Rare and low-frequency coding variants alter human adult height

Complex Phenotypes: Mechanisms Underlying Variation in Human Stature

PURPOSE OF REVIEW

The goal of the review is to provide a comprehensive overview of the current understanding of the mechanisms underlying variation in human stature.

RECENT FINDINGS

Human height is an anthropometric trait that varies considerably within human populations as well as across the globe. Historically, much research focus was placed on understanding the biology of growth plate chondrocytes and how modifications to core chondrocyte proliferation and differentiation pathways potentially shaped height attainment in normal as well as pathological contexts. Recently, much progress has been made to improve our understanding regarding the mechanisms underlying the normal and pathological range of height variation within as well as between human populations, and today, it is understood to reflect complex interactions among a myriad of genetic, environmental, and evolutionary factors. Indeed, recent improvements in genetics (e.g., GWAS) and breakthroughs in functional genomics (e.g., whole exome sequencing, DNA methylation analysis, ATAC-sequencing, and CRISPR) have shed light on previously unknown pathways/mechanisms governing pathological and common height variation. Additionally, the use of an evolutionary perspective has also revealed important mechanisms that have shaped height variation across the planet. This review provides an overview of the current knowledge of the biological mechanisms underlying height variation by highlighting new research findings on skeletal growth control with an emphasis on previously unknown pathways/mechanisms influencing pathological and common height variation. In this context, this review also discusses how evolutionary forces likely shaped the genomic architecture of height across the globe.

Deconstructing the sources of genotype-phenotype associations in humans

Efforts to link variation in the human genome to phenotypes have progressed at a tremendous pace in recent decades. Most human traits have been shown to be affected by a large number of genetic variants across the genome. To interpret these associations and to use them reliably-in particular for phenotypic prediction-a better understanding of the many sources of genotype-phenotype associations is necessary. We summarize the progress that has been made in this direction in humans, notably in decomposing direct and indirect genetic effects as well as population structure confounding. We discuss the natural next steps in data collection and methodology development, with a focus on what can be gained by analyzing genotype and phenotype data from close relatives.

Characterizing rare and low-frequency height-associated variants in the Japanese population

Human height is a representative phenotype to elucidate genetic architecture. However, the majority of large studies have been performed in European population. To investigate the rare and low-frequency variants associated with height, we construct a reference panel (N = 3,541) for genotype imputation by integrating the whole-genome sequence data from 1,037 Japanese with that of the 1000 Genomes Project, and perform a genome-wide association study in 191,787 Japanese. We report 573 height-associated variants, including 22 rare and 42 low-frequency variants. These 64 variants explain 1.7% of the phenotypic variance. Furthermore, a gene-based analysis identifies two genes with multiple height-increasing rare and low-frequency nonsynonymous variants (SLC27A3 and CYP26B1; P_SKAT-O < 2.5 × 10⁻⁶). Our analysis shows a general tendency of the effect sizes of rare variants towards increasing height, which is contrary to findings among Europeans, suggesting that height-associated rare variants are under different selection pressure in Japanese and European populations.

Thousands of genetic loci are known to associate with human height, but these are mainly based on studies in European ancestry populations. Here, Akiyama et al. construct a genotype reference panel for the Japanese population followed by GWAS and report 573 height associated variants in 191,787 Japanese.

A common variant of the pregnancy-associated plasma protein-A (PAPPA) gene encodes a protein with reduced proteolytic activity towards IGF-binding proteins

Pregnancy-associated plasma protein-A (PAPP-A) is a key regulator of insulin-like growth factor (IGF) bioactivity, by releasing the IGFs from their corresponding IGF-binding proteins (IGFBPs). The minor allele of the single nucleotide polymorphism (SNP), rs7020782 (serine < tyrosine), in PAPPA has previously been associated with recurrent pregnancy loss as well as with significant reduced levels of PAPP-A protein in human ovarian follicles. The aim of the present study was to reveal a possible functional effect of the rs7020782 SNP in PAPPA by comparing recombinant PAPP-A proteins from transfected human embryonic kidney 293 T cells. The proteolytic cleavage of IGFBP-4 was shown to be affected by the rs7020782 SNP in PAPPA, showing a significantly reduced cleavage rate for the serine variant compared to the tyrosine variant (p-value < 0.001). The serine variant also showed a trend towards reduced cleavage rates, that was not significant, towards IGFBP-2 and IGFBP-5 compared to the tyrosine variant. No differences were found when analysing cell surface binding, complex formation between PAPP-A and STC2 or proMBP, nor when analysing STC1 inhibition of PAPP-A-mediated IGFBP-4 cleavage. Regulation of IGF bioactivity in reproductive tissues is important and the rs7020782 SNP in PAPPA may disturb this regulation by altering the specific activity of PAPP-A.

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche

Language is universal, but it has few indisputably universal characteristics, with cross-linguistic variation being the norm. For example, languages differ greatly in the number of syllables they allow, resulting in large variation in the Shannon information per syllable. Nevertheless, all natural languages allow their speakers to efficiently encode and transmit information. We show here, using quantitative methods on a large cross-linguistic corpus of 17 languages, that the coupling between language-level (information per syllable) and speaker-level (speech rate) properties results in languages encoding similar information rates (~39 bits/s) despite wide differences in each property individually: Languages are more similar in information rates than in Shannon information or speech rate. These findings highlight the intimate feedback loops between languages' structural properties and their speakers' neurocognition and biology under communicative pressures. Thus, language is the product of a multiscale communicative niche construction process at the intersection of biology, environment, and culture.

Epigenetic disturbances in obesity and diabetes: Epidemiological and functional insights

BACKGROUND

Obesity and type 2 diabetes (T2D) are major public health issues worldwide, and put a significant burden on the healthcare system. Genetic variants, along with traditional risk factors such as diet and physical activity, could account for up to approximately a quarter of disease risk. Epigenetic factors have demonstrated potential in accounting for additional phenotypic variation, along with providing insights into the causal relationship linking genetic variants to phenotypes.

SCOPE OF REVIEW

In this review article, we discuss the epidemiological and functional insights into epigenetic disturbances in obesity and diabetes, along with future research directions and approaches, with a focus on DNA methylation.

MAJOR CONCLUSIONS

Epigenetic mechanisms have been shown to contribute to obesity and T2D disease development, as well as potential differences in disease risks between ethnic populations. Technology to investigate epigenetic profiles in diseased individuals and tissues has advanced significantly in the last years, and suggests potential in application of epigenetic factors in clinical monitoring and as therapeutic options.

Estimation of DNA contamination and its sources in genotyped samples

Array genotyping is a cost-effective and widely used tool that enables assessment of up to millions of genetic markers in hundreds of thousands of individuals. Genotyping array data are typically highly accurate but sensitive to mixing of DNA samples from multiple individuals before or during genotyping. Contaminated samples can lead to genotyping errors and consequently cause false positive signals or reduce power of association analyses. Here, we propose a new method to identify contaminated samples and the sources of contamination within a genotyping batch. Through analysis of array intensity and genotype data from intentionally mixed samples and 22,366 samples of the Michigan Genomics Initiative, an ongoing biobank-based study, we show that our method can reliably estimate contamination. We also show that identifying sources of contamination can implicate problematic sample processing steps and guide process improvements. Compared to existing methods, our approach can estimate the proportion of contaminating DNA more accurately, eliminate the need for external databases of allele frequencies, and provide contamination estimates that are more robust to the ancestral origin of the contaminating sample.

Efficient Mining of Variants From Trios for Ventricular Septal Defect Association Study

Ventricular septal defect (VSD) is a fatal congenital heart disease showing severe consequence in affected infants. Early diagnosis plays an important role, particularly through genetic variants. Existing panel-based approaches of variants mining suffer from shortage of large panels, costly sequencing, and missing rare variants. Although a trio-based method alleviates these limitations to some extent, it is agnostic to novel mutations and computational intensive. Considering these limitations, we are studying a novel variants mining algorithm from trio-based sequencing data and apply it on a VSD trio to identify associated mutations. Our approach starts with irrelevant k-mer filtering from sequences of a trio via a newly conceived coupled Bloom Filter, then corrects sequencing errors by using a statistical approach and extends kept k-mers into long sequences. These extended sequences are used as input for variants needed. Later, the obtained variants are comprehensively analyzed against existing databases to mine VSD-related mutations. Experiments show that our trio-based algorithm narrows down candidate coding genes and lncRNAs by about 10- and 5-folds comparing with single sequence-based approaches, respectively. Meanwhile, our algorithm is 10 times faster and 2 magnitudes memory-frugal compared with existing state-of-the-art approach. By applying our approach to a VSD trio, we fish out an unreported gene—CD80, a combination of two genes—MYBPC3 and TRDN and a lncRNA—NONHSAT096266.2, which are highly likely to be VSD-related.

Longitudinal associations between body composition and regional fat distribution and later attained height at school entry among preschool children predisposed to overweight

BACKGROUND/OBJECTIVES

To investigate the associations between indicators of obesity and fat distribution, such as body mass index (BMI), fat mass, and skinfold measures during preschool age, and attained height at school entry.

SUBJECTS/METHODS

The Healthy Start primary intervention study comprised 1100 obesity-prone preschool children from the greater Copenhagen area, with a mean [standard deviation (SD)] age of 4.0 (1.1) years at baseline. Anthropometry was measured by trained health professionals at baseline (preschool age) and follow-up height at school entry was gathered by school nurses. Prospective associations between body fat measures and later attained height were examined using generalized linear models with adjustments for potential confounders.

RESULTS

Greater adiposity at preschool age was directly associated with a higher attained height at follow-up at school-age, when adjusting for confounders. A baseline difference of one BMI unit was associated with a greater attained height of 0.8 cm [(95% confidence interval (CI) 0.5; 1.2]. Furthermore, a difference of 1 mm in the sum of four skinfolds measured at baseline was associated with a greater attained height of 0.1 cm (95% CI 0.03; 0.2) at follow-up. Children with overweight or obesity at baseline attained a significantly higher height of 2.9 (95% CI 1.6; 4.1) cm at follow-up after full adjustment than normal weight children.

CONCLUSIONS

Our results supports that greater adiposity at preschool age is associated with greater tallness. Although a greater height is assumed to be desirable, accelerated growth in childhood may in itself be a risk factor for obesity later in life.

Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2

Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.

Exome sequencing of Finnish isolates enhances rare-variant association power

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene-trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2, known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.

Temperature and insulin signaling regulate body size in Hydra by the Wnt and TGF-beta pathways

How multicellular organisms assess and control their size is a fundamental question in biology, yet the molecular and genetic mechanisms that control organ or organism size remain largely unsolved. The freshwater polyp Hydra demonstrates a high capacity to adapt its body size to different temperatures. Here we identify the molecular mechanisms controlling this phenotypic plasticity and show that temperature-induced cell number changes are controlled by Wnt- and TGF-β signaling. Further we show that insulin-like peptide receptor (INSR) and forkhead box protein O (FoxO) are important genetic drivers of size determination controlling the same developmental regulators. Thus, environmental and genetic factors directly affect developmental mechanisms in which cell number is the strongest determinant of body size. These findings identify the basic mechanisms as to how size is regulated on an organismic level and how phenotypic plasticity is integrated into conserved developmental pathways in an evolutionary informative model organism.

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?

Analysis of the genetic basis of height in large Jewish nuclear families

Despite intensive study, most of the specific genetic factors that contribute to variation in human height remain undiscovered. We conducted a family-based linkage study of height in a unique cohort of very large nuclear families from a founder (Jewish) population. This design allowed for increased power to detect linkage, compared to previous family-based studies. Loci we identified in discovery families could explain an estimated lower bound of 6% of the variance in height in validation families. We showed that these loci are not tagging known common variants associated with height. Rather, we suggest that the observed signals arise from variants with large effects that are rare globally but elevated in frequency in the Jewish population.

Rare variants with large effects have been suggested to account for some of the missing heritability of height, but detection of such variants in genome-wide association studies (GWAS) requires very large sample sizes due to their low frequencies. Here, we designed a unique study of height, in which we sought to increase the effective frequency of rare variants in our sample. This was done by assembling a cohort of very large nuclear families, with an average of 12 and a maximum of 20 siblings per nuclear family. In this design, any variant segregating in our cohort has a minimum expected frequency of ~1%, regardless of its frequency in the general population. In addition, we recruited all participants from a founder (Jewish) population, in which some variants that are rare in a cosmopolitan population can rise to high frequencies. To further increase power, we developed methods to obtain highly accurate height measurements, genotype calls, and inheritance pattern reconstructions. These factors allowed us to detect many more QTLs than previous family-based studies of height, despite a modest sample size of only 397 participants. The approach described in this paper provides insights into the genetic basis of height and the roles of population-specific vs. cosmopolitan variants, and may serve as a complement to GWAS for genetic investigations of other complex traits.

Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p < 2 × 10^-7). Comparison of exome array variants to regional linkage disequilibrium (LD) patterns and prior genome-wide association study (GWAS) results detected candidate variants (r² > .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p < 1 × 10^-4) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.

RNA sequencing of bipolar disorder lymphoblastoid cell lines implicates the neurotrophic factor HRP-3 in lithium's clinical efficacy

Objectives: Lithium remains the oldest and most effective treatment for mood stabilisation in bipolar disorder (BD), even though at least half of patients are only partially responsive or do not respond. This study aimed to identify biomarkers associated with lithium response in BD, based on comparing RNA sequencing information derived from lymphoblastoid cell lines (LCLs) of lithium-responsive (LR) versus lithium non-responsive (LNR) BD patients, to assess gene expression variations that might bear on treatment outcome. Methods: RNA sequencing was carried out on 24 LCLs from female BD patients (12 LR and 12 LNR) followed by qPCR validation in two additional independent cohorts (41 and 17 BD patients, respectively). Results: Fifty-six genes showed nominal differential expression comparing LR and LNR (FC ≥ |1.3|, P ≤ 0.01). The differential expression of HDGFRP3 and ID2 was validated by qPCR in the independent cohorts. Conclusions: We observed higher expression levels of HDGFRP3 and ID2 in BD patients who favourably respond to lithium. Both of these genes are involved in neurogenesis, and HDGFRP3 has been suggested to be a neurotrophic factor. Additional studies in larger BD cohorts are needed to confirm the potential of HDGFRP3 and ID2 expression levels in blood cells as tentative favourable lithium response biomarkers.

The insulin-like growth factor 2 gene in mammals: Organizational complexity within a conserved locus

The secreted protein, insulin-like growth factor 2 (IGF2), plays a central role in fetal and prenatal growth and development, and is regulated at the genetic level by parental imprinting, being expressed predominantly from the paternally derived chromosome in mice and humans. Here, IGF2/Igf2 and its locus has been examined in 19 mammals from 13 orders spanning ~166 million years of evolutionary development. By using human or mouse DNA segments as queries in genome analyses, and by assessing gene expression using RNA-sequencing libraries, more complexity was identified within IGF2/Igf2 than was annotated previously. Multiple potential 5’ non-coding exons were mapped in most mammals and are presumably linked to distinct IGF2/Igf2 promoters, as shown for several species by interrogating RNA-sequencing libraries. DNA similarity was highest in IGF2/Igf2 coding exons; yet, even though the mature IGF2 protein was conserved, versions of 67 or 70 residues are produced secondary to species-specific maintenance of alternative RNA splicing at a variable intron-exon junction. Adjacent H19 was more divergent than IGF2/Igf2, as expected in a gene for a noncoding RNA, and was identified in only 10/19 species. These results show that common features, including those defining IGF2/Igf2 coding and several non-coding exons, were likely present at the onset of the mammalian radiation, but that others, such as a putative imprinting control region 5’ to H19 and potential enhancer elements 3’ to H19, diversified with speciation. This study also demonstrates that careful analysis of genomic and gene expression repositories can provide new insights into gene structure and regulation.

A frequent variant in the Japanese population determines quasi-Mendelian inheritance of rare retinal ciliopathy

Hereditary retinal degenerations (HRDs) are Mendelian diseases characterized by progressive blindness and caused by ultra-rare mutations. In a genomic screen of 331 unrelated Japanese patients, we identify a disruptive Alu insertion and a nonsense variant (p.Arg1933*) in the ciliary gene RP1, neither of which are rare alleles in Japan. p.Arg1933* is almost polymorphic (frequency = 0.6%, amongst 12,000 individuals), does not cause disease in homozygosis or heterozygosis, and yet is significantly enriched in HRD patients (frequency = 2.1%, i.e., a 3.5-fold enrichment; p-value = 9.2 × 10-5). Familial co-segregation and association analyses show that p.Arg1933* can act as a Mendelian mutation in trans with the Alu insertion, but might also associate with disease in combination with two alleles in the EYS gene in a non-Mendelian pattern of heredity. Our results suggest that rare conditions such as HRDs can be paradoxically determined by relatively common variants, following a quasi-Mendelian model linking monogenic and complex inheritance.

Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent because of the differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an area under the ROC curve of 0.65. Here, we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. In addition, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans.

Advances in genetics of migraine

Background

Migraine is a complex neurovascular disorder with a strong genetic component. There are rare monogenic forms of migraine, as well as more common polygenic forms; research into the genes involved in both types has provided insights into the many contributing genetic factors. This review summarises advances that have been made in the knowledge and understanding of the genes and genetic variations implicated in migraine etiology.

Findings

Migraine is characterised into two main types, migraine without aura (MO) and migraine with aura (MA). Hemiplegic migraine is a rare monogenic MA subtype caused by mutations in three main genes - CACNA1A, ATP1A2 and SCN1A - which encode ion channel and transport proteins. Functional studies in cellular and animal models show that, in general, mutations result in impaired glutamatergic neurotransmission and cortical hyperexcitability, which make the brain more susceptible to cortical spreading depression, a phenomenon thought to coincide with aura symptoms. Variants in other genes encoding ion channels and solute carriers, or with roles in regulating neurotransmitters at neuronal synapses, or in vascular function, can also cause monogenic migraine, hemiplegic migraine and related disorders with overlapping symptoms. Next-generation sequencing will accelerate the finding of new potentially causal variants and genes, with high-throughput bioinformatics analysis methods and functional analysis pipelines important in prioritising, confirming and understanding the mechanisms of disease-causing variants.

With respect to common migraine forms, large genome-wide association studies (GWAS) have greatly expanded our knowledge of the genes involved, emphasizing the role of both neuronal and vascular pathways. Dissecting the genetic architecture of migraine leads to greater understanding of what underpins relationships between subtypes and comorbid disorders, and may have utility in diagnosis or tailoring treatments. Further work is required to identify causal polymorphisms and the mechanism of their effect, and studies of gene expression and epigenetic factors will help bridge the genetics with migraine pathophysiology.

Conclusions

The complexity of migraine disorders is mirrored by their genetic complexity. A comprehensive knowledge of the genetic factors underpinning migraine will lead to improved understanding of molecular mechanisms and pathogenesis, to enable better diagnosis and treatments for migraine sufferers.

Sp1 contributes to overexpression of stanniocalcin 2 through regulation of promoter activity in colon adenocarcinoma

BACKGROUND

Aberrant expression of stanniocalcin 2 (STC2) is implicated in colon adenocarcinoma (COAD). A previous study identified that STC2 functions as a tumor promoter to drive development of some cancers, but the role of its overexpression in the development of COAD remains unclear.

AIM

To evaluate the regulation mechanism of STC2 overexpression in COAD.

METHODS

The expression of STC2 in COAD was assessed by TCGA COAD database and GEO (GSE50760). Methylation level of the STC2 promoter was evaluated with beta value in UALCAN platform, and the correlation between STC2 expression and survival rate was investigated with TCGA COAD. Transcription binding site prediction was conducted by TRANSFAC and LASAGNA, and a luciferase reporter system was used to identify STC2 promoter activity in several cell lines, including HEK293T, NCM460, HT29, SW480, and HCT116. Western blotting was performed to evaluate the role of Sp1 on the expression of STC2.

RESULTS

The central finding of this work is that STC2 is overexpressed in COAD tissues and positively correlated with poor prognosis. Importantly, the binding site of the transcription factor Sp1 is widely located in the promoter region of STC2. A luciferase reporter system was successfully constructed to analyze the transcription activity of STC2, and knocking down the expression of Sp1 significantly inhibited the transcription activity of STC2. Furthermore, inhibition of Sp1 remarkably decreased protein levels of STC2.

CONCLUSION

Our data provide evidence that the transcription factor Sp1 is essential for the overexpression of STC2 in COAD through activation of promoter activity. Taken together, our finding provides new insights into the mechanism of oncogenic function of COAD by STC2.

RAD-sequencing for estimating genomic relatedness matrix-based heritability in the wild: A case study in roe deer

Estimating the evolutionary potential of quantitative traits and reliably predicting responses to selection in wild populations are important challenges in evolutionary biology. The genomic revolution has opened up opportunities for measuring relatedness among individuals with precision, enabling pedigree-free estimation of trait heritabilities in wild populations. However, until now, most quantitative genetic studies based on a genomic relatedness matrix (GRM) have focused on long-term monitored populations for which traditional pedigrees were also available, and have often had access to knowledge of genome sequence and variability. Here, we investigated the potential of RAD-sequencing for estimating heritability in a free-ranging roe deer (Capreolous capreolus) population for which no prior genomic resources were available. We propose a step-by-step analytical framework to optimize the quality and quantity of the genomic data and explore the impact of the single nucleotide polymorphism (SNP) calling and filtering processes on the GRM structure and GRM-based heritability estimates. As expected, our results show that sequence coverage strongly affects the number of recovered loci, the genotyping error rate and the amount of missing data. Ultimately, this had little effect on heritability estimates and their standard errors, provided that the GRM was built from a minimum number of loci (above 7,000). Genomic relatedness matrix-based heritability estimates thus appear robust to a moderate level of genotyping errors in the SNP data set. We also showed that quality filters, such as the removal of low-frequency variants, affect the relatedness structure of the GRM, generating lower h² estimates. Our work illustrates the huge potential of RAD-sequencing for estimating GRM-based heritability in virtually any natural population.

Genetic causes of isolated short stature

Short stature is a common feature, and frequently remains without a specific diagnosis after conventional clinical and laboratorial evaluation. Longitudinal growth is mainly determined by genetic factors, and hundreds of common variants have been associated to height variability among healthy individuals. Although isolated short stature may be caused by the combination of variants, with a deleterious impact on the growth of individuals with polygenic inheritance, recent studies have pointed out some monogenic defects as the cause of the growth disorder observed in nonsyndromic children. The majority of these defects are in genes related to the growth plate cartilage and in the growth hormone (GH) - insulin-like growth factor 1 (IGF-1) axis. Affected patients usually present the mildest spectrum of some forms of skeletal dysplasia, or subtle abnormalities of laboratory tests, suggesting hormonal resistance or insensibility. The lack of specific characteristics, however, does not allow formulation of a definitive diagnosis without the use of broad genetic studies. Thus, molecular genetic studies including panels of genes or exome analysis will become essential in investigating and identifying the causes of isolated short stature in children, with a crucial impact on treatment and follow-up.

Update on the predictability of tall stature from DNA markers in Europeans

Predicting adult height from DNA has important implications in forensic DNA phenotyping. In 2014, we introduced a prediction model consisting of 180 height-associated SNPs based on data from 10,361 Northwestern Europeans enriched with tall individuals (770 > 1.88 standard deviation), which yielded a mid-ranged accuracy (AUC = 0.75 for binary prediction of tall stature and R² = 0.12 for quantitative prediction of adult height). Here, we provide an update on DNA-based height predictability considering an enlarged list of subsequently-published height-associated SNPs using data from the same set of 10,361 Europeans. A prediction model based on the full set of 689 SNPs showed an improved accuracy relative to previous models for both tall stature (AUC = 0.79) and quantitative height (R² = 0.21). A feature selection analysis revealed a subset of 412 most informative SNPs while the corresponding prediction model retained most of the accuracy (AUC = 0.76 and R² = 0.19) achieved with the full model. Over all, our study empirically exemplifies that the accuracy for predicting human appearance phenotypes with very complex underlying genetic architectures, such as adult height, can be improved by increasing the number of phenotype-associated DNA variants. Our work also demonstrates that a careful sub-selection allows for a considerable reduction of the number of DNA predictors that achieve similar prediction accuracy as provided by the full set. This is forensically relevant due to restrictions in the number of SNPs simultaneously analyzable with forensically suitable DNA technologies in the current days of targeted massively parallel sequencing in forensic genetics.

Multigene Sequencing Analysis of Children Born Small for Gestational Age With Isolated Short Stature

CONTEXT

Patients born small for gestational age (SGA) who present with persistent short stature could have an underlying genetic etiology that will account for prenatal and postnatal growth impairment. We applied a unique massive parallel sequencing approach in cohort of patients with exclusively nonsyndromic SGA to simultaneously interrogate for clinically substantial genetic variants.

OBJECTIVE

To perform a genetic investigation of children with isolated short stature born SGA.

DESIGN

Screening by exome (n = 16) or targeted gene panel (n = 39) sequencing.

SETTING

Tertiary referral center for growth disorders.

PATIENTS AND METHODS

We selected 55 patients born SGA with persistent short stature without an identified cause of short stature.

MAIN OUTCOME MEASURES

Frequency of pathogenic findings.

RESULTS

We identified heterozygous pathogenic or likely pathogenic genetic variants in 8 of 55 patients, all in genes already associated with growth disorders. Four of the genes are associated with growth plate development, IHH (n = 2), NPR2 (n = 2), SHOX (n = 1), and ACAN (n = 1), and two are involved in the RAS/MAPK pathway, PTPN11 (n = 1) and NF1 (n = 1). None of these patients had clinical findings that allowed for a clinical diagnosis. Seven patients were SGA only for length and one was SGA for both length and weight.

CONCLUSION

These genomic approaches identified pathogenic or likely pathogenic genetic variants in 8 of 55 patients (15%). Six of the eight patients carried variants in genes associated with growth plate development, indicating that mild forms of skeletal dysplasia could be a cause of growth disorders in this group of patients.

Validation of Genome-Wide Polygenic Risk Scores for Coronary Artery Disease in French Canadians

BACKGROUND

Coronary artery disease (CAD) represents one of the leading causes of morbidity and mortality worldwide. Given the healthcare risks and societal impacts associated with CAD, their clinical management would benefit from improved prevention and prediction tools. Polygenic risk scores (PRS) based on an individual's genome sequence are emerging as potentially powerful biomarkers to predict the risk to develop CAD. Two recently derived genome-wide PRS have shown high specificity and sensitivity to identify CAD cases in European-ancestry participants from the UK Biobank. However, validation of the PRS predictive power and transferability in other populations is now required to support their clinical utility.

METHODS

We calculated both PRS (GPSCAD and metaGRSCAD) in French-Canadian individuals from 3 cohorts totaling 3639 prevalent CAD cases and 7382 controls and tested their power to predict prevalent, incident, and recurrent CAD. We also estimated the impact of the founder French-Canadian familial hypercholesterolemia deletion ( LDLR delta >15 kb deletion) on CAD risk in one of these cohorts and used this estimate to calibrate the impact of the PRS.

RESULTS

Our results confirm the ability of both PRS to predict prevalent CAD comparable to the original reports (area under the curve=0.72-0.89). Furthermore, the PRS identified about 6% to 7% of individuals at CAD risk similar to carriers of the LDLR delta >15 kb mutation, consistent with previous estimates. However, the PRS did not perform as well in predicting an incident or recurrent CAD (area under the curve=0.56-0.60), maybe because of confounding because 76% of the participants were on statin treatment. This result suggests that additional work is warranted to better understand how ascertainment biases and study design impact PRS for CAD.

CONCLUSIONS

Collectively, our results confirm that novel, genome-wide PRS is able to predict CAD in French Canadians; with further improvements, this is likely to pave the way towards more targeted strategies to predict and prevent CAD-related adverse events.

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use

BACKGROUND

Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk.

METHODS

We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci.

RESULTS

Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals.

CONCLUSIONS

Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.

A local score approach improves GWAS resolution and detects minor QTL: application to Medicago truncatula quantitative disease resistance to multiple Aphanomyces euteiches isolates

Quantitative trait loci (QTL) with small effects, which are pervasive in quantitative phenotypic variation, are difficult to detect in genome-wide association studies (GWAS). To improve their detection, we propose to use a local score approach that accounts for the surrounding signal due to linkage disequilibrium, by accumulating association signals from contiguous single markers. Simulations revealed that, in a GWAS context with high marker density, the local score approach outperforms single SNP p-value-based tests for detecting minor QTL (heritability of 5-10%) and is competitive with regard to alternative methods, which also aggregate p-values. Using more than five million SNPs, this approach was applied to identify loci involved in Quantitative Disease Resistance (QDR) to different isolates of the plant root rot pathogen Aphanomyces euteiches, from a GWAS performed on a collection of 174 accessions of the model legume Medicago truncatula. We refined the position of a previously reported major locus, underlying MYB/NB-ARC/tyrosine kinase candidate genes conferring resistance to two closely related A. euteiches isolates belonging to pea pathotype I. We also discovered a diversity of minor resistance QTL, not detected using p-value-based tests, some of which being putatively shared in response to pea (pathotype I and III) and/or alfalfa (race 1 and 2) isolates. Candidate genes underlying these QTL suggest pathogen effector recognition and plant proteasome as key functions associated with M. truncatula resistance to A. euteiches. GWAS on any organism can benefit from the local score approach to uncover many weak-effect QTL.

Functional rare and low frequency variants in BLK and BANK1 contribute to human lupus

Systemic lupus erythematosus (SLE) is the prototypic systemic autoimmune disease. It is thought that many common variant gene loci of weak effect act additively to predispose to common autoimmune diseases, while the contribution of rare variants remains unclear. Here we describe that rare coding variants in lupus-risk genes are present in most SLE patients and healthy controls. We demonstrate the functional consequences of rare and low frequency missense variants in the interacting proteins BLK and BANK1, which are present alone, or in combination, in a substantial proportion of lupus patients. The rare variants found in patients, but not those found exclusively in controls, impair suppression of IRF5 and type-I IFN in human B cell lines and increase pathogenic lymphocytes in lupus-prone mice. Thus, rare gene variants are common in SLE and likely contribute to genetic risk.

Function-altering variants of immune-related genes cause rare autoimmune syndromes, whereas their contribution to common autoimmune diseases remains uncharacterized. Here the authors show that rare variants of lupus-associated genes are present in the majority of lupus patients and healthy controls, but only the variants found in lupus patients alter gene function.

Weighting sequence variants based on their annotation increases the power of genome-wide association studies in dairy cattle

Background

Genome-wide association studies (GWAS) are widely used to identify regions of the genome that harbor genetic determinants of quantitative traits. However, the multiple-testing burden from scanning tens of millions of whole-genome sequence variants reduces the power to identify associated variants, especially if sample size is limited. In addition, factors such as inaccuracy of imputation, complex linkage disequilibrium structures, and multiple closely-located causal variants may result in an identified causative mutation not being the most significant single nucleotide polymorphism in a particular genomic region. Therefore, the use of information from different sources, particularly variant annotations, was proposed to enhance the fine-mapping of causal variants. Here, we tested whether applying significance thresholds based on variant annotation categories increases the power of GWAS compared with a flat Bonferroni multiple-testing correction.

Results

Whole-genome sequence variants in dairy cattle were categorized according to type and predicted impact. Then, GWAS between markers and 17 quantitative traits were analyzed for enrichment for association of each annotation category. By using annotation categories that were determined with the variants effect predictor software and datasets indicating regions of open chromatin, “low impact” variants were found to be highly enriched. Moreover, when the variants annotated as “modifier” and not located at open chromatin regions were further classified into different types of potential regulatory elements, the high impact variants, moderate impact variants, variants located in the 3′ and 5′ untranslated regions, and variants located in potential non-coding RNA regions exhibited relatively more enrichment. In contrast, a similar study on human GWAS data reported that enrichment of association signals was highest with high impact variants. We observed an increase in power when these variant category-based significance thresholds were applied for GWAS results on stature in Nordic Holstein cattle, as more candidate genes from previous large GWAS meta-analysis for cattle stature were confirmed.

Conclusions

Use of variant category-based genome-wide significance thresholds can marginally increase the power to detect the candidate genes in cattle. With the continued improvements in annotation of the bovine genome, we anticipate that the growing usefulness of variant category-based significance thresholds will be demonstrated.

Electronic supplementary material

The online version of this article (10.1186/s12711-019-0463-9) contains supplementary material, which is available to authorized users.

GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures

Bone area is one measure of bone size that is easily derived from dual-energy X-ray absorptiometry (DXA) scans. In a GWA study of DXA bone area of the hip and lumbar spine (N ≥ 28,954), we find thirteen independent association signals at twelve loci that replicate in samples of European and East Asian descent (N = 13,608 - 21,277). Eight DXA area loci associate with osteoarthritis, including rs143384 in GDF5 and a missense variant in COL11A1 (rs3753841). The strongest DXA area association is with rs11614913[T] in the microRNA MIR196A2 gene that associates with lumbar spine area (P = 2.3 × 10-42, β = -0.090) and confers risk of hip fracture (P = 1.0 × 10-8, OR = 1.11). We demonstrate that the risk allele is less efficient in repressing miR-196a-5p target genes. We also show that the DXA area measure contributes to the risk of hip fracture independent of bone density.

Mutations in PIK3C2A cause syndromic short stature, skeletal abnormalities, and cataracts associated with ciliary dysfunction

PIK3C2A is a class II member of the phosphoinositide 3-kinase (PI3K) family that catalyzes the phosphorylation of phosphatidylinositol (PI) into PI(3)P and the phosphorylation of PI(4)P into PI(3,4)P2. At the cellular level, PIK3C2A is critical for the formation of cilia and for receptor mediated endocytosis, among other biological functions. We identified homozygous loss-of-function mutations in PIK3C2A in children from three independent consanguineous families with short stature, coarse facial features, cataracts with secondary glaucoma, multiple skeletal abnormalities, neurological manifestations, among other findings. Cellular studies of patient-derived fibroblasts found that they lacked PIK3C2A protein, had impaired cilia formation and function, and demonstrated reduced proliferative capacity. Collectively, the genetic and molecular data implicate mutations in PIK3C2A in a new Mendelian disorder of PI metabolism, thereby shedding light on the critical role of a class II PI3K in growth, vision, skeletal formation and neurological development. In particular, the considerable phenotypic overlap, yet distinct features, between this syndrome and Lowe’s syndrome, which is caused by mutations in the PI-5-phosphatase OCRL, highlight the key role of PI metabolizing enzymes in specific developmental processes and demonstrate the unique non-redundant functions of each enzyme. This discovery expands what is known about disorders of PI metabolism and helps unravel the role of PIK3C2A and class II PI3Ks in health and disease.

Identifying the genetic basis of rare disorders can provide insight into gene function, susceptibility to disease, guide the development of new therapeutics, improve opportunities for genetic counseling, and help clinicians evaluate and potentially treat complicated clinical presentations. However, it is estimated that the genetic basis of approximately one-half of all rare genetic disorders remains unknown. We describe one such rare disorder based on genetic and clinical evaluations of individuals from 3 unrelated consanguineous families with a similar constellation of features including short stature, coarse facial features, cataracts with secondary glaucoma, multiple skeletal abnormalities, neurological manifestations including stroke, among other findings. We discovered that these features were due to deficiency of the PIK3C2A enzyme. PIK3C2A is a class II member of the phosphoinositide 3-kinase (PI3K) family that catalyzes the phosphorylation of the lipids phosphatidylinositol (PI) into PI(3)P and the phosphorylation of PI(4)P into PI(3,4)P2 that are essential for a variety of cellular processes including cilia formation and vesicle trafficking. This syndrome is the first monogenic disorder caused by mutations in a class II PI3K family member and thus sheds new light on their role in human development.

Predicting adult height from DNA variants in a European-Asian admixed population

Accurate genomic profiling for adult height is of high practical relevance in forensics genetics. Adult height is a classical reference trait in the field of human complex trait genetics characterized by highly polygenic nature and relatively high heritability. A meta-analysis of genome-wide association studies by the Genetic Investigation of Anthropocentric Traits (GIANT) consortium has identified 697 DNA variants associated with adult height in Europeans; however, whether these variants will still be informative in non-Europeans is still in question. The present study investigated the predictive power of these 697 height-associated SNPs in 687 Uyghurs of European-Asian admixed origin. Among all GIANT SNPs, 11% showed nominally significant association (6.78 × 10^-4 < p < 0.05) with adult height in the Uyghur population and among the significant SNPs 77% of allele effects were in the same direction as those in Europeans reported in the GIANT study. Fitting linear and logistic models using a polygenic score consisting of all GIANT SNPs resulted in an 80-20 cross-validated mean R² of 10.08% (95% CI 3.16-18.40%) for quantitative height prediction and a mean AUC value of 0.65 (95% CI 0.57-0.72%) for qualitative "above average" prediction. Fine-tuning the SNP set using their association p values considerably improved the prediction results (number of SNPs = 62, R² = 15.59%, 95% CI 6.80-25.71%; AUC = 0.70, 95% CI 62-0.77) in the Uyghurs. Overall, our findings demonstrate substantial differences between the European and Asian populations in the genetics of adult height, emphasizing the importance of population heterogeneity underlying the genetic architecture of adult height.

Meta-analysis of genome-wide association studies provides insights into genetic control of tomato flavor

Tomato flavor has changed over the course of long-term domestication and intensive breeding. To understand the genetic control of flavor, we report the meta-analysis of genome-wide association studies (GWAS) using 775 tomato accessions and 2,316,117 SNPs from three GWAS panels. We discover 305 significant associations for the contents of sugars, acids, amino acids, and flavor-related volatiles. We demonstrate that fruit citrate and malate contents have been impacted by selection during domestication and improvement, while sugar content has undergone less stringent selection. We suggest that it may be possible to significantly increase volatiles that positively contribute to consumer preferences while reducing unpleasant volatiles, by selection of the relevant allele combinations. Our results provide genetic insights into the influence of human selection on tomato flavor and demonstrate the benefits obtained from meta-analysis.

Flavor is one of the most important traits for improving tomato sensory quality and consumer acceptability. Here, the authors report meta-analysis of genome-wide association studies of flavor related traits and show genetic insights into the influence of human selection during domestication and improvement.

Functionally oriented analysis of cardiometabolic traits in a trans-ethnic sample

Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.

Rediscovering the value of families for psychiatric genetics research

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.

Mendelian randomisation analyses find pulmonary factors mediate the effect of height on coronary artery disease

There is evidence that lower height is associated with a higher risk of coronary artery disease (CAD) and increased risk of type 2 diabetes (T2D). It is not clear though whether these associations are causal, direct or mediated by other factors. Here we show that one standard deviation higher genetically determined height (~6.5 cm) is causally associated with a 16% decrease in CAD risk (OR = 0.84, 95% CI 0.80-0.87). This causal association remains after performing sensitivity analyses relaxing pleiotropy assumptions. The causal effect of height on CAD risk is reduced by 1-3% after adjustment for potential mediators (lipids, blood pressure, glycaemic traits, body mass index, socio-economic status). In contrast, our data suggest that lung function (measured by forced expiratory volume [FEV1] and forced vital capacity [FVC]) is a mediator of the effect of height on CAD. We observe no direct causal effect of height on the risk of T2D.

Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders

Studies of the genetics of psychiatric disorders have become one of the most exciting and fast-moving areas in human genetics. A decade ago, there were few reproducible findings, and now there are hundreds. In this review, we focus on the findings that have illuminated the genetic architecture of psychiatric disorders and the challenges of using these findings to inform our understanding of pathophysiology. The evidence is now overwhelming that psychiatric disorders are "polygenic"-that many genetic loci contribute to risk. With the exception of a subset of those with ASD, few individuals with a psychiatric disorder have a single, deterministic genetic cause; rather, developing a psychiatric disorder is influenced by hundreds of different genetic variants, consistent with a polygenic model. As progressively larger studies have uncovered more about their genetic architecture, the need to elucidate additional architectures has become clear. Even if we were to have complete knowledge of the genetic architecture of a psychiatric disorder, full understanding requires deep knowledge of the functional genomic architecture-the implicated loci impact regulatory processes that influence gene expression and the functional coordination of genes that control biological processes. Following from this is cellular architecture: of all brain regions, cell types, and developmental stages, where and when are the functional architectures operative? Given that the genetic architectures of different psychiatric disorders often strongly overlap, we are challenged to re-evaluate and refine the diagnostic architectures of psychiatric disorders using fundamental genetic and neurobiological data.

Pervasive function and evidence for selection across standing genetic variation in S. cerevisiae

Quantitative genetics aims to map genotype to phenotype, often with the goal of understanding how organisms evolved. However, it remains unclear whether the genetic variants identified are exemplary of evolution. Here we analyzed progeny of two wild Saccharomyces cerevisiae isolates to identify 195 loci underlying complex metabolic traits, resolving 107 to single polymorphisms with diverse molecular mechanisms. More than 20% of causal variants exhibited patterns of emergence inconsistent with neutrality. Moreover, contrary to drift-centric expectation, variation in diverse wild yeast isolates broadly exhibited this property: over 30% of shared natural variants exhibited phylogenetic signatures suggesting that they are not neutral. This pattern is likely attributable to both homoplasy and balancing selection on ancestral polymorphism. Variants that emerged repeatedly were more likely to have done so in isolates from the same ecological niche. Our results underscore the power of super-resolution mapping of ecologically relevant traits in understanding adaptation and evolution.

The genetics of depression: successful genome-wide association studies introduce new challenges

The recent successful genome-wide association studies (GWASs) for depression have yielded more than 80 replicated loci and brought back the excitement that had evaporated during the years of negative GWAS findings. The identified loci provide anchors to explore their relevance for depression, but this comes with new challenges. Using the watershed model of genotype-phenotype relationships as a conceptual aid and recent genetic findings on other complex phenotypes, we discuss why it took so long and identify seven future challenges. The biggest challenge involves the identification of causal mechanisms since GWAS associations merely flag genomic regions without a direct link to underlying biological function. Furthermore, the genetic association with the index phenotype may also be part of a more extensive causal pathway (e.g., from variant to comorbid condition) or be due to indirect influences via intermediate traits located in the causal pathways to the final outcome. This challenge is highly relevant for depression because even its narrow definition of major depressive disorder captures a heterogeneous set of phenotypes which are often measured by even more broadly defined operational definitions consisting of a few questions (minimal phenotyping). Here, Mendelian randomization and future discovery of additional genetic variants for depression and related phenotypes will be of great help. In addition, reduction of phenotypic heterogeneity may also be worthwhile. Other challenges include detecting rare variants, determining the genetic architecture of depression, closing the "heritability gap", and realizing the potential for personalized treatment. Along the way, we identify pertinent open questions that, when addressed, will advance the field.

Common Disease Is More Complex Than Implied by the Core Gene Omnigenic Model

The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent "omnigenic" or "core genes" model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.

Genomic basis of delayed reward discounting

Delayed reward discounting (DRD) is a behavioral economic measure of impulsivity, reflecting how rapidly a reward loses value based on its temporal distance. In humans, more impulsive DRD is associated with susceptibility to a number of psychiatric diseases (e.g., addiction, ADHD), health outcomes (e.g., obesity), and lifetime outcomes (e.g., educational attainment). Although the determinants of DRD are both genetic and environmental, this review focuses on its genetic basis. Both rodent studies using inbred strains and human twin studies indicate that DRD is moderately heritable, a conclusion that was further supported by a recent human genome-wide association study (GWAS) that used single nucleotide polymorphisms (SNP) to estimate heritability. The GWAS of DRD also identified genetic correlations with psychiatric diagnoses, health outcomes, and measures of cognitive performance. Future research priorities include rodent studies probing putative genetic mechanisms of DRD and human GWASs using larger samples and non-European cohorts. Continuing to characterize genomic influences on DRD has the potential to yield important biological insights with implications for a variety of medically and socially important outcomes.

Statistical power in genome-wide association studies and quantitative trait locus mapping

Power calculation prior to a genetic experiment can help investigators choose the optimal sample size to detect a quantitative trait locus (QTL). Without the guidance of power analysis, an experiment may be underpowered or overpowered. Either way will result in wasted resource. QTL mapping and genome-wide association studies (GWAS) are often conducted using a linear mixed model (LMM) with controls of population structure and polygenic background using markers of the whole genome. Power analysis for such a mixed model is often conducted via Monte Carlo simulations. In this study, we derived a non-centrality parameter for the Wald test statistic for association, which allows analytical power analysis. We show that large samples are not necessary to detect a biologically meaningful QTL, say explaining 5% of the phenotypic variance. Several R functions are provided so that users can perform power analysis to determine the minimum sample size required to detect a given QTL with a certain statistical power or calculate the statistical power with given sample size and known values of other population parameters.