Evolutionary perspectives on polygenic selection, missing heritability, and GWAS

Genomic diversity in functionally relevant genes modifies neurodevelopmental versus neoplastic risks in individuals with germline PTEN variants

Individuals with germline PTEN variants (PHTS) have increased risks of the seemingly disparate phenotypes of cancer and neurodevelopmental disorders (NDD), including autism spectrum disorder (ASD). Etiology of the phenotypic variability remains elusive. Here, we hypothesized that decreased genomic diversity, manifested by increased homozygosity, may be one etiology. Comprehensive analyses of 376 PHTS patients of European ancestry revealed significant enrichment of homozygous common variants in genes involved in inflammatory processes in the PHTS-NDD group and in genes involved in differentiation and chromatin structure regulation in the PHTS-ASD group. Pathway analysis revealed pathways germane to NDD/ASD, including neuroinflammation and synaptogenesis. Collapsing analysis of the homozygous variants identified suggestive modifier NDD/ASD genes. In contrast, we found enrichment of homozygous ultra-rare variants in genes modulating cell death in the PHTS-cancer group. Finally, homozygosity burden as a predictor of ASD versus cancer outcomes in our validated prediction model for NDD/ASD performed favorably.

Genetic susceptibility to lung squamous cell carcinoma: new insights on 9q33.2 variants and tobacco smoking

Genome-wide association studies (GWAS) have identified over 60 susceptibility loci for lung cancer, yet the biological mechanisms underlying these associations remain largely unknown, particularly for lung squamous cell carcinoma (LUSC). Here, we integrated data from 3890 LUSC cases and 13 328 controls of Chinese descent, and performed a conditional analysis to explore independent genetic variants and analyzed the interaction between the genetic variants and smoking. Our study was the first to identify a specific association between genetic variants in the 9q33.2 region and increased risk of LUSC in smokers. After adjusting for the tag SNP rs4573350 in 9q33.2, no additional significant genetic variants were found. However, significant additive (RERI = 1.66, 95% CI: 1.17-2.22, AP = 0.26, 95% CI: 0.19-0.33) and multiple interactions (OR = 1.30, 95% CI: 1.08-1.56, P = 5.40 × 10-3) were observed between rs4573350 and smoking. Compared to nonsmokers with the CC genotype, smokers with the CT/TT genotype showed an increased risk of 6.29-fold (95% CI: 5.46-7.23, P = 2.00 × 10-16). Functional annotation identified rs4573350 as the strongest functional variant within the linkage disequilibrium block. Biological experiments confirmed that the combined exposure to the T allele of rs4573350 and cigarette smoke extract promotes the expression of the ZBTB26 by modulating the binding ability of the transcription factor FOXA1. Furthermore, ZBTB26 was found to regulate tumorigenesis of LUSC both in vitro and in vivo by affecting the expression of PCNA, which is involved in cell cycle and promotes tumorigenesis of LUSC.

Genetic Analysis and Predictive Modeling of COVID-19 Severity in a Hospital-Based Patient Cohort

The COVID-19 pandemic has had a devastating impact, with more than 7 million deaths worldwide. Advanced age and comorbidities partially explain severe cases of the disease, but genetic factors also play a significant role. Genome-wide association studies (GWASs) have been instrumental in identifying loci associated with SARS-CoV-2 infection. Here, we report the results from a >820 K variant GWAS in a COVID-19 patient cohort from the hospitals associated with IIS Biobizkaia. We compared intensive care unit (ICU)-hospitalized patients with non-ICU-hospitalized patients. The GWAS was complemented with an integrated phenotype and genetic modeling analysis using HLA genotypes, a previously identified COVID-19 polygenic risk score (PRS) and clinical data. We identified four variants associated with COVID-19 severity with genome-wide significance (rs58027632 in KIF19; rs736962 in HTRA1; rs77927946 in DMBT1; and rs115020813 in LINC01283). In addition, we designed a multivariate predictive model including HLA, PRS and clinical data which displayed an area under the curve (AUC) value of 0.79. Our results combining human genetic information with clinical data may help to improve risk assessment for the development of a severe outcome of COVID-19.

Sibling similarity can reveal key insights into genetic architecture

The use of siblings to infer the factors influencing complex traits has been a cornerstone of quantitative genetics. Here, we utilise siblings for a novel application: the inference of genetic architecture, specifically that relating to individuals with extreme trait values (e.g. in the top 1%). Inferring the genetic architecture most relevant to this group of individuals is important because they are at the greatest risk of disease and may be more likely to harbour rare variants of large effect due to natural selection. We develop a theoretical framework that derives expected distributions of sibling trait values based on an index sibling's trait value, estimated trait heritability, and null assumptions that include infinitesimal genetic effects and environmental factors that are either controlled for or have combined Gaussian effects. This framework is then used to develop statistical tests powered to distinguish between trait tails characterised by common polygenic architecture from those that include substantial enrichments of de novo or rare variant (Mendelian) architecture. We apply our tests to UK Biobank data here, although we note that they can be used to infer genetic architecture in any cohort or health registry that includes siblings and their trait values, since these tests do not use genetic data. We describe how our approach has the potential to help disentangle the genetic and environmental causes of extreme trait values, and to improve the design and power of future sequencing studies to detect rare variants.

Supergenes are not necessary to explain the maintenance of complex alternative phenotypes

Evolutionary biology aims to explain the diversity seen in nature. Evolutionary theory provides frameworks to understand how simple polymorphisms or continuous variation are maintained, but phenotypes inherited as discrete suites of quantitative traits are difficult to fit into this framework. Supergenes have been proposed as a solution to this problem-if causal genes are co-located, they can be inherited as if a single gene, thus bridging the gap between simple polymorphisms and continuous traits. We develop models to ask: how are critical supergenes for maintaining phenotypic diversity? In our simplest model, without explicit genetic architectures, three alternative reproductive morphs are maintained in many of the parameter combinations we evaluated. For these same parameter values, models with demographic stochasticity, recombination and mutation (but without explicit genetic architecture) maintained only two of these three morphs, with stochasticity determining which morphs persisted. With explicit genetic architectures, regardless of whether causal loci were co-located in a supergene or distributed randomly, this stochasticity in which morphs are maintained was reduced. Even when phenotypic variation was lost, genetic diversity was maintained. Altogether, categorical traits with polygenic bases exhibited similar evolutionary dynamics to those determined by supergenes. Our work suggests that supergenes are not the only answer to the puzzle of how discrete polygenic phenotypic variation is maintained.

Genomic diversity in functionally relevant genes modifies neurodevelopmental versus neoplastic risks in individuals with germline PTEN variants

Individuals with germline PTEN variants (PHTS) have increased risks of the seemingly disparate phenotypes of cancer and neurodevelopmental disorders (NDD), including autism spectrum disorder (ASD). Etiology of the phenotypic variability remains elusive. Here, we hypothesized that decreased genomic diversity, manifested by increased homozygosity, may be one etiology. Comprehensive analyses of 376 PHTS patients of European ancestry revealed significant enrichment of homozygous common variants in genes involved in inflammatory processes in the PHTS-NDD group and in genes involved in differentiation and chromatin structure regulation in the PHTS-ASD group. Pathway analysis revealed pathways germane to NDD/ASD, including neuroinflammation and synaptogenesis. Collapsing analysis of the homozygous variants identified suggestive modifier NDD/ASD genes. In contrast, we found enrichment of homozygous ultra-rare variants in genes modulating cell death in the PHTS-cancer group. Finally, homozygosity burden as a predictor of ASD versus cancer outcomes in our validated prediction model for NDD/ASD performed favorably.

Competition-cooperation mechanism between Escherichia coli and Staphylococcus aureus based on systems mapping

Introduction

Interspecies interactions are a crucial driving force of species evolution. The genes of each coexisting species play a pivotal role in shaping the structure and function within the community, but how to identify them at the genome-wide level has always been challenging.

Methods

In this study, we embed the Lotka-Volterra ordinary differential equations in the theory of community ecology into the systems mapping model, so that this model can not only describe how the quantitative trait loci (QTL) of a species directly affects its own phenotype, but also describe the QTL of the species how to indirectly affect the phenotype of its interacting species, and how QTL from different species affects community behavior through epistatic interactions.

Results

By designing and implementing a co-culture experiment for 100 pairs of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), we mapped 244 significant QTL combinations in the interaction process of the two bacteria using this model, including 69 QTLs from E. coli and 59 QTLs from S. aureus, respectively. Through gene annotation, we obtained 57 genes in E. coli, among which the genes with higher frequency were ypdC, nrfC, yphH, acrE, dcuS, rpnE, and ptsA, while we obtained 43 genes in S. aureus, among which the genes with higher frequency were ebh, SAOUHSC_00172, capF, gdpP, orfX, bsaA, and phnE1.

Discussion

By dividing the overall growth into independent growth and interactive growth, we could estimate how QTLs modulate interspecific competition and cooperation. Based on the quantitative genetic model, we can obtain the direct genetic effect, indirect genetic effect, and genome-genome epistatic effect related to interspecific interaction genes, and then further mine the hub genes in the QTL networks, which will be particularly useful for inferring and predicting the genetic mechanisms of community dynamics and evolution. Systems mapping can provide a tool for studying the mechanism of competition and cooperation among bacteria in co-culture, and this framework can lay the foundation for a more comprehensive and systematic study of species interactions.

Characterizing the polygenic architecture of complex traits in populations of East Asian and European descent

To investigate the polygenicity of complex traits in populations of East Asian (EAS) and European (EUR) descents, we leveraged genome-wide data from Biobank Japan, UK Biobank, and FinnGen cohorts. Specifically, we analyzed up to 215 outcomes related to 18 health domains, assessing their polygenic architecture via descriptive statistics, such as the proportion of susceptibility SNPs per trait (πc). While we did not observe EAS-EUR differences in the overall distribution of polygenicity parameters across the phenotypes investigated, there were ancestry-specific patterns in the polygenicity differences between health domains. In EAS, pairwise comparisons across health domains showed enrichment for πc differences related to hematological and metabolic traits (hematological fold-enrichment = 4.45, p = 2.15 × 10-7; metabolic fold-enrichment = 4.05, p = 4.01 × 10-6). For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains (EAS-hematological median πc = 0.15%, EAS-metabolic median πc = 0.18%) with the strongest πc difference with respect to respiratory traits (EAS-respiratory median πc = 0.50%; hematological-p = 2.26 × 10-3; metabolic-p = 3.48 × 10-3). In EUR, pairwise comparisons showed multiple πc differences related to the endocrine category (fold-enrichment = 5.83, p = 4.76 × 10-6), where these traits showed a low proportion of susceptibility SNPs (EUR-endocrine median πc = 0.01%) with the strongest difference with respect to psychiatric phenotypes (EUR-psychiatric median πc = 0.50%; p = 1.19 × 10-4). Simulating sample sizes of 1,000,000 and 5,000,000 individuals, we also showed that ancestry-specific polygenicity patterns translate into differences across health domains in the genetic variance explained by susceptibility SNPs projected to be genome-wide significant (e.g., EAS hematological-neoplasm p = 2.18 × 10-4; EUR endocrine-gastrointestinal p = 6.80 × 10-4). These findings highlight that traits related to the same health domains may present ancestry-specific variability in their polygenicity.

Estimation of indirect genetic effects and heritability under assortative mating

Both direct genetic effects (effects of alleles in an individual on that individual) and indirect genetic effects - effects of alleles in an individual (e.g. parents) on another individual (e.g. offspring) - can contribute to phenotypic variation and genotype-phenotype associations. Here, we consider a phenotype affected by direct and parental indirect genetic effects under assortative mating at equilibrium. We generalize classical theory to derive a decomposition of the equilibrium phenotypic variance in terms of direct and indirect genetic effect components. We extend this theory to show that popular methods for estimating indirect genetic effects or 'genetic nurture' through analysis of parental and offspring polygenic predictors (called polygenic indices or scores - PGIs or PGSs) are substantially biased by assortative mating. We propose an improved method for estimating indirect genetic effects while accounting for assortative mating that can also correct heritability estimates for bias due to assortative mating. We validate our method in simulations and apply it to PGIs for height and educational attainment (EA), estimating that the equilibrium heritability of height is 0.699 (S.E. = 0.075 ) and finding no evidence for indirect genetic effects on height. We estimate a very high correlation between parents' underlying genetic components for EA, 0.755 (S.E. = 0.035), which is inconsistent with twin based estimates of the heritability of EA, possibly due to confounding in the EA PGI and/or in twin studies. We implement our method in the software package snipar, enabling researchers to apply the method to data including observed and/or imputed parental genotypes. We provide a theoretical framework for understanding the results of PGI analyses and a practical methodology for estimating heritability and indirect genetic effects while accounting for assortative mating.

Characterizing the polygenic architecture of complex traits in populations of East Asian and European descent

To investigate the polygenicity of complex traits in populations of East Asian (EAS) and European (EUR) descents, we leveraged genome-wide data from Biobank Japan, UK Biobank, and FinnGen cohorts. Specifically, we analyzed up to 215 outcomes related to 18 health domains, assessing their polygenic architecture via descriptive statistics, such as the proportion of susceptibility SNPs per trait (π _c ). While we did not observe EAS-EUR differences in the overall distribution of polygenicity parameters across the phenotypes investigated, there were ancestry-specific patterns in the polygenicity differences between health domains. In EAS, pairwise comparisons across health domains showed enrichment for π _c differences related to hematological and metabolic traits (hematological fold-enrichment=4.45, p=2.15×10 ^-7 ; metabolic fold-enrichment=4.05, p=4.01×10 ^-6 ). For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains (EAS-hematological median π _c =0.15%, EAS-metabolic median π _c =0.18%) with the strongest π _c difference with respect to respiratory traits (EAS-respiratory median π _c =0.50%; Hematological-p=2.26×10 ^-3 ; Metabolic-p=3.48×10 ^-3 ). In EUR, pairwise comparisons showed multiple π _c differences related to the endocrine category (fold-enrichment=5.83, p=4.76×10 ^-6 ), where these traits showed a low proportion of susceptibility SNPs (EUR-endocrine median π _c =0.01%) with the strongest difference with respect to psychiatric phenotypes (EUR-psychiatric median π _c =0.50%; p=1.19×10 ^-4 ). Simulating sample sizes of 1,000,000 and 5,000,000 individuals, we also showed that ancestry-specific polygenicity patterns translate into differences across health domains in the genetic variance explained by susceptibility SNPs projected to be genome-wide significant (e.g., EAS hematological-neoplasm p=2.18×10 ^-4 ; EUR endocrine-gastrointestinal p=6.80×10 ^-4 ). These findings highlight that traits related to the same health domains may present ancestry-specific variability in their polygenicity.

From genetic variation to precision medicine

Genetics has been an important tool for discovering new aspects of biology across life. In humans, there is growing momentum behind the application of this knowledge to drive innovation in clinical care, most notably through developments in precision medicine. Nowhere has the impact of genetics on clinical practice been more striking than in the field of rare disorders. For most of these conditions, individual disease susceptibility is influenced by DNA sequence variation in a single or a small number of genes. In contrast, most common disorders are multifactorial and are caused by a complex interplay of multiple genetic, environmental and stochastic factors. The longstanding division of human disease genetics into rare and common components has obscured the continuum of human traits and echoes aspects of the century-old debate between the Mendelian and biometric views of human genetics. In this article, we discuss the differences in data and concepts between rare and common disease genetics. Opportunities to unify these two areas are noted and the importance of adopting a holistic perspective that integrates diverse genetic and environmental factors is discussed.

Mosaic patterns of selection in genomic regions associated with diverse human traits

Natural selection shapes the genetic architecture of many human traits. However, the prevalence of different modes of selection on genomic regions associated with variation in traits remains poorly understood. To address this, we developed an efficient computational framework to calculate positive and negative enrichment of different evolutionary measures among regions associated with complex traits. We applied the framework to summary statistics from >900 genome-wide association studies (GWASs) and 11 evolutionary measures of sequence constraint, population differentiation, and allele age while accounting for linkage disequilibrium, allele frequency, and other potential confounders. We demonstrate that this framework yields consistent results across GWASs with variable sample sizes, numbers of trait-associated SNPs, and analytical approaches. The resulting evolutionary atlas maps diverse signatures of selection on genomic regions associated with complex human traits on an unprecedented scale. We detected positive enrichment for sequence conservation among trait-associated regions for the majority of traits (>77% of 290 high power GWASs), which included reproductive traits. Many traits also exhibited substantial positive enrichment for population differentiation, especially among hair, skin, and pigmentation traits. In contrast, we detected widespread negative enrichment for signatures of balancing selection (51% of GWASs) and absence of enrichment for evolutionary signals in regions associated with late-onset Alzheimer's disease. These results support a pervasive role for negative selection on regions of the human genome that contribute to variation in complex traits, but also demonstrate that diverse modes of evolution are likely to have shaped trait-associated loci. This atlas of evolutionary signatures across the diversity of available GWASs will enable exploration of the relationship between the genetic architecture and evolutionary processes in the human genome.

Genomic Assessment of Cancer Susceptibility in the Threatened Catalina Island Fox (Urocyon littoralis catalinae)

Small effective population sizes raise the probability of extinction by increasing the frequency of potentially deleterious alleles and reducing fitness. However, the extent to which cancers play a role in the fitness reduction of genetically depauperate wildlife populations is unknown. Santa Catalina island foxes (Urocyon littoralis catalinae) sampled in 2007-2008 have a high prevalence of ceruminous gland tumors, which was not detected in the population prior to a recent bottleneck caused by a canine distemper epidemic. The disease appears to be associated with inflammation from chronic ear mite (Otodectes) infections and secondary elevated levels of Staphyloccus pseudointermedius bacterial infections. However, no other environmental factors to date have been found to be associated with elevated cancer risk in this population. Here, we used whole genome sequencing of the case and control individuals from two islands to identify candidate loci associated with cancer based on genetic divergence, nucleotide diversity, allele frequency spectrum, and runs of homozygosity. We identified several candidate loci based on genomic signatures and putative gene functions, suggesting that cancer susceptibility in this population may be polygenic. Due to the efforts of a recovery program and weak fitness effects of late-onset disease, the population size has increased, which may allow selection to be more effective in removing these presumably slightly deleterious alleles. Long-term monitoring of the disease alleles, as well as overall genetic diversity, will provide crucial information for the long-term persistence of this threatened population.

Quo Vadis Psychiatry? Why It Is Time to Endorse Evolutionary Theory

In recent decades, psychiatry and the neurosciences have made little progress in terms of preventing, diagnosing, classifying, or treating mental disorders. Here we argue that the dilemma of psychiatry and the neurosciences is, in part, based on fundamental misconceptions about the human mind, including misdirected nature-nurture debates, the lack of definitional concepts of "normalcy," distinguishing defense from defect, disregarding life history theory, evolutionarily uninformed genetic and epigenetic research, the "disconnection" of the brain from the rest of the body, and lack of attention to actual behavior in real-world interactions. All these conceptual difficulties could potentially benefit from an approach that uses evolutionary theory to improve the understanding of causal mechanisms, gene-environment interaction, individual differences in behavioral ecology, interaction between the gut (and other organs) and the brain, as well as cross-cultural and across-species comparison. To foster this development would require reform of the curricula of medical schools.

Linking the genomic signatures of human beat synchronization and learned song in birds

The development of rhythmicity is foundational to communicative and social behaviours in humans and many other species, and mechanisms of synchrony could be conserved across species. The goal of the current paper is to explore evolutionary hypotheses linking vocal learning and beat synchronization through genomic approaches, testing the prediction that genetic underpinnings of birdsong also contribute to the aetiology of human interactions with musical beat structure. We combined state-of-the-art-genomic datasets that account for underlying polygenicity of these traits: birdsong genome-wide transcriptomics linked to singing in zebra finches, and a human genome-wide association study of beat synchronization. Results of competitive gene set analysis revealed that the genetic architecture of human beat synchronization is significantly enriched for birdsong genes expressed in songbird Area X (a key nucleus for vocal learning, and homologous to human basal ganglia). These findings complement ethological and neural evidence of the relationship between vocal learning and beat synchronization, supporting a framework of some degree of common genomic substrates underlying rhythm-related behaviours in two clades, humans and songbirds (the largest evolutionary radiation of vocal learners). Future cross-species approaches investigating the genetic underpinnings of beat synchronization in a broad evolutionary context are discussed. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.

Consistent Assignment of Risk and Benign Allele at rs2303153 in the CF Modifier Gene SCNN1B in Three Independent F508del-CFTR Homozygous Patient Populations

CFTR encodes for a chloride and bicarbonate channel expressed at the apical membrane of polarized epithelial cells. Transepithelial sodium transport mediated by the amiloride-sensitive sodium channel ENaC is thought to contribute to the manifestation of CF disease. Thus, ENaC is a therapeutic target in CF and a valid cystic fibrosis modifier gene. We have characterized SCNN1B as a genetic modifier in the three independent patient cohorts of F508del-CFTR homozygotes. We could identify a regulatory element at SCNN1B to the genomic segment rs168748-rs2303153-rs4968000 by fine-mapping (Pbest = 0.0177), consistently observing the risk allele rs2303153-C and the contrasting benign allele rs2303153-G in all three patient cohorts. Furthermore, our results show that expression levels of SCNN1B are associated with rs2303153 genotype in intestinal epithelia (p = 0.003). Our data confirm that the well-established biological role of SCNN1B can be recognized by an association study on informative endophenotypes in the rare disease cystic fibrosis and calls attention to reproducible results in association studies obtained from small, albeit carefully characterized patient populations.

Meta-analysis: Congruence of genomic and phenotypic differentiation across diverse natural study systems

Linking genotype to phenotype is a primary goal for understanding the genomic underpinnings of evolution. However, little work has explored whether patterns of linked genomic and phenotypic differentiation are congruent across natural study systems and traits. Here, we investigate such patterns with a meta-analysis of studies examining population-level differentiation at subsets of loci and traits putatively responding to divergent selection. We show that across the 31 studies (88 natural population-level comparisons) we examined, there was a moderate (R 2 = 0.39) relationship between genomic differentiation (F ST ) and phenotypic differentiation (P ST ) for loci and traits putatively under selection. This quantitative relationship between P ST and F ST for loci under selection in diverse taxa provides broad context and cross-system predictions for genomic and phenotypic adaptation by natural selection in natural populations. This context may eventually allow for more precise ideas of what constitutes "strong" differentiation, predictions about the effect size of loci, comparisons of taxa evolving in nonparallel ways, and more. On the other hand, links between P ST and F ST within studies were very weak, suggesting that much work remains in linking genomic differentiation to phenotypic differentiation at specific phenotypes. We suggest that linking genotypes to specific phenotypes can be improved by correlating genomic and phenotypic differentiation across a spectrum of diverging populations within a taxon and including wide coverage of both genomes and phenomes.

Genomics at cellular resolution: insights into cognitive disorders and their evolution

High-throughput genomic sequencing approaches have held the promise of understanding and ultimately leading to treatments for cognitive disorders such as autism spectrum disorders, schizophrenia and Alzheimer's disease. Although significant progress has been made into identifying genetic variants associated with these diseases, these studies have also uncovered that these disorders are mostly genetically complex and thus challenging to model in non-human systems. Improvements in such models might benefit from understanding the evolution of the human genome and how such modifications have affected brain development and function. The intersection of genome-wide variant information with cell-type-specific expression and epigenetic information will further assist in resolving the contribution of particular cell types in evolution or disease. For example, the role of non-neuronal cells in brain evolution and cognitive disorders has gone mostly underappreciated until the recent availability of single-cell transcriptomic approaches. In this review, we discuss recent studies that carry out cell-type-specific assessments of gene expression in brain tissue across primates and between healthy and disease populations. The emerging results from these studies are beginning to elucidate how specific cell types in the evolved human brain are contributing to cognitive disorders.

Genomics of Endometriosis: From Genome Wide Association Studies to Exome Sequencing

This review aims at better understanding the genetics of endometriosis. Endometriosis is a frequent feminine disease, affecting up to 10% of women, and characterized by pain and infertility. In the most accepted hypothesis, endometriosis is caused by the implantation of uterine tissue at ectopic abdominal places, originating from retrograde menses. Despite the obvious genetic complexity of the disease, analysis of sibs has allowed heritability estimation of endometriosis at ~50%. From 2010, large Genome Wide Association Studies (GWAS), aimed at identifying the genes and loci underlying this genetic determinism. Some of these loci were confirmed in other populations and replication studies, some new loci were also found through meta-analyses using pooled samples. For two loci on chromosomes 1 (near CCD42) and chromosome 9 (near CDKN2A), functional explanations of the SNP (Single Nucleotide Polymorphism) effects have been more thoroughly studied. While a handful of chromosome regions and genes have clearly been identified and statistically demonstrated as at-risk for the disease, only a small part of the heritability is explained (missing heritability). Some attempts of exome sequencing started to identify additional genes from families or populations, but are still scarce. The solution may reside inside a combined effort: increasing the size of the GWAS designs, better categorize the clinical forms of the disease before analyzing genome-wide polymorphisms, and generalizing exome sequencing ventures. We try here to provide a vision of what we have and what we should obtain to completely elucidate the genetics of this complex disease.

Functional variant rs17525453 within RAB35 gene promoter is possibly associated with increased risk of Parkinson's disease in Taiwanese population

Our previous study suggests that upregulated RAB35 is implicated in etiology of Parkinson's disease (PD). We hypothesized that upregulated RAB35 results from single nucleotide polymorphisms (SNPs) in RAB35 gene promoter. We identified SNPs within RAB35 gene promoter by analyzing DNA samples of discovery cohort and validation cohort. SNP rs17525453 within RAB35 gene promoter (T>C at position of -66) was significantly associated with idiopathic PD patients. Compared to normal controls, sporadic PD patients had higher C allele frequency. CC and CT genotype significantly increased risk of PD compared with TT genotype. SNP rs17525453 within RAB35 gene promoter leads to formation of transcription factor TFII-I binding site. Results of EMSA and supershift assay indicated that TFII-I binds to rs17525453 sequence of RAB35 gene promoter. Luciferase reporter assays showed that rs17525453 variant of RAB35 gene promoter possesses an augmented transcriptional activity. Our results suggest that functional variant rs17525453 within RAB35 gene promoter is likely to enhance transcriptional activity and upregulate RAB35 protein, which could lead to increased risk of PD in Taiwanese population.

Polygenic Risk Scores

As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings, including prediction of disease risk, have become apparent. Many complex diseases have numerous associated loci with detectable effects implicating risk for or protection from disease. A common contemporary approach to using this information for disease prediction is through the application of genetic risk scores. These scores estimate an individual's liability for a specific outcome by aggregating the effects of associated loci into a single measure as described in the previous version of this article. Although genetic risk scores have traditionally included variants that meet criteria for genome-wide significance, an extension known as the polygenic risk score has been developed to include the effects of more variants across the entire genome. Here, we describe common methods and software packages for calculating and interpreting polygenic risk scores. In this revised version of the article, we detail information that is needed to perform a polygenic risk score analysis, considerations for planning the analysis and interpreting results, as well as discussion of the limitations based on the choices made. We also provide simulated sample data and a walkthrough for four different polygenic risk score software. © 2021 Wiley Periodicals LLC.

Negative selection on complex traits limits phenotype prediction accuracy between populations

Phenotype prediction is a key goal for medical genetics. Unfortunately, most genome-wide association studies are done in European populations, which reduces the accuracy of predictions via polygenic scores in non-European populations. Here, we use population genetic models to show that human demographic history and negative selection on complex traits can result in population-specific genetic architectures. For traits where alleles with the largest effect on the trait are under the strongest negative selection, approximately half of the heritability can be accounted for by variants in Europe that are absent from Africa, leading to poor performance in phenotype prediction across these populations. Further, under such a model, individuals in the tails of the genetic risk distribution may not be identified via polygenic scores generated in another population. We empirically test these predictions by building a model to stratify heritability between European-specific and shared variants and applied it to 37 traits and diseases in the UK Biobank. Across these phenotypes, ∼30% of the heritability comes from European-specific variants. We conclude that genetic association studies need to include more diverse populations to enable the utility of phenotype prediction in all populations.

Hierarchical Modelling of Haplotype Effects on a Phylogeny

We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations, and leveraging similarities can improve the estimation of effects. We build on extensive literature and develop an autoregressive model of order one that models haplotype effects by leveraging phylogenetic relationships described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network, and we refer to the model as the haplotype network model. The model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. Our key contribution is that we obtain a sparse model, and by using hierarchical autoregression, the flow of information between similar haplotypes is estimated from the data. A simulation study shows that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially with few observations for a specific haplotype. We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a study of mitochondrial haplotype effects on milk yield in cattle. We provide R code to fit the model with the INLA package.

The KMT2A gene: mRNA differential expression in the ovary and a novel 13-nt nucleotide sequence variant associated with litter size in cashmere goats

A genome-wide association study had shown that lysine methyltransferase 2A (KMT2A), which encodes the histone 3 lysine 4 methyltransferase and reportedly can regulate gametogenesis, steroidogenesis, and development as well as other biological processes, is a potential candidate gene influencing litter size in the dairy goat, suggesting its key function in animal reproduction. Here, we aimed to explore the genetic effects of the KMT2A gene on litter size in females of the Chinese indigenous cashmere goat, using a large sample size (n > 1,000), based on their levels of RNA transcription and DNA variation. First, mRNA expression levels of this gene in ovarian tissues between the low-prolific group (first-born litter size = 1) and high-prolific group (first-born litter size ≥2) were significantly different, revealing the potential functioning of KMT2A in goat prolific. Moreover, a novel 13-nt nucleotide sequence variant was identified in Shaanbei white cashmere goats (n = 1,616). In accordance with the independent chi-square (χ²) analysis, the distribution of genotypes (P = 2.57 × 10^-9) and allelotypes (P = 3.00 × 10^-7) between the low- and high-prolific groups differed significantly, indicating the 13-nt mutation was associated with litter size. Further analysis showed that the insertion/insertion (II) genotype was significantly different with insertion/deletion (ID) (P = 1.76 × 10^-9) and deletion/deletion (DD) (P = 7.00 × 10^-6), with goats having the DD genotype producing an average litter size larger than the other genotypes. Taken together, these findings suggest KMT2A can serve as a candidate gene for breeding goats, which may have implications for improving the future development of the goat industry.

Functional genomics, genetic risk profiling and cell phenotypes in neurodegenerative disease

Human genetics provides unbiased insights into the causes of human disease, which can be used to create a foundation for effective ways to more accurately diagnose patients, stratify patients for more successful clinical trials, discover and develop new therapies, and ultimately help patients choose the safest and most promising therapeutic option based on their risk profile. But the process for translating basic observations from human genetics studies into pathogenic disease mechanisms and treatments is laborious and complex, and this challenge has particularly slowed the development of interventions for neurodegenerative disease. In this review, we discuss the many steps in the process, the important considerations at each stage, and some of the latest tools and technologies that are available to help investigators translate insights from human genetics into diagnostic and therapeutic strategies that will lead to the sort of advances in clinical care that make a difference for patients.

What Has a Century of Quantitative Genetics Taught Us About Nature's Genetic Tool Kit?

The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms.

Evolutionary insights into plant breeding

Crop domestication is a fascinating area of study, as shown by a multitude of recent reviews. Coupled with the increasing availability of genomic and phenomic resources in numerous crop species, insights from evolutionary biology will enable a deeper understanding of the genetic architecture and short-term evolution of complex traits, which can be used to inform selection strategies. Future advances in crop improvement will rely on the integration of population genetics with plant breeding methodology, and the development of community resources to support research in a variety of crop life histories and reproductive strategies. We highlight recent advances related to the role of selective sweeps and demographic history in shaping genetic architecture, how these breakthroughs can inform selection strategies, and the application of precision gene editing to leverage these connections.