing data

High-throughput sequencing data can be used to predict phenotypes from genotypes, and this corresponds to establishing a prognostic model. In extended pedigrees the relatedness of subjects provides additional information so that genetic values, fixed or random genetic components, and heritability can be estimated. At the Genetic Analysis Workshop 18, the working group on genetic prediction dealt with both establishing a prognostic model and, in one contribution, comparing standard logistic regression with robust logistic regression in a sample of unrelated affected or unaffected individuals. Results of both logistic regression approaches were similar. All other contributions to this group used extended family data, in general using the quantitative trait blood pressure. The individual contributions varied in several important aspects, such as the estimation of the kinship matrix and the estimation method. Contributors chose various approaches for model validation, including different versions of cross-validation or within-family validation. Within-family validation included model building in the upper generations and validation in later generations. The choice of the statistical model and the computational algorithm had substantial effects on computation time. If decorrelation approaches were applied, the computational burden was substantially reduced. Some software packages estimated negative eigenvalues, although eigenvalues of correlation matrices should be non-negative. Most statistical models and software packages have been developed for experimental crosses and planned breeding programs. With their specialized pedigree structures, they are not sufficiently flexible to accommodate the variability of human pedigrees in general, and improved implementations are required.

Applied animal genomics: results from the field

Genomic selection (GS) is the use of statistical methods to estimate the genetic merit of a genotyped animal based on prediction equations derived from large ancestral populations with both phenotypes and genotypes. It has revolutionized the dairy cattle breeding industry and has been implemented with varying degrees of success in other animal breeding programs, including swine, poultry, and beef cattle. The findings of empirical field studies applying GS to the breeding sectors of these main animal protein industries are reviewed. Several translational considerations must be addressed before implementing GS in genetic improvement programs. These include determining and obtaining economically relevant phenotypes and determining the optimal size of the training population, cost-effective genotyping strategies, the practicality of field implementation, and the relative costs versus the benefits of the realized rate of genetic gain. GS may additionally change the optimal breeding scheme design, and studies that address this consideration are also reviewed briefly.

Whole genome sequencing in support of wellness and health maintenance

Background

Whole genome sequencing is poised to revolutionize personalized medicine, providing the capacity to classify individuals into risk categories for a wide range of diseases. Here we begin to explore how whole genome sequencing (WGS) might be incorporated alongside traditional clinical evaluation as a part of preventive medicine. The present study illustrates novel approaches for integrating genotypic and clinical information for assessment of generalized health risks and to assist individuals in the promotion of wellness and maintenance of good health.

Methods

Whole genome sequences and longitudinal clinical profiles are described for eight middle-aged Caucasian participants (four men and four women) from the Center for Health Discovery and Well Being (CHDWB) at Emory University in Atlanta. We report multivariate genotypic risk assessments derived from common variants reported by genome-wide association studies (GWAS), as well as clinical measures in the domains of immune, metabolic, cardiovascular, musculoskeletal, respiratory, and mental health.

Results

Polygenic risk is assessed for each participant for over 100 diseases and reported relative to baseline population prevalence. Two approaches for combining clinical and genetic profiles for the purposes of health assessment are then presented. First we propose conditioning individual disease risk assessments on observed clinical status for type 2 diabetes, coronary artery disease, hypertriglyceridemia and hypertension, and obesity. An approximate 2:1 ratio of concordance between genetic prediction and observed sub-clinical disease is observed. Subsequently, we show how more holistic combination of genetic, clinical and family history data can be achieved by visualizing risk in eight sub-classes of disease. Having identified where their profiles are broadly concordant or discordant, an individual can focus on individual clinical results or genotypes as they develop personalized health action plans in consultation with a health partner or coach.

Conclusion

The CHDWB will facilitate longitudinal evaluation of wellness-focused medical care based on comprehensive self-knowledge of medical risks.