Evaluating empirical bounds on complex disease genetic architecture

Association between asthma and type 2 diabetes in a Swedish adult population: a register-based cross-sectional study

OBJECTIVE

Asthma and type 2 diabetes are two important causes of morbidity globally. We examined both the association of type 2 diabetes with asthma in Swedish adults and the familial co-aggregation of the diseases.

METHODS

We conducted a cross-sectional study of all adults aged 25-85 in Sweden between 2009 and 2013. Asthma and type 2 diabetes status were ascertained from the health registers. Models were adjusted for sex, age, education level, income and country of birth and in a subset, for body mass index (BMI). We further conducted a familial coaggregation analysis to determine if shared familial factors could explain any observed findings.

RESULTS

The study included 5 299 245 participants, 25 292 (0.5%) had both asthma and type 2 diabetes. In the total population, the OR for the association between type 2 diabetes and asthma was 1.47 (95% CI 1.45 to 1.49); in the population of men (1.30 (95% CI 1.27 to 1.32)) and women (1.63 (95% CI 1.60 to 1.66)). The ORs were slightly higher among men (1.51 (95% CI 1.45 to 1.56)) and women (2.04 (95% CI 1.96 to 2.11)) for whom BMI measurements were available but attenuated with adjustment for BMI (1.45 (95% CI 1.40 to 1.51)) and (1.76 (95% CI 1.68 to 1.84)). Diabetes was more likely if a full sibling had asthma than if the sibling did not (1.13 (95% CI 1.10 to 1.15)).

CONCLUSIONS

We found an association between asthma and type 2 diabetes that was sustained after adjusting for BMI, indicating that BMI alone does not explain this relationship. We also found that the two conditions coaggregate in siblings, indicating that the association is partly due to shared familial genetic and environmental risk factors.

How (Ultra-)Rare Gene Variants Improve Our Understanding of More Common Autoimmune and Inflammatory Diseases

The aim of this study was to explore the impact of rare and ultra-rare genetic variants on the understanding and treatment of autoimmune and autoinflammatory diseases with a focus on systemic lupus erythematosus (SLE) and Behçet syndrome. This review summarizes current research on the monogenic causes of SLE and Behçet syndrome, highlighting the various pathways that can be responsible for these unique phenotypes. In monogenic SLE, the identification of complement and DNASE1L3 deficiencies has elucidated mechanisms of apoptotic body accumulation and extracellular nucleic acid sensing. Type I interferonopathies underline the specific role of DNA/RNA sensing and the interferon overexpression in the development of systemic autoimmunity. Other significant genetic defects include Toll-like receptor hypersignaling and JAK/STATopathies, which contribute to the breakdown of immune tolerance. To date, genetic defects directly affecting B and T cell biology only account for a minority of identified causes of monogenic lupus, highlighting the importance of a tight regulation of mechanistic target of rapamycin and RAS (Rat sarcoma GTPase)/MAPK (mitogen-activated protein kinase) signaling in lupus. In Behçet syndrome, rare variants in TNFAIP3, RELA, and NFKB1 genes have been identified, underscoring the importance of NF-κB overactivation. Additional monogenic diseases such as ELF4, WDR1 mutations and trisomy 8 further illustrate the genetic complexity of this condition. Observations from genetic studies in SLE and Behçet syndrome highlight the complexity of systemic inflammatory diseases in which distinct molecular defects caused by single-gene mutations can promote lupus or Behçet syndromes, often unrecognizable from their genetically complex "classical" forms. Insights gained from studying rare genetic variants enhance our understanding of immune function in health and disease, paving the way for targeted therapies and personalized medicine.

Germline Genetic Associations for Hepatobiliary Cancers

Hepatobiliary cancers (HBCs) include hepatocellular carcinoma, cholangiocarcinoma, and gallbladder carcinoma, which originate from the liver, bile ducts, and gallbladder, respectively. They are responsible for a substantial burden of cancer-related deaths worldwide. Despite knowledge of risk factors and advancements in therapeutics and surgical interventions, the prognosis for most patients with HBC remains bleak. There is evidence from familial aggregation and case-control studies to suggest a familial risk component in HBC susceptibility. Recent progress in genomics research has led to the identification of germline variants including single nucleotide polymorphisms (SNPs) and pathogenic or likely pathogenic (P/LP) variants in cancer-associated genes associated with HBC risk. These findings emerged from genome-wide association studies and next-generation sequencing techniques such as whole-exome sequencing. Patients with other cancer types, including breast, colon, ovarian, prostate, and pancreatic cancer, are recommended by guidelines to undergo germline genetic testing, but similar recommendations are lagging in HBC. This prompts the question of whether multi-gene panel testing should be integrated into clinical guidelines for HBC management. Here, we review the hereditary genetics of HBC, explore studies investigating SNPs and P/LP variants in HBC patients, discuss the clinical implications and potential for personalized treatments and impact on patient's family members, and conclude that additional studies are needed to examine how genetic testing can be applied clinically.

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.

Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs

Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an "integrative prior" for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.

Identification of prolactin receptor variants with diverse effects on receptor signalling

The prolactin receptor (PRLR) signals predominantly through the JAK2-STAT5 pathway regulating multiple physiological functions relating to fertility, lactation, and metabolism. However, the molecular pathology and role of PRLR mutations and signalling are incompletely defined, with progress hampered by a lack of reported disease-associated PRLR variants. To date, two common germline PRLR variants are reported to demonstrate constitutive activity, with one, Ile146Leu, overrepresented in benign breast disease, while a rare activating variant, Asn492Ile, is reported to be associated with an increased incidence of prolactinoma. In contrast, an inactivating germline heterozygous PRLR variant (His188Arg) was reported in a kindred with hyperprolactinaemia, while an inactivating compound heterozygous PRLR variant (Pro269Leu/Arg171Stop) was identified in an individual with hyperprolactinaemia and agalactia. We hypothesised that additional rare germline PRLR variants, identified from large-scale sequencing projects (ExAC and GnomAD), may be associated with altered in vitro PRLR signalling activity. We therefore evaluated >300 previously uncharacterised non-synonymous, germline PRLR variants and selected 10 variants for in vitro analysis based on protein prediction algorithms, proximity to known functional domains and structural modelling. Five variants, including extracellular and intracellular domain variants, were associated with altered responses when compared to the wild-type receptor. These altered responses included loss- and gain-of-function activities related to STAT5 signalling, Akt and FOXO1 activity, as well as cell viability and apoptosis. These studies provide further insight into PRLR structure-function and indicate that rare germline PRLR variants may have diverse modulating effects on PRLR signalling, although the pathophysiologic relevance of such alterations remains to be defined.

Missense Variants of von Willebrand Factor in the Background of COVID-19 Associated Coagulopathy

COVID-19 associated coagulopathy (CAC), characterized by endothelial dysfunction and hypercoagulability, evokes pulmonary immunothrombosis in advanced COVID-19 cases. Elevated von Willebrand factor (vWF) levels and reduced activities of the ADAMTS13 protease are common in CAC. Here, we aimed to determine whether common genetic variants of these proteins might be associated with COVID-19 severity and hemostatic parameters. A set of single nucleotide polymorphisms (SNPs) in the vWF (rs216311, rs216321, rs1063856, rs1800378, rs1800383) and ADAMTS13 genes (rs2301612, rs28729234, rs34024143) were genotyped in 72 COVID-19 patients. Cross-sectional cohort analysis revealed no association of any polymorphism with disease severity. On the other hand, analysis of variance (ANOVA) uncovered associations with the following clinical parameters: (1) the rs216311 T allele with enhanced INR (international normalized ratio); (2) the rs1800383 C allele with elevated fibrinogen levels; and (3) the rs1063856 C allele with increased red blood cell count, hemoglobin, and creatinine levels. No association could be observed between the phenotypic data and the polymorphisms in the ADAMTS13 gene. Importantly, in silico protein conformational analysis predicted that these missense variants would display global conformational alterations, which might affect the stability and plasma levels of vWF. Our results imply that missense vWF variants might modulate the thrombotic risk in COVID-19.

Weighted kernels improve multi-environment genomic prediction

Crucial to variety improvement programs is the reliable and accurate prediction of genotype's performance across environments. However, due to the impactful presence of genotype by environment (G×E) interaction that dictates how changes in expression and function of genes influence target traits in different environments, prediction performance of genomic selection (GS) using single-environment models often falls short. Furthermore, despite the successes of genome-wide association studies (GWAS), the genetic insights derived from genome-to-phenome mapping have not yet been incorporated in predictive analytics, making GS models that use Gaussian kernel primarily an estimator of genomic similarity, instead of the underlying genetics characteristics of the populations. Here, we developed a GS framework that, in addition to capturing the overall genomic relationship, can capitalize on the signal of genetic associations of the phenotypic variation as well as the genetic characteristics of the populations. The capacity of predicting the performance of populations across environments was demonstrated by an overall gain in predictability up to 31% for the winter wheat DH population. Compared to Gaussian kernels, we showed that our multi-environment weighted kernels could better leverage the significance of genetic associations and yielded a marked improvement of 4-33% in prediction accuracy for half-sib families. Furthermore, the flexibility incorporated in our Bayesian implementation provides the generalizable capacity required for predicting multiple highly genetic heterogeneous populations across environments, allowing reliable GS for genetic improvement programs that have no access to genetically uniform material.

The genetics of non-monogenic IBD

Inflammatory bowel disease (IBD), with Crohn's disease and ulcerative colitis as main subtypes, is a prototypical multifactorial disease with both genetic and environmental factors involved. Genetically, IBD covers a wide spectrum from monogenic to polygenic forms. In polygenic disease, many genetic variants each contribute a small amount to disease risk. With the advent of genome-wide association studies (GWAS), it became possible to find these variants and corresponding genes, leading so far to the discovery of ca 240 loci associated with IBD. Together, these however explain only 20-25% of the heritability of IBD, leaving a large portion unaccounted for. This missing heritability might be hidden in common variants with even lower effect than the ones currently found through GWAS, but also in rare variants which can be found through large-scale sequencing studies or potentially in multiplex families. In this review, we will give an overview of the current knowledge about the genetics of non-monogenic IBD and how it differs from the monogenic form(s), and future perspectives. The history of IBD genetic studies from twin studies over linkage studies to GWAS, and finally large-scale sequencing studies and the revisiting of multiplex families will be discussed.

The genetics of autoimmune Addison disease: past, present and future

Autoimmune Addison disease is an endocrinopathy that is fatal if not diagnosed and treated in a timely manner. Its rarity has hampered unbiased studies of the predisposing genetic factors. A 2021 genome-wide association study, explaining up to 40% of the genetic susceptibility, has revealed new disease loci and reproduced some of the previously reported associations, while failing to reproduce others. Credible risk loci from both candidate gene and genome-wide studies indicate that, like one of its most common comorbidities, type 1 diabetes mellitus, Addison disease is primarily caused by aberrant T cell behaviour. Here, we review the current understanding of the genetics of autoimmune Addison disease and its position in the wider field of autoimmune disorders. The mechanisms that could underlie the effects on the adrenal cortex are also discussed.

Benchmarking of univariate pleiotropy detection methods applied to epilepsy

Pleiotropy is a widespread phenomenon that may increase insight into the etiology of biological and disease traits. Since genome-wide association studies frequently provide information on a single trait only, only univariate pleiotropy detection methods are applicable, with yet unknown comparative performance. Here, we compared five such methods with respect to their ability to detect pleiotropy, including meta-analysis, ASSET, cFDR, CPBayes, and PLACO, by performing extended computer simulations that varied the underlying etiological model for pleiotropy for a pair of traits, including the number of causal variants, degree of traits' overlap, effect sizes as well as trait prevalence, and varying sample sizes. Our results indicate that ASSET provides the best trade-off between power and protection against false positives. We then applied ASSET to a previously published ILAE consortium dataset on complex epilepsies, comprising genetic generalized epilepsy and focal epilepsy cases and corresponding controls. We identified a novel candidate locus at 17q21.32 and confirmed locus 2q24.3, previously identified to act pleiotropically on both epilepsy subtypes by a mega-analysis. Functional annotation, tissue-specific expression and regulatory function analysis as well as Bayesian co-localization analysis corroborated this result, rendering 17q21.32 a worthwhile candidate for follow-up studies on pleiotropy in epilepsies. This article is protected by copyright. All rights reserved.

Canalization of the Polygenic Risk for Common Diseases and Traits in the UK Biobank Cohort

Since organisms develop and thrive in the face of constant perturbations due to environmental and genetic variation, species may evolve resilient genetic architectures. We sought evidence for this process, known as canalization, through a comparison of the prevalence of phenotypes as a function of the polygenic score (PGS) across environments in the UK Biobank cohort study. Contrasting seven diseases and three categorical phenotypes with respect to 151 exposures in 408,925 people, the deviation between the prevalence-risk curves was observed to increase monotonically with the PGS percentile in one-fifth of the comparisons, suggesting extensive PGS-by-Environment (PGS×E) interaction. After adjustment for the dependency of allelic effect sizes on increased prevalence in the perturbing environment, cases where polygenic influences are greater or lesser than expected are seen to be particularly pervasive for educational attainment, obesity, and metabolic condition type-2 diabetes. Inflammatory bowel disease analysis shows fewer interactions but confirms that smoking and some aspects of diet influence risk. Notably, body mass index has more evidence for decanalization (increased genetic influence at the extremes of polygenic risk), whereas the waist-to-hip ratio shows canalization, reflecting different evolutionary pressures on the architectures of these weight-related traits. An additional 10 % of comparisons showed evidence for an additive shift of prevalence independent of PGS between exposures. These results provide the first widespread evidence for canalization protecting against disease in humans and have implications for personalized medicine as well as understanding the evolution of complex traits. The findings can be explored through an R shiny app at https://canalization-gibsonlab.shinyapps.io/rshiny/.

Human loss-of-function variants in the serotonin 2C receptor associated with obesity and maladaptive behavior

Serotonin reuptake inhibitors and receptor agonists are used to treat obesity, anxiety and depression. Here we studied the role of the serotonin 2C receptor (5-HT_2CR) in weight regulation and behavior. Using exome sequencing of 2,548 people with severe obesity and 1,117 control individuals without obesity, we identified 13 rare variants in the gene encoding 5-HT_2CR (HTR2C) in 19 unrelated people (3 males and 16 females). Eleven variants caused a loss of function in HEK293 cells. All people who carried variants had hyperphagia and some degree of maladaptive behavior. Knock-in male mice harboring a human loss-of-function HTR2C variant developed obesity and reduced social exploratory behavior; female mice heterozygous for the same variant showed similar deficits with reduced severity. Using the 5-HT_2CR agonist lorcaserin, we found that depolarization of appetite-suppressing proopiomelanocortin neurons was impaired in knock-in mice. In conclusion, we demonstrate that 5-HT_2CR is involved in the regulation of human appetite, weight and behavior. Our findings suggest that melanocortin receptor agonists might be effective in treating severe obesity in individuals carrying HTR2C variants. We suggest that HTR2C should be included in diagnostic gene panels for severe childhood-onset obesity.

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag" variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation.

Maintenance of Complex Trait Variation: Classic Theory and Modern Data

Numerous studies have found evidence that GWAS loci experience negative selection, which increases in intensity with the effect size of identified variants. However, there is also accumulating evidence that this selection is not entirely mediated by the focal trait and contains a substantial pleiotropic component. Understanding how selective constraint shapes phenotypic variation requires advancing models capable of balancing these and other components of selection, as well as empirical analyses capable of inferring this balance and how it is generated by the underlying biology. We first review the classic theory connecting phenotypic selection to selection at individual loci as well as approaches and findings from recent analyses of negative selection in GWAS data. We then discuss geometric theories of pleiotropic selection with the potential to guide future modeling efforts. Recent findings revealing the nature of pleiotropic genetic variation provide clues to which genetic relationships are important and should be incorporated into analyses of selection, while findings that effect sizes vary between populations indicate that GWAS measurements could be misleading if effect sizes have also changed throughout human history.

Clinical relevance of targeted exome sequencing in patients with rare syndromic short stature

BACKGROUND

Large-scale genomic analyses have provided insight into the genetic complexity of short stature (SS); however, only a portion of genetic causes have been identified. In this study, we identified disease-causing mutations in a cohort of Korean patients with suspected syndromic SS by targeted exome sequencing (TES).

METHODS

Thirty-four patients in South Korea with suspected syndromic disorders based on abnormal growth and dysmorphic facial features, developmental delay, or accompanying anomalies were enrolled in 2018-2020 and evaluated by TES.

RESULTS

For 17 of 34 patients with suspected syndromic SS, a genetic diagnosis was obtained by TES. The mean SDS values for height, IGF-1, and IGFBP-3 for these 17 patients were - 3.27 ± 1.25, - 0.42 ± 1.15, and 0.36 ± 1.31, respectively. Most patients displayed distinct facial features (16/17) and developmental delay or intellectual disability (12/17). In 17 patients, 19 genetic variants were identified, including 13 novel heterozygous variants, associated with 15 different genetic diseases, including many inherited rare skeletal disorders and connective tissue diseases (e.g., cleidocranial dysplasia, Hajdu-Cheney syndrome, Sheldon-Hall, acromesomelic dysplasia Maroteaux type, and microcephalic osteodysplastic primordial dwarfism type II). After re-classification by clinical reassessment, including family member testing and segregation studies, 42.1% of variants were pathogenic, 42.1% were likely pathogenic variant, and 15.7% were variants of uncertain significance. Ultra-rare diseases accounted for 12 out of 15 genetic diseases (80%).

CONCLUSIONS

A high positive result from genetic testing suggests that TES may be an effective diagnostic approach for patients with syndromic SS, with implications for genetic counseling. These results expand the mutation spectrum for rare genetic diseases related to SS in Korea.

Refinement of the clinical variant interpretation framework by statistical evidence and machine learning

BACKGROUND

Although the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines for variant interpretation are used widely in clinical genetics, there is room for improvement of these knowledge-based guidelines.

METHODS

Statistical assessment of average deleteriousness of start-lost, stop-lost, and in-frame insertion and deletion (indel) variants and extraction of deleterious subsets was performed, being informed by proportions of rare variants in the general population of the Genome Aggregation Database (gnomAD). A machine learning-based model scoring the pathogenicity of start-lost variants (the PoStaL model) was constructed by predicting possible translation initiation sites on transcripts by deep learning and training a random forest on known pathogenic and likely benign variants.

FINDINGS

The proportion of rare variants was highest in stop-lost variants, followed by in-frame indels and start-lost variants, suggesting that the criteria in the ACMG/AMP guidelines assigning PVS (pathogenic very strong) to start-lost variants and PM (pathogenic moderate) to stop-lost and in-frame indel variants would not be appropriate. Regarding deleterious subsets, stop-lost variants introducing extensions of more than 30 amino acids and in-frame indels computationally predicted to be damaging are enriched for rare and known pathogenic variants. For start-lost variants, we developed the PoStaL model, which outperforms existing tools. We also provide comprehensive lists of the PoStaL scores for start-lost variants and the length of extended amino acids by stop-lost variants.

CONCLUSIONS

Our study could contribute to refinement of the ACMG/AMP guidelines, provides resources for future investigation, and provides an example of how to improve knowledge-based frameworks by data-driven approaches.

FUNDING

The study was supported by grants from the Japan Agency for Medical Research and Development (AMED) and the Japan Society for the Promotion of Science (JSPS).

The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures

The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpretability and biological replication. It also reduces potentially confounding effects of population substructure on outcome prediction. The TM consists of three main components. First, replicable but flexible feature engineering procedures translate genome-scale data into biologically informative features that appropriately contextualize simple variant calls/genotypes within biological and functional contexts. Second, model-free, nonparametric ML-based feature filtering procedures empirically reduce dimensionality and noise of both original genotype calls and engineered features. Third, a powerful ML algorithm for feature selection is used to differentiate risk variant contributions across variant frequency and functional prediction spectra. The TM simultaneously evaluates potential contributions of variants operative under polygenic and heterogeneous models of genetic architecture. Our TM enables integration of biological information (e.g., genomic annotations) within conceptual frameworks akin to geneset-/pathways-based and collapsing methods, but overcomes some of these methods' limitations. The full TM pipeline is executed in R. Our approach and initial findings from its application to a whole-exome schizophrenia case-control data set are presented. These TM procedures extend the findings of the primary investigation and yield novel results.

Evolutionary demographic models reveal the strength of purifying selection on susceptibility alleles to late-onset diseases

Assessing the role played by purifying selection on a susceptibility allele to late-onset disease (SALOD) is crucial to understanding the puzzling allelic spectrum of a disease, because most alleles are recent and rare. This fact is surprising because it suggests that alleles are under purifying selection while those that are involved in post-menopause mortality are often considered neutral in the genetic literature. The aim of this article is to use an evolutionary demography model to assess the magnitude of selection on SALODs while accounting for epidemiological and sociocultural factors. We develop an age-structured population model allowing for the calculation of SALOD selection coefficients (1) for a large and realistic parameter space for disease onset, (2) in a two-sex model in which men can reproduce in old age and (3) for situations in which child survival depends on maternal, paternal and grandmaternal care. The results show that SALODs are under purifying selection for most known age-at-onset distributions of late-onset genetic diseases. Estimates regarding various genes involved in susceptibility to cancer or Huntington's disease demonstrate that negative selection largely overcomes the effects of drift in most human populations. This is also probably true for neurodegenerative or polycystic kidney diseases, although sociocultural factors modulate the effect of selection in these cases. We conclude that neutrality is probably the exception among alleles that have a deleterious effect in old age and that accounting for sociocultural factors is required to understand the full extent of the force of selection shaping senescence in humans.

Recent shifts in the genomic ancestry of Mexican Americans may alter the genetic architecture of biomedical traits

People in the Americas represent a diverse continuum of populations with varying degrees of admixture among African, European, and Amerindigenous ancestries. In the United States, populations with non-European ancestry remain understudied, and thus little is known about the genetic architecture of phenotypic variation in these populations. Using genotype data from the Hispanic Community Health Study/Study of Latinos, we find that Amerindigenous ancestry increased by an average of ~20% spanning 1940s-1990s in Mexican Americans. These patterns result from complex interactions between several population and cultural factors which shaped patterns of genetic variation and influenced the genetic architecture of complex traits in Mexican Americans. We show for height how polygenic risk scores based on summary statistics from a European-based genome-wide association study perform poorly in Mexican Americans. Our findings reveal temporal changes in population structure within Hispanics/Latinos that may influence biomedical traits, demonstrating a need to improve our understanding of admixed populations.

Evolutionary perspectives on polygenic selection, missing heritability, and GWAS

Genome-wide association studies (GWAS) have successfully identified many trait-associated variants, but there is still much we do not know about the genetic basis of complex traits. Here, we review recent theoretical and empirical literature regarding selection on complex traits to argue that "missing heritability" is as much an evolutionary problem as it is a statistical problem. We discuss empirical findings that suggest a role for selection in shaping the effect sizes and allele frequencies of causal variation underlying complex traits, and the limitations of these studies. We then use simulations of selection, realistic genome structure, and complex human demography to illustrate the results of recent theoretical work on polygenic selection, and show that statistical inference of causal loci is sharply affected by evolutionary processes. In particular, when selection acts on causal alleles, it hampers the ability to detect causal loci and constrains the transferability of GWAS results across populations. Last, we discuss the implications of these findings for future association studies, and suggest that future statistical methods to infer causal loci for genetic traits will benefit from explicit modeling of the joint distribution of effect sizes and allele frequencies under plausible evolutionary models.

Extreme Polygenicity of Complex Traits Is Explained by Negative Selection

Complex traits and common diseases are extremely polygenic, their heritability spread across thousands of loci. One possible explanation is that thousands of genes and loci have similarly important biological effects when mutated. However, we hypothesize that for most complex traits, relatively few genes and loci are critical, and negative selection-purging large-effect mutations in these regions-leaves behind common-variant associations in thousands of less critical regions instead. We refer to this phenomenon as flattening. To quantify its effects, we introduce a mathematical definition of polygenicity, the effective number of independently associated SNPs (Me), which describes how evenly the heritability of a trait is spread across the genome. We developed a method, stratified LD fourth moments regression (S-LD4M), to estimate Me, validating that it produces robust estimates in simulations. Analyzing 33 complex traits (average N = 361k), we determined that heritability is spread ∼4× more evenly among common SNPs than among low-frequency SNPs. This difference, together with evolutionary modeling of new mutations, suggests that complex traits would be orders of magnitude less polygenic if not for the influence of negative selection. We also determined that heritability is spread more evenly within functionally important regions in proportion to their heritability enrichment; functionally important regions do not harbor common SNPs with greatly increased causal effect sizes, due to selective constraint. Our results suggest that for most complex traits, the genes and loci with the most critical biological effects often differ from those with the strongest common-variant associations.

Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

Many traits of interest are highly heritable and genetically complex, meaning that much of the variation they exhibit arises from differences at numerous loci in the genome. Complex traits and their evolution have been studied for more than a century, but only in the last decade have genome-wide association studies (GWASs) in humans begun to reveal their genetic basis. Here, we bring these threads of research together to ask how findings from GWASs can further our understanding of the processes that give rise to heritable variation in complex traits and of the genetic basis of complex trait evolution in response to changing selection pressures (i.e., of polygenic adaptation). Conversely, we ask how evolutionary thinking helps us to interpret findings from GWASs and informs related efforts of practical importance.

Corneal Perforation After Corneal Cross-Linking in Keratoconus Associated With Potentially Pathogenic ZNF469 Mutations

PURPOSE

To report a case of bilateral and repetitive corneal perforations after corneal cross-linking (CXL) for keratoconus in a woman harboring potentially pathogenic variants in the ZNF469 gene and to characterize the keratoconus phenotype in this woman and her daughter who shared the same ZNF469 mutations.

METHODS

Clinical characterization of the proband and her daughter followed by sequencing of the genes associated with brittle cornea syndrome, ZNF469 and PRDM5, in both individuals.

RESULTS

An Ashkenazi Jewish woman in her sixth decade presented with diffuse corneal thinning and progressive steepening consistent with keratoconus. After CXL, epithelium-off in the first eye and epithelium-on in the second, she developed spontaneous corneal perforations in each eye. Her daughter in her fourth decade demonstrated a similar pattern of diffuse corneal thinning and progressive corneal steepening but did not undergo CXL and did not develop corneal perforation. Screening of the ZNF469 and PRDM5 genes revealed 3 missense ZNF469 variants (c.2035G>A, c.10244G>C, and c.11119A>G) in cis arrangement on 1 allele of ZNF469 in both proband and her daughter. Although the 3 variants share low (<0.01) global minor allele frequencies, each has significantly higher minor allele frequencies (0.01-0.03) in the Ashkenazi Jewish population, leading to uncertainty regarding a pathogenic role for the identified variants.

CONCLUSIONS

CXL may be associated with the development of corneal perforation in particular at-risk individuals with keratoconus. Identifying clinical and genetic risk factors, including screening of ZNF469 and PRDM5, may be useful in the prevention of significant complications after CXL.

Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future

Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.

Evaluating the Potential of Younger Cases and Older Controls Cohorts to Improve Discovery Power in Genome-Wide Association Studies of Late-Onset Diseases

For more than a decade, genome-wide association studies have been making steady progress in discovering the causal gene variants that contribute to late-onset human diseases. Polygenic late-onset diseases in an aging population display a risk allele frequency decrease at older ages, caused by individuals with higher polygenic risk scores becoming ill proportionately earlier and bringing about a change in the distribution of risk alleles between new cases and the as-yet-unaffected population. This phenomenon is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes, while for late-onset diseases with relatively lower prevalence and heritability, exemplified by cancers, the effect is significantly lower. In this research, computer simulations have demonstrated that genome-wide association studies of late-onset polygenic diseases showing high cumulative incidence together with high initial heritability will benefit from using the youngest possible age-matched cohorts. Moreover, rather than using age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.

Die Rolle seltener Varianten bei häufigen Krankheiten

Häufige Krankheiten, die sog. Volkskrankheiten, sind in der Regel multifaktoriell verursacht, d. h. zu ihrer Entwicklung tragen sowohl genetische Faktoren als auch nicht-genetische Umgebungseinflüsse bei. Die geschätzte Gesamterblichkeit (‑heritabilität) reicht von moderat bis vergleichsweise hoch. Die genetische Architektur ist komplex und kann das gesamte allelische Spektrum, von häufigen Varianten mit niedriger Penetranz bis hin zu seltenen Varianten mit höherer Penetranz, sowie alle möglichen Kombinationen umfassen. Während häufige Varianten seit mehreren Jahren mit großem Erfolg durch genomweite Assoziationsstudien (GWAS) identifiziert werden, war bisher die Identifizierung seltener Varianten, insbesondere aufgrund der großen Zahl beitragender Gene, nur begrenzt erfolgreich. Dies ändert sich derzeit dank der Anwendung von Hochdurchsatz-Sequenziertechnologien („next-generation sequencing“, NGS) und der daraus resultierenden zunehmenden Verfügbarkeit von exom- und genomweiten Sequenzdaten großer Kollektive. In diesem Artikel geben wir einen Überblick über die Bedeutung seltener Varianten bei häufigen Erkrankungen sowie den aktuellen Stand in Bezug auf deren Identifizierung mittels NGS. Wir betrachten insbesondere die folgenden Fragen: Bei welchen häufigen Krankheiten ist ein Beitrag seltener Varianten zu erwarten, wie können diese Varianten identifiziert werden, und welches Potenzial bieten seltene Varianten für das Verständnis biologischer Prozesse bzw. für die Translation in die klinische Praxis?

Age-related late-onset disease heritability patterns and implications for genome-wide association studies

Genome-wide association studies (GWASs) and other computational biology techniques are gradually discovering the causal gene variants that contribute to late-onset human diseases. After more than a decade of genome-wide association study efforts, these can account for only a fraction of the heritability implied by familial studies, the so-called "missing heritability" problem. Computer simulations of polygenic late-onset diseases (LODs) in an aging population have quantified the risk allele frequency decrease at older ages caused by individuals with higher polygenic risk scores (PRSs) becoming ill proportionately earlier. This effect is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer's disease, coronary artery disease, cerebral stroke, and type 2 diabetes. The incidence rate for LODs grows exponentially for decades after early onset ages, guaranteeing that the cohorts used for GWASs overrepresent older individuals with lower PRSs, whose disease cases are disproportionately due to environmental causes such as old age itself. This mechanism explains the decline in clinical predictive power with age and the lower discovery power of familial studies of heritability and GWASs. It also explains the relatively constant-with-age heritability found for LODs of lower prevalence, exemplified by cancers.

The Contribution of Low-Frequency and Rare Coding Variation to Susceptibility to Type 2 Diabetes

PURPOSE OF REVIEW

Soon after the first genome-wide association study (GWAS) for type 2 diabetes (T2D) was published, it was hypothesized that rare and low-frequency variants might explain a substantial proportion of disease risk. Rare coding variants in particular were emphasized given their large expected role in disease. This review summarizes the extent to which recent T2D genetic studies provide evidence for or against this hypothesis.

RECENT FINDINGS

Following a comprehensive study of T2D genetic architecture using three sequencing and genotyping technologies, four even larger studies have provided a yet higher resolution view of the role of rare and low-frequency coding variation in T2D susceptibility. Empirical evidence strongly suggests that common regulatory variants are the dominant contributor to T2D heritability. However, rare coding variants may nonetheless be pervasive across T2D-relevant genes. A strategy using common variants to map disease genes, and rare coding variants to link molecular gene perturbations to cellular and phenotypic effects, may be an effective means to investigate T2D pathogenesis and potential new therapies.

Disease Heritability Enrichment of Regulatory Elements Is Concentrated in Elements with Ancient Sequence Age and Conserved Function across Species

Regulatory elements, e.g., enhancers and promoters, have been widely reported to be enriched for disease and complex trait heritability. We investigated how this enrichment varies with the age of the underlying genome sequence, the conservation of regulatory function across species, and the target gene of the regulatory element. We estimated heritability enrichment by applying stratified LD score regression to summary statistics from 41 independent diseases and complex traits (average N = 320K) and meta-analyzing results across traits. Enrichment of human putative enhancers and promoters was larger in elements with older sequence age, assessed via alignment with other species irrespective of conserved functionality: putative enhancer elements with ancient sequence age (older than the split between marsupial and placental mammals) were 8.8× enriched (versus 2.5× for all putative enhancers; p = 3e-14), and promoter elements with ancient sequence age were 13.5× enriched (versus 5.1× for all promoters; p = 5e-16). Enrichment of human putative enhancers and promoters was also larger in elements whose regulatory function was conserved across species, e.g., human putative enhancers that were enhancers in ≥5 of 9 other mammals were 4.6× enriched (p = 5e-12 versus all putative enhancers). Enrichment of human promoters was larger in promoters of loss-of-function intolerant genes: 12.0× enrichment (p = 8e-15 versus all promoters). The mean value of several measures of negative selection within these genomic annotations mirrored all of these findings. Notably, the annotations with these excess heritability enrichments were jointly significant conditional on each other and on our baseline-LD model, which includes a broad set of coding, conserved, regulatory, and LD-related annotations.

Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data

BACKGROUND

Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D). One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes. However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate.

METHODS

Here, we describe implementation of an analytical pipeline to address this question. First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals. Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks. Third, we use GWAS data to test the T2D association enrichment of the "non-seed" proteins introduced into the network, as a measure of the overall functional connectivity of the network.

RESULTS

We find (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D (p = 0.0014) but not control traits, (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks and (c) enhanced enrichment (p = 3.9 × 10^- 5) when we combine the analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion.

CONCLUSIONS

These analyses reveal a pattern of non-random functional connectivity between candidate causal genes at T2D GWAS loci and highlight the products of genes including YWHAG, SMAD4 or CDK2 as potential contributors to T2D-relevant islet dysfunction. The approach we describe can be applied to other complex genetic and genomic datasets, facilitating integration of diverse data types into disease-associated networks.

Association of prolactin receptor (PRLR) variants with prolactinomas

Prolactinomas are the most frequent type of pituitary tumors, which represent 10-20% of all intracranial neoplasms in humans. Prolactinomas develop in mice lacking the prolactin receptor (PRLR), which is a member of the cytokine receptor superfamily that signals via Janus kinase-2-signal transducer and activator of transcription-5 (JAK2-STAT5) or phosphoinositide 3-kinase-Akt (PI3K-Akt) pathways to mediate changes in transcription, differentiation and proliferation. To elucidate the role of the PRLR gene in human prolactinomas, we determined the PRLR sequence in 50 DNA samples (35 leucocytes, 15 tumors) from 46 prolactinoma patients (59% males, 41% females). This identified six germline PRLR variants, which comprised four rare variants (Gly57Ser, Glu376Gln, Arg453Trp and Asn492Ile) and two low-frequency variants (Ile76Val, Ile146Leu), but no somatic variants. The rare variants, Glu376Gln and Asn492Ile, which were in complete linkage disequilibrium, and are located in the PRLR intracellular domain, occurred with significantly higher frequencies (P < 0.0001) in prolactinoma patients than in 60 706 individuals of the Exome Aggregation Consortium cohort and 7045 individuals of the Oxford Biobank. In vitro analysis of the PRLR variants demonstrated that the Asn492Ile variant, but not Glu376Gln, when compared to wild-type (WT) PRLR, increased prolactin-induced pAkt signaling (>1.3-fold, P < 0.02) and proliferation (1.4-fold, P < 0.02), but did not affect pSTAT5 signaling. Treatment of cells with an Akt1/2 inhibitor or everolimus, which acts on the Akt pathway, reduced Asn492Ile signaling and proliferation to WT levels. Thus, our results identify an association between a gain-of-function PRLR variant and prolactinomas and reveal a new etiology and potential therapeutic approach for these neoplasms.

Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection

Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1%) explain less than 10% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.

Human Semaphorin 3 Variants Link Melanocortin Circuit Development and Energy Balance

Hypothalamic melanocortin neurons play a pivotal role in weight regulation. Here, we examined the contribution of Semaphorin 3 (SEMA3) signaling to the development of these circuits. In genetic studies, we found 40 rare variants in SEMA3A-G and their receptors (PLXNA1-4; NRP1-2) in 573 severely obese individuals; variants disrupted secretion and/or signaling through multiple molecular mechanisms. Rare variants in this set of genes were significantly enriched in 982 severely obese cases compared to 4,449 controls. In a zebrafish mutagenesis screen, deletion of 7 genes in this pathway led to increased somatic growth and/or adiposity demonstrating that disruption of Semaphorin 3 signaling perturbs energy homeostasis. In mice, deletion of the Neuropilin-2 receptor in Pro-opiomelanocortin neurons disrupted their projections from the arcuate to the paraventricular nucleus, reduced energy expenditure, and caused weight gain. Cumulatively, these studies demonstrate that SEMA3-mediated signaling drives the development of hypothalamic melanocortin circuits involved in energy homeostasis.

Established and emerging strategies to crack the genetic code of obesity

Tremendous progress has been made in the genetic elucidation of obesity over the past two decades, driven largely by technological, methodological and organizational innovations. Current strategies for identifying obesity-predisposing loci/genes, including cytogenetics, linkage analysis, homozygosity mapping, admixture mapping, candidate gene studies, genome-wide association studies, custom genotyping arrays, whole-exome sequencing and targeted exome sequencing, have achieved differing levels of success, and the identified loci in aggregate explain only a modest fraction of the estimated heritability of obesity. This review outlines the successes and limitations of these approaches and proposes novel strategies, including the use of exceptionally large sample sizes, the study of diverse ethnic groups and deep phenotypes and the application of innovative methods and study designs, to identify the remaining obesity-predisposing genes. The use of both established and emerging strategies has the potential to crack the genetic code of obesity in the not-too-distant future. The resulting knowledge is likely to yield improvements in obesity prediction, prevention and care.

VarGenius executes cohort-level DNA-seq variant calling and annotation and allows to manage the resulting data through a PostgreSQL database

BACKGROUND

Targeted resequencing has become the most used and cost-effective approach for identifying causative mutations of Mendelian diseases both for diagnostics and research purposes. Due to very rapid technological progress, NGS laboratories are expanding their capabilities to address the increasing number of analyses. Several open source tools are available to build a generic variant calling pipeline, but a tool able to simultaneously execute multiple analyses, organize, and categorize the samples is still missing.

RESULTS

Here we describe VarGenius, a Linux based command line software able to execute customizable pipelines for the analysis of multiple targeted resequencing data using parallel computing. VarGenius provides a database to store the output of the analysis (calling quality statistics, variant annotations, internal allelic variant frequencies) and sample information (personal data, genotypes, phenotypes). VarGenius can also perform the "joint analysis" of hundreds of samples with a single command, drastically reducing the time for the configuration and execution of the analysis. VarGenius executes the standard pipeline of the Genome Analysis Tool-Kit (GATK) best practices (GBP) for germinal variant calling, annotates the variants using Annovar, and generates a user-friendly output displaying the results through a web page. VarGenius has been tested on a parallel computing cluster with 52 machines with 120GB of RAM each. Under this configuration, a 50 M whole exome sequencing (WES) analysis for a family was executed in about 7 h (trio or quartet); a joint analysis of 30 WES in about 24 h and the parallel analysis of 34 single samples from a 1 M panel in about 2 h.

CONCLUSIONS

We developed VarGenius, a "master" tool that faces the increasing demand of heterogeneous NGS analyses and allows maximum flexibility for downstream analyses. It paves the way to a different kind of analysis, centered on cohorts rather than on singleton. Patient and variant information are stored into the database and any output file can be accessed programmatically. VarGenius can be used for routine analyses by biomedical researchers with basic Linux skills providing additional flexibility for computational biologists to develop their own algorithms for the comparison and analysis of data. The software is freely available at: https://github.com/frankMusacchia/VarGenius.

Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability ([Formula: see text]) versus 2.1 ± 0.2% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 ± 12% of [Formula: see text] versus 12 ± 2% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%).

The demographic and adaptive history of central African hunter-gatherers and farmers

Central Africa, a forested region that supports an exceptionally high biodiversity, hosts the world's largest group of hunter-gatherers, who live in close proximity with groups that have adopted agriculture over the past 5000 years. Our understanding of the prehistory of these populations has been dramatically hampered by the almost total absence of fossil remains in this region, a limitation that has recently been circumvented by population genomics approaches. Different studies have estimated that ancestors of rainforest hunter-gatherers and Bantu-speaking farmers separated more than 60 000 years ago, supporting the occurrence of ancient population structure in Africa since the Late Pleistocene. Conversely, the Holocene in central Africa was characterized by large-scale population migrations associated with the emergence of agriculture, and increased genetic interactions between autochthonous rainforest hunter-gatherers and expanding Bantu-speaking farmers. Genomic scans have detected numerous candidate loci for positive selection in these populations, including convergent adaptation for short stature in groups of rainforest hunter-gatherers and local adaptation to endemic malaria in western and central Africans. Furthermore, there is recent increasing evidence that adaptive variation has been acquired by various African populations through admixture, suggesting a previously unappreciated role of intraspecies gene flow in local adaptation. Ancient and modern DNA studies will greatly broaden, and probably challenge, our view on the past history of central Africa, where introgression from yet uncharacterized archaic hominins and long-term adaptation to distinct ecological niches are suspected.

Common Variant Burden Contributes to the Familial Aggregation of Migraine in 1,589 Families

Complex traits, including migraine, often aggregate in families, but the underlying genetic architecture behind this is not well understood. The aggregation could be explained by rare, penetrant variants that segregate according to Mendelian inheritance or by the sufficient polygenic accumulation of common variants, each with an individually small effect, or a combination of the two hypotheses. In 8,319 individuals across 1,589 migraine families, we calculated migraine polygenic risk scores (PRS) and found a significantly higher common variant burden in familial cases (n = 5,317, OR = 1.76, 95% CI = 1.71-1.81, p = 1.7 × 10-109) compared to population cases from the FINRISK cohort (n = 1,101, OR = 1.32, 95% CI = 1.25-1.38, p = 7.2 × 10-17). The PRS explained 1.6% of the phenotypic variance in the population cases and 3.5% in the familial cases (including 2.9% for migraine without aura, 5.5% for migraine with typical aura, and 8.2% for hemiplegic migraine). The results demonstrate a significant contribution of common polygenic variation to the familial aggregation of migraine.

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures

Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03)], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases.

Using Full Genomic Information to Predict Disease: Breaking Down the Barriers Between Complex and Mendelian Diseases

While sequence-based genetic tests have long been available for specific loci, especially for Mendelian disease, the rapidly falling costs of genome-wide genotyping arrays, whole-exome sequencing, and whole-genome sequencing are moving us toward a future where full genomic information might inform the prognosis and treatment of a variety of diseases, including complex disease. Similarly, the availability of large populations with full genomic information has enabled new insights about the etiology and genetic architecture of complex disease. Insights from the latest generation of genomic studies suggest that our categorization of diseases as complex may conceal a wide spectrum of genetic architectures and causal mechanisms that ranges from Mendelian forms of complex disease to complex regulatory structures underlying Mendelian disease. Here, we review these insights, along with advances in the prediction of disease risk and outcomes from full genomic information.

Population genetics and GWAS: A primer

This primer provides some background to help non-specialists understand a new theoretical evolutionary genetics study that helps explain why thousands of variants of small effect contribute to complex traits.

A population genetic interpretation of GWAS findings for human quantitative traits

Human genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes-notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and because genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed-form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWASs is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWASs for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose shortly before or during the Out-of-Africa bottleneck at sites with selection coefficients around s = 10-3.

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.