Rare and low-frequency coding variants alter human adult height

A genetic approach to evaluation of short stature of undetermined cause

Short stature is a common presentation to paediatric endocrinologists. After exclusion of major endocrine or systemic disease, most children with short stature are diagnosed based on a description of their growth pattern and the height of their parents (eg, familial short stature). Height is a polygenic trait and genome-wide association studies have identified many of the associated genetic loci. Here we review the application of genetic studies, including copy number variant analysis, targeted gene panels, and whole-exome sequencing in children with idiopathic short stature. We estimate 25-40% of children diagnosed with idiopathic short stature could receive a molecular diagnosis using these technologies. A molecular diagnosis for short stature is important for affected individuals and their families and might inform treatment decisions surrounding use of growth hormone or insulin-like growth factor 1 therapy.

Exploring the role of low-frequency and rare exonic variants in alcohol and tobacco use

BACKGROUND

Alcohol and tobacco use are heritable phenotypes. However, only a small number of common genetic variants have been identified, and common variants account for a modest proportion of the heritability. Therefore, this study aims to investigate the role of low-frequency and rare variants in alcohol and tobacco use.

METHODS

We meta-analyzed ExomeChip association results from eight discovery cohorts and included 12,466 subjects and 7432 smokers in the analysis of alcohol consumption and tobacco use, respectively. The ExomeChip interrogates low-frequency and rare exonic variants, and in addition a small pool of common variants. We investigated top variants in an independent sample in which ICD-9 diagnoses of "alcoholism" (N = 25,508) and "tobacco use disorder" (N = 27,068) had been assessed. In addition to the single variant analysis, we performed gene-based, polygenic risk score (PRS), and pathway analyses.

RESULTS

The meta-analysis did not yield exome-wide significant results. When we jointly analyzed our top results with the independent sample, no low-frequency or rare variants reached significance for alcohol consumption or tobacco use. However, two common variants that were present on the ExomeChip, rs16969968 (p = 2.39 × 10^-7) and rs8034191 (p = 6.31 × 10^-7) located in CHRNA5 and AGPHD1 at 15q25.1, showed evidence for association with tobacco use.

DISCUSSION

Low-frequency and rare exonic variants with large effects do not play a major role in alcohol and tobacco use, nor does the aggregate effect of ExomeChip variants. However, our results confirmed the role of the CHRNA5-CHRNA3-CHRNB4 cluster of nicotinic acetylcholine receptor subunit genes in tobacco use.

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer

The breast cancer risk variants identified in genome-wide association studies explain only a small fraction of the familial relative risk, and the genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82 × 10-6, including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony-forming efficiency. Our study provides new insights into breast cancer genetics and biology.

Beyond disease susceptibility-Leveraging genome-wide association studies for new insights into complex disease biology

Genetic studies in complex diseases have been highly successful, but have also been largely one-dimensional: predominantly focusing on the genetic contribution to disease susceptibility. While this is undoubtedly important-indeed it is a pre-requisite for understanding the mechanisms underlying disease development-there are many other important aspects of disease biology that have received comparatively little attention. In this review, I will discuss how existing genetic data can be leveraged to provide new insights into other aspects of disease biology, why such insights could change the way we think about complex disease, and how this could provide opportunities for better therapies and/or facilitate personalised medicine. To do this, I will use the example of Crohn's disease-a chronic form of inflammatory bowel disease that has been one of the main success stories in complex disease genetics. Indeed, thanks to genetic studies, we now have a much more detailed understanding of the processes involved in Crohn's disease development, but still know relatively little about what determines the subsequent disease course (prognosis) and why this differs so considerably between individuals. I will discuss how we came to realise that genetic variation plays an important role in determining disease prognosis and how this has changed the way we think about Crohn's disease genetics. This will illustrate how phenotypic data can be used to leverage new insights from genetic data and will provide a broadly applicable framework that could yield new insights into the biology of multiple diseases.

Genome data uncover four synergistic key regulators for extremely small body size in horses

Background

Miniature size in horses represents an extreme reduction of withers height that originated after domestication. In some breeds, it is a highly desired trait representing a breed- or subtype-specific feature. The genomic changes that emerged due to strong-targeted selection towards this distinct type remain unclear.

Results

Comparisons of whole-genome sequencing data from two Miniature Shetland ponies and one standard-sized Shetland pony, performed to elucidate genetic determinants for miniature size, revealed four synergistic variants, limiting withers height to 34.25 in. (87 cm). Runs of homozygosity regions were detected spanning these four variants in both the Miniature Shetland ponies and the standard-sized Shetland pony. They were shown to be characteristic of the Shetland pony breed, resulting in a miniature type under specific genotypic combinations. These four genetic variants explained 72% of the size variation among Shetland ponies and related breeds. The length of the homozygous regions indicate that they arose over 1000 years ago. In addition, a copy number variant was identified in DIAPH3 harboring a loss exclusively in ponies and donkeys and thus representing a potential height-associated variant.

Conclusion

This study reveals main drivers for miniature size in horses identified in whole genome data and thus provides relevant candidate genes for extremely short stature in mammals.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4877-5) contains supplementary material, which is available to authorized users.

Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum

There is a limited understanding about the impact of rare protein-truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals. Protein-truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, and ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization, and reduced age at enrollment. Gene sets implicated from GWASs did not show a significant protein-truncating variants burden beyond what was captured by established Mendelian genes. In conclusion, we provide a thorough investigation of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.

Does Malnutrition Have a Genetic Component?

Malnutrition is a complex disorder, defined by an imbalance, excess, or deficiency of nutrient intake. The visible signs of malnutrition are stunted growth and wasting, but malnourished children are also more likely to have delays in neurocognitive development, vaccine failure, and susceptibility to infection. Despite malnutrition being a major global health problem, we do not yet understand the pathogenesis of this complex disorder. Although lack of food is a major contributor to childhood malnutrition, it is not the sole cause. The mother's prenatal nutritional status, enteric infections, and intestinal inflammation also contribute to the risk of childhood malnutrition and recovery. Here, we discuss another potential risk factor, host and maternal genetics, that may play a role in the risk of malnutrition via several biological pathways. Understanding the genetic risks of malnutrition may help to identify ideal targets for intervention and treatment of malnutrition.

ADAMTS proteins in human disorders

ADAMTS proteins are a superfamily of 26 secreted molecules comprising two related, but distinct families. ADAMTS proteases are zinc metalloendopeptidases, most of whose substrates are extracellular matrix (ECM) components, whereas ADAMTS-like proteins lack a metalloprotease domain, reside in the ECM and have regulatory roles vis-à-vis ECM assembly and/or ADAMTS activity. Evolutionary conservation and expansion of ADAMTS proteins in mammals is suggestive of crucial embryologic or physiological roles in humans. Indeed, Mendelian disorders or birth defects resulting from naturally occurring ADAMTS2, ADAMTS3, ADAMTS10, ADAMTS13, ADAMTS17, ADAMTS20, ADAMTSL2 and ADAMTSL4 mutations as well as numerous phenotypes identified in genetically engineered mice have revealed ADAMTS participation in major biological pathways. Important roles have been identified in a few acquired conditions. ADAMTS5 is unequivocally implicated in pathogenesis of osteoarthritis via degradation of aggrecan, a major structural proteoglycan in cartilage. ADAMTS7 is strongly associated with coronary artery disease and promotes atherosclerosis. Autoantibodies to ADAMTS13 lead to a platelet coagulopathy, thrombotic thrombocytopenic purpura, which is similar to that resulting from ADAMTS13 mutations. ADAMTS proteins have numerous potential connections to other human disorders that were identified by genome-wide association studies. Here, we review inherited and acquired human disorders in which ADAMTS proteins participate, and discuss progress and prospects in therapeutics.

Neutral Theory: From Complex Population History to Natural Selection and Sociocultural Phenomena in Human Populations

Here, we present a synthetic view on how Kimura's Neutral theory has helped us gaining insight on the different evolutionary forces that shape human evolution. We put this perspective in the frame of recent emerging challenges: the use of whole genome data for reconstructing population histories, natural selection on complex polygenic traits, and integrating cultural processes in human evolution.

Similarity and variation in the insulin-like growth factor 2 - H19 locus in primates

Insulin-like growth factor 2 (IGF2), a small, secreted protein, is critical for fetal and prenatal growth in humans and other mammals. The IGF2 gene and its mouse homolog comprise part of a conserved linkage group that is regulated by parental imprinting, with IGF2/ Igf2 being expressed from the paternal chromosome, and the adjacent H19 gene from the maternal chromosome. By using information extracted from public genomic and gene expression databases, I have now analyzed this locus in nine nonhuman primate species representing over 60 million years of evolutionary divergence from a common progenitor. Both IGF2 and H19 genes and the entire locus have been conserved among these primates. Each primate IGF2 gene except for gibbon and marmoset is composed of 10 exons and contains five potential promoters, each with distinctive 5'-untranslated exons. Similarly, except for marmoset and mouse lemur, H19 consists of six exons and has two promoters. DNA sequence conservation is high, not only in orthologous exons and promoters, but also in a putative imprinting control region located 5' to H19 and in multiple potential distal enhancer elements found 3' to H19. Collectively, these results support the hypothesis that common regulatory processes shaped the IGF2 - H19 locus before the onset of primate speciation more than 85 million years ago. This study also leads to the conclusion that inaccuracies in data presentation in genetic repositories could limit our ability to develop novel insights about roles of individual genes and multigene loci in mammalian physiology and disease.

Full genetic analysis for genome-wide association study of Fangji: a powerful approach for effectively dissecting the molecular architecture of personalized traditional Chinese medicine

Elucidation of the systems biology foundation underlying the effect of Fangji, which are multi-herbal traditional Chinese medicine (TCM) formulas, is one of the major aims in the field. The numerous bioactive ingredients of a Fangji deal with the multiple targets of a complex disease, which is influenced by a number of genes and their interactions with the environment. Genome-wide association study (GWAS) is an unbiased approach for dissecting the genetic variants underlying complex diseases and individual response to a given treatment. GWAS has great potential for the study of systems biology from the point of view of genomics, but the capacity using current analysis models is largely handicapped, as evidenced by missing heritability. Recent development of a full genetic model, in which gene-gene interactions (dominance and epistasis) and gene-environment interactions are all considered, has addressed these problems. This approach has been demonstrated to substantially increase model power, remarkably improving the detection of association of GWAS and the construction of the molecular architecture. This analysis does not require a very large sample size, which is often difficult to meet for a GWAS of treatment response. Furthermore, this analysis can integrate other omic information and allow for variations of Fangji, which is very promising for Fangjiomic study and detection of the sophisticated molecular architecture of the function of Fangji, as well as for the delineation of the systems biology of personalized medicine in TCM in an unbiased and comprehensive manner.

PAPPA2 as a Therapeutic Modulator of IGF-I Bioavailability: in Vivo and in Vitro Evidence

Context

Pregnancy-associated plasma protein A2 (PAPPA2) is a protease that cleaves IGF-binding protein (IGFBP)-3 and IGFBP-5, liberating free IGF-I. Five patients from two families with genetic mutations in PAPPA2 presented with growth retardation, elevated total IGF-I, and IGFBP-3 but decreased free IGF-I.

Objective

To determine whether plasma transfusion or recombinant human (rh)PAPPA2 could increase free IGF-I in patients with PAPPA2 deficiency or idiopathic short stature (ISS).

Design

Single patient interventional study combined with in vitro experimentation.

Setting

Academic medical center.

Patients

Three siblings with PAPPA2 deficiency and four patients with ISS.

Interventions

An adult female with PAPPA2 deficiency received a 20 mL/kg plasma transfusion. PAPPA2, intact IGFBP-3, and free and total IGF-I levels were monitored during 2 weeks. rhPAPPA2 was added to serum from patients with PAPPA2 deficiency and ISS in vitro for 4 hours. Intact IGFBP-3 and free IGF-I levels were assayed via ELISA.

Main Outcome Measures

Free IGF-I concentrations.

Results

Plasma transfusion resulted in a 2.5-fold increase of free IGF-I levels on day 1 posttransfusion with a return to baseline during a 2-week period. In vitro studies demonstrated a dose-dependent increase in free IGF-I and decrease in intact IGFBP-3 after the addition of rhPAPPA2. The increase in free IGF-I was more pronounced in patients with PAPPA2 deficiency compared with those with ISS.

Conclusions

PAPPA2 plays a key role in regulation of IGF-I bioavailability. rhPAPPA2 is a promising therapy to increase free IGF-I levels both in patients with PAPPA2 deficiency as well as in patients with ISS.

Evaluation and application of summary statistic imputation to discover new height-associated loci

As most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as summary statistics imputation, which we improved to accommodate variable sample size across SNVs. Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, genotype imputation boasts a 3- to 5-fold lower root-mean-square error, and better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded a decrease in statistical power by 9, 43 and 35%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian randomisation or LD-score regression.

Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease

Less than 3% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In >400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency < 5%) pLOF variant-phenotype associations. pLOF variants in the gene GPR151 protect against obesity and type 2 diabetes, in the gene IL33 against asthma and allergic disease, and in the gene IFIH1 against hypothyroidism. In the gene PDE3B, pLOF variants associate with elevated height, improved body fat distribution and protection from coronary artery disease. Our findings prioritize genes for which pharmacologic mimics of pLOF variants may lower risk for disease.

First High-Density Linkage Map and Single Nucleotide Polymorphisms Significantly Associated With Traits of Economic Importance in Yellowtail Kingfish Seriola lalandi

The genetic resources available for the commercially important fish species Yellowtail kingfish (YTK) (Seriola lalandi) are relative sparse. To overcome this, we aimed (1) to develop a linkage map for this species, and (2) to identify markers/variants associated with economically important traits in kingfish (with an emphasis on body weight). Genetic and genomic analyses were conducted using 13,898 single nucleotide polymorphisms (SNPs) generated from a new high-throughput genotyping by sequencing platform, Diversity Arrays Technology (DArTseq^TM) in a pedigreed population comprising 752 animals. The linkage analysis enabled to map about 4,000 markers to 24 linkage groups (LGs), with an average density of 3.4 SNPs per cM. The linkage map was integrated into a genome-wide association study (GWAS) and identified six variants/SNPs associated with body weight (P < 5e^-8) when a multi-locus mixed model was used. Two out of the six significant markers were mapped to LGs 17 and 23, and collectively they explained 5.8% of the total genetic variance. It is concluded that the newly developed linkage map and the significantly associated markers with body weight provide fundamental information to characterize genetic architecture of growth-related traits in this population of YTK S. lalandi.

Signatures of negative selection in the genetic architecture of human complex traits

We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.

Precision medicine screening using whole-genome sequencing and advanced imaging to identify disease risk in adults

Advances in technology are enabling evaluation for prevention and early detection of age-related chronic diseases associated with premature mortality, such as cancer and cardiovascular diseases. These diseases kill about one-third of men and one-quarter of women between the ages of 50 and 74 years old in the United States. We used whole-genome sequencing, advanced imaging, and other clinical testing to screen 209 active, symptom-free adults. We identified a broad set of complementary age-related chronic disease risks associated with premature mortality.

Reducing premature mortality associated with age-related chronic diseases, such as cancer and cardiovascular disease, is an urgent priority. We report early results using genomics in combination with advanced imaging and other clinical testing to proactively screen for age-related chronic disease risk among adults. We enrolled active, symptom-free adults in a study of screening for age-related chronic diseases associated with premature mortality. In addition to personal and family medical history and other clinical testing, we obtained whole-genome sequencing (WGS), noncontrast whole-body MRI, dual-energy X-ray absorptiometry (DXA), global metabolomics, a new blood test for prediabetes (Quantose IR), echocardiography (ECHO), ECG, and cardiac rhythm monitoring to identify age-related chronic disease risks. Precision medicine screening using WGS and advanced imaging along with other testing among active, symptom-free adults identified a broad set of complementary age-related chronic disease risks associated with premature mortality and strengthened WGS variant interpretation. This and other similarly designed screening approaches anchored by WGS and advanced imaging may have the potential to extend healthy life among active adults through improved prevention and early detection of age-related chronic diseases (and their risk factors) associated with premature mortality.

Phenotype risk scores identify patients with unrecognized Mendelian disease patterns

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and may identify subsets of patients with distinct genetic causes for common diseases.

Multiple genetic variations confer risks for obesity and type 2 diabetes mellitus in arab descendants from UAE

BACKGROUND

The United Arab Emirates (UAE) is one of the countries most threatened with obesity. Here we investigated associations between hundreds of single-nucleotide polymorphisms (SNPs) and the following obesity indicators: body mass index (BMI), waist circumference (WC), and height. We also investigated the associations between obesity-related genes with type 2 diabetes mellitus (T2DM).

METHODS

We tested 87, 58, and 586 SNPs in a previous genome-wide significance level for associations with BMI (n = 880), WC (n = 455), and height (n = 897), respectively. For each trait, we used normally transformed Z scores and tested them with SNPs using linear regression models that incorporated age and gender as covariates. The weighted polygenic risk scores for significant SNPs for each trait were tested with the corresponding Z scores using linear regression models with the same covariates. We further tested 145 obesity loci with T2DM (464 cases, 415 controls) using a logistic regression model including age, gender, and BMI Z scores as covariates.

RESULTS

The Mean BMI was 29.39 kg/m², and mean WC was 103.66 cm. Hypertension and dyslipidemia were common obesity comorbidities (>60%). The best associations for BMI was in FTO, LOC284260 and USP37, and for WC in RFX7 and MYEOV. For height, the best association was in NSD1 followed by MFAP2 and seven other loci. The polygenic scores revealed stronger associations for each trait than individual SNPs; although they could only explain <1% of the traits' Z scores variations. For T2DM, the strongest associations were with the TCF7L2 and MC4R loci (P < 0.01, OR ~1.70), with novel associations detected with KCNK3 and RARB.

CONCLUSIONS

In this first study of Arab descendants, we confirmed several known obesity (FTO, USP37, and RFX7), height (NSD1, MFAP2), and T2DM (TCF7L2, MC4R) associations; and report novel associations, like KCNK3 and RARB for T2DM.

The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development

Drug development continues to be costly and slow, with medications failing due to lack of efficacy or presence of toxicity. The promise of pharmacogenomic discovery includes tailoring therapeutics based on an individual's genetic makeup, rational drug development, and repurposing medications. Rapid growth of large research cohorts, linked to electronic health record (EHR) data, fuels discovery of new genetic variants predicting drug action, supports Mendelian randomization experiments to show drug efficacy, and suggests new indications for existing medications. New biomedical informatics and machine-learning approaches advance the ability to interpret clinical information, enabling identification of complex phenotypes and subpopulations of patients. We review the recent history of use of "big data" from EHR-based cohorts and biobanks supporting these activities. Future studies using EHR data, other information sources, and new methods will promote a foundation for discovery to more rapidly advance precision medicine.

Drug development in the era of precision medicine

For the past three decades, the use of genomics to inform drug discovery and development pipelines has generated both excitement and scepticism. Although earlier efforts successfully identified some new drug targets, the overall clinical efficacy of developed drugs has remained unimpressive, owing in large part to the heterogeneous causes of disease. Recent technological and analytical advances in genomics, however, have now made it possible to rapidly identify and interpret the genetic variation underlying a single patient's disease, thereby providing a window into patient-specific mechanisms that cause or contribute to disease, which could ultimately enable the 'precise' targeting of these mechanisms. Here, we first examine and highlight the successes and limitations of the earlier phases of genomics in drug discovery and development. We then review the current major efforts in precision medicine and discuss the potential broader utility of mechanistically guided treatments going forward.

Genetic short stature

Adult height and growth patterns are largely genetically programmed. Studies in twins have indicated that the heritability of height is high (>80%), suggesting that genetic variation is the main determinant of stature. Height exhibits a normal (Gaussian) distribution according to sex, age, and ancestry. Short stature is usually defined as a height which is 2 standard deviations (S.D.) less than the mean height of a specific population. This definition includes 2.3% of the population and usually includes healthy individuals. In this group of short stature non-syndromic conditions, the genetic influence occurs polygenically or oligogenically. As a rule, each common genetic variant accounts for a small effect (1mm) on individual height variation. Recently, several studies demonstrated that some rare variants can cause greater effect on height, without causing a syndromic condition. In more extreme cases, height SDS below 2.5 or 3 (which would comprise approximately 0.6 and 0.1% of the population, respectively) is frequently associated with syndromic conditions and are usually caused by a monogenic defect. More than 1,000 inherited/genetic diseases have growth disorder as an important phenotype. These conditions are usually responsible for syndromic short stature. In the coming years, we expect to discover several genetic causes of short stature, thereby explaining the phenotype of what we currently classify as short stature of unknown cause. These discoveries will have a profound impact on the follow-up and treatment of these children.

New genetic tools in the diagnosis of growth defects

Growth is a complex biological process governed by thousands of genes. Genetic defects in a wide array of genes can cause severe growth disorders. Genomic technologies including chromosomal microarrays and whole exome sequencing have revolutionized our ability to diagnose growth disorders. In this brief review, we will discuss each of these technologies and how they have been applied in the field of growth disorders.

IHH Gene Mutations Causing Short Stature With Nonspecific Skeletal Abnormalities and Response to Growth Hormone Therapy

CONTEXT

Genetic evaluation has been recognized as an important tool to elucidate the causes of growth disorders.

OBJECTIVE

To investigate the cause of short stature and to determine the phenotype of patients with IHH mutations, including the response to recombinant human growth hormone (rhGH) therapy.

PATIENTS AND METHODS

We studied 17 families with autosomal-dominant short stature by using whole exome sequencing and screened IHH defects in 290 patients with growth disorders. Molecular analyses were performed to evaluate the potential impact of N-terminal IHH variants.

RESULTS

We identified 10 pathogenic or possibly pathogenic variants in IHH, an important regulator of endochondral ossification. Molecular analyses revealed a smaller potential energy of mutated IHH molecules. The allele frequency of rare, predicted to be deleterious IHH variants found in short-stature samples (1.6%) was higher than that observed in two control cohorts (0.017% and 0.08%; P < 0.001). Identified IHH variants segregate with short stature in a dominant inheritance pattern. Affected individuals typically manifest mild disproportional short stature with a frequent finding of shortening of the middle phalanx of the fifth finger. None of them have classic features of brachydactyly type A1, which was previously associated with IHH mutations. Five patients heterozygous for IHH variants had a good response to rhGH therapy. The mean change in height standard deviation score in 1 year was 0.6.

CONCLUSION

Our study demonstrated the association of pathogenic variants in IHH with short stature with nonspecific skeletal abnormalities and established a frequent cause of growth disorder, with a preliminary good response to rhGH.

Genetic architecture: the shape of the genetic contribution to human traits and disease

Genetic architecture describes the characteristics of genetic variation that are responsible for heritable phenotypic variability. It depends on the number of genetic variants affecting a trait, their frequencies in the population, the magnitude of their effects and their interactions with each other and the environment. Defining the genetic architecture of a complex trait or disease is central to the scientific and clinical goals of human genetics, which are to understand disease aetiology and aid in disease screening, diagnosis, prognosis and therapy. Recent technological advances have enabled genome-wide association studies and emerging next-generation sequencing studies to begin to decipher the nature of the heritable contribution to traits and disease. Here, we describe the types of genetic architecture that have been observed, how architecture can be measured and why an improved understanding of genetic architecture is central to future advances in the field.

What Happened to the IGF Binding Proteins?

Insulinlike growth factor (IGF) binding proteins (IGFBPs) 1 to 6 are high-affinity regulators of IGF activity. They generally inhibit IGF actions by preventing binding to the IGF-I receptor but can also enhance their actions under some conditions. Posttranslational modifications such as glycosylation and phosphorylation modulate IGFBP properties, and IGFBP proteolysis results in IGF release. IGFBPs have more recently been shown to have IGF-independent actions. A number of mechanisms are involved, including modulation of other growth factor pathways, nuclear localization and transcriptional regulation, interaction with the sphingolipid pathway, and binding to non-IGF biomolecules in the extracellular space and matrix, on the cell surface and intracellularly. IGFBPs modulate important biological processes, including cell proliferation, survival, migration, senescence, autophagy, and angiogenesis. Their actions have been implicated in growth, metabolism, cancer, stem cell maintenance and differentiation, and immune regulation. Recent studies have shown that epigenetic mechanisms are involved in the regulation of IGFBP abundance. A more complete understanding of IGFBP biology is necessary to further define their cellular roles and determine their therapeutic potential.

De novo mutations implicate novel genes in systemic lupus erythematosus

The omnigenic model of complex disease stipulates that the majority of the heritability will be explained by the effects of common variation on genes in the periphery of core disease pathways. Rare variant associations, expected to explain far less of the heritability, may be enriched in core disease genes and thus will be instrumental in the understanding of complex disease pathogenesis and their potential therapeutic targets. Here, using complementary whole-exome sequencing, high-density imputation, and in vitro cellular assays, we identify candidate core genes in the pathogenesis of systemic lupus erythematosus (SLE). Using extreme-phenotype sampling, we sequenced the exomes of 30 SLE parent-affected-offspring trios and identified 14 genes with missense de novo mutations (DNM), none of which are within the >80 SLE susceptibility loci implicated through genome-wide association studies. In a follow-up cohort of 10, 995 individuals of matched European ancestry, we imputed genotype data to the density of the combined UK10K-1000 genomes Phase III reference panel across the 14 candidate genes. Gene-level analyses indicate three functional candidates: DNMT3A, PRKCD, and C1QTNF4. We identify a burden of rare variants across PRKCD associated with SLE risk (P = 0.0028), and across DNMT3A associated with two severe disease prognosis sub-phenotypes (P = 0.0005 and P = 0.0033). We further characterise the TNF-dependent functions of the third candidate gene C1QTNF4 on NF-κB activation and apoptosis, which are inhibited by the p.His198Gln DNM. Our results identify three novel genes in SLE susceptibility and support extreme-phenotype sampling and DNM gene discovery to aid the search for core disease genes implicated through rare variation.

Genome-wide association studies in Crohn's disease: Past, present and future

Over the course of the past decade, genome-wide association studies (GWAS) have revolutionised our understanding of complex disease genetics. One of the diseases that has benefitted most from this technology has been Crohn's disease (CD), with the identification of autophagy, the IL-17/IL-23 axis and innate lymphoid cells as key players in CD pathogenesis. Our increasing understanding of the genetic architecture of CD has also highlighted how a failure to suppress aberrant immune responses may contribute to disease development - a realisation that is now being incorporated into the design of new treatments. However, despite these successes, a significant proportion of disease heritability remains unexplained. Similarly, most of the causal variants at associated loci have not yet been identified, and even fewer have been functionally characterised. Because of the inarguable rise in the incidence of CD in regions of the world that previously had low disease rates, GWAS studies will soon have to shift from a largely Caucasian focus to include populations from other ethnic backgrounds. Future studies should also move beyond conventional studies of disease susceptibility into phenotypically driven 'within-cases' analyses in order to explore the role of genetics in other important aspects of disease biology. These studies are likely to include assessments of prognosis and/or response to treatments and may be critical if personalised medicine is ever to become a reality.

Regulation of plasma von Willebrand factor

Von Willebrand factor (VWF) is a multimeric plasma glycoprotein that plays a central role in the initiation of blood coagulation. Through interactions between its specific functional domains, the vascular wall, coagulation factor VIII, and platelet receptors, VWF maintains hemostasis by binding to platelets and delivering factor VIII to the sites of vascular injury. In the healthy human population, plasma VWF levels vary widely. The important role of VWF is illustrated by individuals at the extremes of the normal distribution of plasma VWF concentrations where individuals with low VWF levels are more likely to present with mucocutaneous bleeding. Conversely, people with high VWF levels are at higher risk for venous thromboembolic disease, stroke, and coronary artery disease. This report will summarize recent advances in our understanding of environmental influences and the genetic control of VWF plasma variation in healthy and symptomatic populations and will also highlight the unanswered questions that are currently driving this field of study.

Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice

The rich genetic diversity in Oryza sativa and Oryza rufipogon serves as the main sources in rice breeding. Large-scale resequencing has been undertaken to discover allelic variants in rice, but much of the information for genetic variation is often lost by direct mapping of short sequence reads onto the O. sativa japonica Nipponbare reference genome. Here we constructed a pan-genome dataset of the O. sativa-O. rufipogon species complex through deep sequencing and de novo assembly of 66 divergent accessions. Intergenomic comparisons identified 23 million sequence variants in the rice genome. This catalog of sequence variations includes many known quantitative trait nucleotides and will be helpful in pinpointing new causal variants that underlie complex traits. In particular, we systemically investigated the whole set of coding genes using this pan-genome data, which revealed extensive presence and absence of variation among rice accessions. This pan-genome resource will further promote evolutionary and functional studies in rice.

Reference quality assembly of the 3.5-Gb genome of Capsicum annuum from a single linked-read library

Linked-Read sequencing technology has recently been employed successfully for de novo assembly of human genomes, however, the utility of this technology for complex plant genomes is unproven. We evaluated the technology for this purpose by sequencing the 3.5-gigabase (Gb) diploid pepper (Capsicum annuum) genome with a single Linked-Read library. Plant genomes, including pepper, are characterized by long, highly similar repetitive sequences. Accordingly, significant effort is used to ensure that the sequenced plant is highly homozygous and the resulting assembly is a haploid consensus. With a phased assembly approach, we targeted a heterozygous F₁ derived from a wide cross to assess the ability to derive both haplotypes and characterize a pungency gene with a large insertion/deletion. The Supernova software generated a highly ordered, more contiguous sequence assembly than all currently available C. annuum reference genomes. Over 83% of the final assembly was anchored and oriented using four publicly available de novo linkage maps. A comparison of the annotation of conserved eukaryotic genes indicated the completeness of assembly. The validity of the phased assembly is further demonstrated with the complete recovery of both 2.5-Kb insertion/deletion haplotypes of the PUN1 locus in the F₁ sample that represents pungent and nonpungent peppers, as well as nearly full recovery of the BUSCO2 gene set within each of the two haplotypes. The most contiguous pepper genome assembly to date has been generated which demonstrates that Linked-Read library technology provides a tool to de novo assemble complex highly repetitive heterozygous plant genomes. This technology can provide an opportunity to cost-effectively develop high-quality genome assemblies for other complex plants and compare structural and gene differences through accurate haplotype reconstruction.

Insulin- like Growth Factor-Binding Protein Action in Bone Tissue: A Key Role for Pregnancy- Associated Plasma Protein-A

The insulin-like growth factor (IGF) axis is required for the differentiation, development, and maintenance of bone tissue. Accordingly, dysregulation of this axis is associated with various skeletal pathologies including growth abnormalities and compromised bone structure. It is becoming increasingly apparent that the action of the IGF axis must be viewed holistically taking into account not just the actions of the growth factors and receptors, but also the influence of soluble high affinity IGF binding proteins (IGFBPs).There is a recognition that IGFBPs exert IGF-dependent and IGF-independent effects in bone and other tissues and that an understanding of the mechanisms of action of IGFBPs and their regulation in the pericellular environment impact critically on tissue physiology. In this respect, a group of IGFBP proteinases (which may be considered as ancillary members of the IGF axis) play a crucial role in regulating IGFBP function. In this model, cleavage of IGFBPs by specific proteinases into fragments with lower affinity for growth factor(s) regulates the partition of IGFs between IGFBPs and cell surface IGF receptors. In this review, we examine the importance of IGFBP function in bone tissue with special emphasis on the role of pregnancy associated plasma protein-A (PAPP-A). We examine the function of PAPP-A primarily as an IGFBP-4 proteinase and present evidence that PAPP-A induced cleavage of IGFBP-4 is potentially a key regulatory step in bone metabolism. We also highlight some recent findings with regard to IGFBP-2 and IGFBP-5 (also PAPP-A substrates) function in bone tissue and briefly discuss the actions of the other three IGFBPs (-1, -3, and -6) in this tissue. Although our main focus will be in bone we will allude to IGFBP activity in other cells and tissues where appropriate.

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Psychiatric Genomics: An Update and an Agenda

The Psychiatric Genomics Consortium (PGC) is the largest consortium in the history of psychiatry. This global effort is dedicated to rapid progress and open science, and in the past decade it has delivered an increasing flow of new knowledge about the fundamental basis of common psychiatric disorders. The PGC has recently commenced a program of research designed to deliver "actionable" findings-genomic results that 1) reveal fundamental biology, 2) inform clinical practice, and 3) deliver new therapeutic targets. The central idea of the PGC is to convert the family history risk factor into biologically, clinically, and therapeutically meaningful insights. The emerging findings suggest that we are entering a phase of accelerated genetic discovery for multiple psychiatric disorders. These findings are likely to elucidate the genetic portions of these truly complex traits, and this knowledge can then be mined for its relevance for improved therapeutics and its impact on psychiatric practice within a precision medicine framework. [AJP at 175: Remembering Our Past As We Envision Our Future November 1946: The Genetic Theory of Schizophrenia Franz Kallmann's influential twin study of schizophrenia in 691 twin pairs was the largest in the field for nearly four decades. (Am J Psychiatry 1946; 103:309-322 )].

Novel Modulators of the Growth Hormone - Insulin-Like Growth Factor Axis: Pregnancy-Associated Plasma Protein-A2 and Stanniocalcin-2

Growth hormone (GH) and its mediator, insulin-like growth factor-1 (IGF-1), play a critical role in human growth. In circulation, IGF-1 is found in a ternary complex with IGF binding proteins (IGFBPs) and acid labile subunit (ALS) but little attention has been paid to the regulation of IGF-1 bioavailability. Recently, pregnancy-associated plasma protein-A2 (PAPP-A2) and stanniocalcin-2 (STC2) were identified as novel modulators of IGF-I bioavailability. PAPP-A2 is a protease which cleaves IGFBP-3 and -5, while STC2 inhibits PAPP-A and PAPP-A2 activity. In collaboration with a group in Madrid, we reported the first human cases carrying mutations in the PAPPA2 gene who presented with short stature, elevated total IGF-1, IGFBP-3, IGFBP-5 and ALS, but low free IGF-1. Additionally, the patients demonstrated insulin resistance and below average bone mineral density (BMD). The PAPP-A2 deficient patients were treated with recombinant human IGF-1, resulting in improvements in growth velocity, insulin resistance, and BMD. These findings suggested that the bioactive, free IGF-1 liberated from IGFBPs by PAPP-A2 is important for human growth. Mouse models of PAPP-A2 and STC2 provide further insights into their roles in growth physiology. This review will summarize new insights into PAPP-A2 and STC2 and their role in the GH-IGF axis, thereby highlighting the importance of the regulation of IGF-1 bioavailability in human health and disease.

From forensic epigenetics to forensic epigenomics: broadening DNA investigative intelligence

Human genetic variation is a major resource in forensics, but does not allow all forensically relevant questions to be answered. Some questions may instead be addressable via epigenomics, as the epigenome acts as an interphase between the fixed genome and the dynamic environment. We envision future forensic applications of DNA methylation analysis that will broaden DNA-based forensic intelligence. Together with genetic prediction of appearance and biogeographic ancestry, epigenomic lifestyle prediction is expected to increase the ability of police to find unknown perpetrators of crime who are not identifiable using current forensic DNA profiling.

Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers

Multiple omic profiles have been generated for many cancer types; however, comprehensive assessment of their prognostic values across cancers is limited. We conducted a pan-cancer prognostic assessment and presented a multi-omic kernel machine learning method to systematically quantify the prognostic values of high-throughput genomic, epigenomic, and transcriptomic profiles individually, integratively, and in combination with clinical factors for 3,382 samples across 14 cancer types. We found that the prognostic performance varied substantially across cancer types. mRNA and miRNA expression profile frequently performed the best, followed by DNA methylation profile. Germline susceptibility variants displayed low prognostic performance consistently across cancer types. The integration of omic profiles with clinical variables can lead to substantially improved prognostic performance over the use of clinical variables alone in half of cancer types examined. Moreover, we showed that the kernel machine learning method consistently outperformed existing prognostic signatures, suggesting that including a large number of omic biomarkers may provide substantial improvement in prognostic assessment. Our study provides a comprehensive portrait of omic architecture for tumor prognosis across cancers, and highlights the prognostic value of genome-wide omic biomarker aggregation, which may facilitate refined prognostic assessment in the era of precision oncology.

Polygenicity and Epistasis Underlie Fitness-Proximal Traits in the Caenorhabditis elegans Multiparental Experimental Evolution (CeMEE) Panel

Understanding the genetic basis of complex traits remains a major challenge in biology. Polygenicity, phenotypic plasticity, and epistasis contribute to phenotypic variance in ways that are rarely clear. This uncertainty can be problematic for estimating heritability, for predicting individual phenotypes from genomic data, and for parameterizing models of phenotypic evolution. Here, we report an advanced recombinant inbred line (RIL) quantitative trait locus mapping panel for the hermaphroditic nematode Caenorhabditis elegans, the C. elegans multiparental experimental evolution (CeMEE) panel. The CeMEE panel, comprising 507 RILs at present, was created by hybridization of 16 wild isolates, experimental evolution for 140-190 generations, and inbreeding by selfing for 13-16 generations. The panel contains 22% of single-nucleotide polymorphisms known to segregate in natural populations, and complements existing C. elegans mapping resources by providing fine resolution and high nucleotide diversity across > 95% of the genome. We apply it to study the genetic basis of two fitness components, fertility and hermaphrodite body size at time of reproduction, with high broad-sense heritability in the CeMEE. While simulations show that we should detect common alleles with additive effects as small as 5%, at gene-level resolution, the genetic architectures of these traits do not feature such alleles. We instead find that a significant fraction of trait variance, approaching 40% for fertility, can be explained by sign epistasis with main effects below the detection limit. In congruence, phenotype prediction from genomic similarity, while generally poor ([Formula: see text]), requires modeling epistasis for optimal accuracy, with most variance attributed to the rapidly evolving chromosome arms.

Clinical relevance of systematic phenotyping and exome sequencing in patients with short stature

Purpose

Short stature is a common condition of great concern to patients and their families. Mostly genetic in origin, the underlying cause often remains elusive due to clinical and genetic heterogeneity.

Methods

We systematically phenotyped 565 patients where common nongenetic causes of short stature were excluded, selected 200 representative patients for whole-exome sequencing, and analyzed the identified variants for pathogenicity and the affected genes regarding their functional relevance for growth.

Results

By standard targeted diagnostic and phenotype assessment, we identified a known disease cause in only 13.6% of the 565 patients. Whole-exome sequencing in 200 patients identified additional mutations in known short-stature genes in 16.5% of these patients who manifested only part of the symptomatology. In 15.5% of the 200 patients our findings were of significant clinical relevance. Heterozygous carriers of recessive skeletal dysplasia alleles represented 3.5% of the cases.

Conclusion

A combined approach of systematic phenotyping, targeted genetic testing, and whole-exome sequencing allows the identification of the underlying cause of short stature in at least 33% of cases, enabling physicians to improve diagnosis, treatment, and genetic counseling. Exome sequencing significantly increases the diagnostic yield and consequently care in patients with short stature.

[Common and rare variants, polygenic traits and missing heritability]

Recently, a systematic (but limited) search for rare variants implicated in adult height, a highly polygenic trait, has uncovered a number of new variants for which the effect size is inversely correlated with the minor allele frequency. This opens interesting perspectives on the genetic architecture of complex traits and on the vexing problem of "missing heritability".

Convergence between biological, behavioural and genetic determinants of obesity

Multiple biological, behavioural and genetic determinants or correlates of obesity have been identified to date. Genome-wide association studies (GWAS) have contributed to the identification of more than 100 obesity-associated genetic variants, but their roles in causal processes leading to obesity remain largely unknown. Most variants are likely to have tissue-specific regulatory roles through joint contributions to biological pathways and networks, through changes in gene expression that influence quantitative traits, or through the regulation of the epigenome. The recent availability of large-scale functional genomics resources provides an opportunity to re-examine obesity GWAS data to begin elucidating the function of genetic variants. Interrogation of knockout mouse phenotype resources provides a further avenue to test for evidence of convergence between genetic variation and biological or behavioural determinants of obesity.

Commentary: Fisher's infinitesimal model: A story for the ages

Mendel (1866) suggested that if many heritable "factors" contribute to a trait, near-continuous variation could result. Fisher (1918) clarified the connection between Mendelian inheritance and continuous trait variation by assuming many loci, each with small effect, and by informally invoking the central limit theorem. Barton et al. (2017) rigorously analyze the approach to a multivariate Gaussian distribution of the genetic effects for descendants of parents who may be related. This commentary distinguishes three nested approximations, referred to as "infinitesimal genetics," "Gaussian descendants" and "Gaussian population," each plausibly called "the infinitesimal model." The first and most basic is Fisher's "infinitesimal" approximation of the underlying genetics - namely, many loci, each making a small contribution to the total variance. As Barton et al. (2017) show, in the limit as the number of loci increases (with enough additivity), the distribution of genotypic values for descendants approaches a multivariate Gaussian, whose variance-covariance structure depends only on the relatedness, not the phenotypes, of the parents (or whether their population experiences selection or other processes such as mutation and migration). Barton et al. (2017) call this rigorously defensible "Gaussian descendants" approximation "the infinitesimal model." However, it is widely assumed that Fisher's genetic assumptions yield another Gaussian approximation, in which the distribution of breeding values in a population follows a Gaussian - even if the population is subject to non-Gaussian selection. This third "Gaussian population" approximation, is also described as the "infinitesimal model." Unlike the "Gaussian descendants" approximation, this third approximation cannot be rigorously justified, except in a weak-selection limit, even for a purely additive model. Nevertheless, it underlies the two most widely used descriptions of selection-induced changes in trait means and genetic variances, the "breeder's equation" and the "Bulmer effect." Future generations may understand why the "infinitesimal model" provides such useful approximations in the face of epistasis, linkage, linkage disequilibrium and strong selection.

One level up: abnormal proteolytic regulation of IGF activity plays a role in human pathophysiology

The discovery of a mutation in a specific gene can be very important for determining the pathophysiology underlying the disease of a patient and may also help to decide the best treatment protocol on an individual basis. However, sometimes the discovery of mutations in new proteins advances our comprehension in a more widespread manner. The growth hormone (GH)/insulin-like growth factor (IGF)-1 axis is fundamental for systemic growth, but is also involved in many other important processes. Our understanding of this system in physiology and pathophysiology has advanced throughout the years with each discovery of mutations in members of this axis. This review focuses on the most recent discovery: mutations in the metalloproteinase pregnancy-associated plasma protein-A2 (PAPP-A2), one of the proteases involved in liberating IGF-1 from the complexes in which it circulates, in patients with delayed growth failure. We also discuss the advances in the stanniocalcins (STC1 and STC2), proteins that modulate PAPP-A2, as well as PAPP-A. These new advances not only bring us one step closer to understanding the strict spatial and temporal control of this axis in systemic growth and maturation, but also highlight possible therapeutic targets when this system goes awry.

CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits

There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01–0.2%), with large effects on height (>2.4 cm), weight (>5 kg), and body mass index (BMI) (>3.5 kg/m²). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m² for each Mb of total deletion burden (P = 2.5 × 10⁻¹⁰, 6.0 × 10⁻⁵, and 2.9 × 10⁻³). Our study provides evidence that the same genes (e.g., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.

Individual SNPs have small effects on anthropometric traits, yet the impact of CNVs has remained largely unknown. Here, Kutalik and co-workers perform a large-scale genome-wide meta-analysis of structural variation and find rare CNVs associated with height, weight and BMI with large effect sizes.

Concepts, estimation and interpretation of SNP-based heritability

Narrow-sense heritability (h²) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all causal variants. Estimation of h² previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability (). In this Perspective, we discuss recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of , definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results.

Detecting gene subnetworks under selection in biological pathways

Advances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, detecting selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. We developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach, implemented in the R package signet, improves on gene-level classical tests for selection by identifying both new candidate genes and new biological processes involved in adaptation to altitude.