Rare and low-frequency coding variants alter human adult height

ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies

Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.

Disease Heritability Studies Harness the Healthcare System to Achieve Massive Scale

Heritability studies are essential for defining genetic influences on disease risk and trait variability. Polubriaginof et al. show how massive amounts of data contained in electronic health records can be used for heritability studies on hundreds of phenotypes. Mining emergency contact information with comparison to existing gold standards showcases the broad utility of the approach.

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.

Genetic regulation of linear growth

Linear growth occurs at the growth plate. Therefore, genetic defects that interfere with the normal function of the growth plate can cause linear growth disorders. Many genetic causes of growth disorders have already been identified in humans. However, recent genome-wide approaches have broadened our knowledge of the mechanisms of linear growth, not only providing novel monogenic causes of growth disorders but also revealing single nucleotide polymorphisms in genes that affect height in the general population. The genes identified as causative of linear growth disorders are heterogeneous, playing a role in various growth-regulating mechanisms including those involving the extracellular matrix, intracellular signaling, paracrine signaling, endocrine signaling, and epigenetic regulation. Understanding the underlying genetic defects in linear growth is important for clinicians and researchers in order to provide proper diagnoses, management, and genetic counseling, as well as to develop better treatment approaches for children with growth disorders.

Evolutionary conserved networks of human height identify multiple Mendelian causes of short stature

Height is a heritable and highly heterogeneous trait. Short stature affects 3% of the population and in most cases is genetic in origin. After excluding known causes, 67% of affected individuals remain without diagnosis. To identify novel candidate genes for short stature, we performed exome sequencing in 254 unrelated families with short stature of unknown cause and identified variants in 63 candidate genes in 92 (36%) independent families. Based on systematic characterization of variants and functional analysis including expression in chondrocytes, we classified 13 genes as strong candidates. Whereas variants in at least two families were detected for all 13 candidates, two genes had variants in 6 (UBR4) and 8 (LAMA5) families, respectively. To facilitate their characterization, we established a clustered network of 1025 known growth and short stature genes, which yielded 29 significantly enriched clusters, including skeletal system development, appendage development, metabolic processes, and ciliopathy. Eleven of the candidate genes mapped to 21 of these clusters, including CPZ, EDEM3, FBRS, IFT81, KCND1, PLXNA3, RASA3, SLC7A8, UBR4, USP45, and ZFHX3. Fifty additional growth-related candidates we identified await confirmation in other affected families. Our study identifies Mendelian forms of growth retardation as an important component of idiopathic short stature.

Growth hormone therapy in children; research and practice - A review

Short stature remains the most common reason for referral to a pediatric Endocrinologist and its management remains a challenge. One of the main controversies is the diagnosis of idiopathic short stature and the role of new technologies for genetic investigation of children with inadequate growth. Complexities in management of children with short stature includes selection of who should receive interventions such as recombinant human growth hormone, and how should this agent dose be adjusted during treatment. Should anthropometrical data be the primary determinant or should biochemical and genetic data be used to improve growth response and safety? Furthermore, what is considered a suboptimal response to growth hormone therapy and how should this be managed? Treatment of children with short stature remains a "hot" topic and more data is needed in several areas. These issues are reviewed in this paper.

Genetics of biologically based psychological differences

In recent years, substantial effort has gone into disentangling the genetic contribution to individual differences in behaviour (such as personality and temperament traits). Heritability estimates from twin and family studies, and more recently using whole genome approaches, suggest a substantial genetic component to these traits. However, efforts to identify the genes that influence these traits have had relatively little success. Here, we review current work investigating the heritability of individual differences in behavioural traits and provide an overview of the results from genome-wide association analyses of these traits to date. In addition, we discuss the implications of these findings for the potential applications of Mendelian randomization.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'.

Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer - but not other cancers - explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

The Monocarboxylate Transporter SLC16A6 Regulates Adult Length in Zebrafish and Is Associated With Height in Humans

When fasted as larvae or fed ketogenic diets as adults, homozygous zebrafish slc16a6a mutants develop hepatic steatosis because their livers cannot export the major ketone body β-hydroxybutyrate, diverting liver-trapped ketogenic carbon atoms to triacylglycerol. Here, we find that slc16a6a mutants are longer than their wild-type siblings. This effect is largely not sexually dimorphic, nor is it affected by dietary fat content on a pure genetic background. A mixed genetic background alters the proportionality of mass to length modestly. We also observe that non-coding variations in the 5′-untranslated region and first intron, and coding variations within the fifth exon of the orthologous human gene locus SLC16A6 are highly significantly associated with human height. Since both zebrafish and human orthologs of SLC16A6 are expressed in multiple locations, this gene likely regulates height through modulating transport of monocarboxylic acids in several tissues.

Deleterious Mutation Burden and Its Association with Complex Traits in Sorghum (Sorghum bicolor)

Sorghum (Sorghum bicolor (L.) Moench) is a major staple food cereal for millions of people worldwide. Valluru et al. identify putative deleterious mutations among ∼5.5M segregating variants of 229 diverse sorghum...

Sorghum (Sorghum bicolor L.) is a major food cereal for millions of people worldwide. The sorghum genome, like other species, accumulates deleterious mutations, likely impacting its fitness. The lack of recombination, drift, and the coupling with favorable loci impede the removal of deleterious mutations from the genome by selection. To study how deleterious variants impact phenotypes, we identified putative deleterious mutations among ∼5.5 M segregating variants of 229 diverse biomass sorghum lines. We provide the whole-genome estimate of the deleterious burden in sorghum, showing that ∼33% of nonsynonymous substitutions are putatively deleterious. The pattern of mutation burden varies appreciably among racial groups. Across racial groups, the mutation burden correlated negatively with biomass, plant height, specific leaf area (SLA), and tissue starch content (TSC), suggesting that deleterious burden decreases trait fitness. Putatively deleterious variants explain roughly one-half of the genetic variance. However, there is only moderate improvement in total heritable variance explained for biomass (7.6%) and plant height (average of 3.1% across all stages). There is no advantage in total heritable variance for SLA and TSC. The contribution of putatively deleterious variants to phenotypic diversity therefore appears to be dependent on the genetic architecture of traits. Overall, these results suggest that incorporating putatively deleterious variants into genomic models slightly improves prediction accuracy because of extensive linkage. Knowledge of deleterious variants could be leveraged for sorghum breeding through either genome editing and/or conventional breeding that focuses on the selection of progeny with fewer deleterious alleles.

Genetics of Growth Disorders-Which Patients Require Genetic Testing?

The second 360° European Meeting on Growth Hormone Disorders, held in Barcelona, Spain, in June 2017, included a session entitled Pragmatism vs. Curiosity in Genetic Diagnosis of Growth Disorders, which examined current concepts of genetics and growth in the clinical setting, in terms of both growth failure and overgrowth. For patients with short stature, multiple genes have been identified that result in GH deficiency, which may be isolated or associated with additional pituitary hormone deficiencies, or in growth hormone resistance, primary insulin-like growth factor (IGF) acid-labile subunit deficiency, IGF-I deficiency, IGF-II deficiency, IGF-I resistance, and primary PAPP-A2 deficiency. While genetic causes of short stature were previously thought to primarily be associated with the GH-IGF-I axis, it is now established that multiple genetic anomalies not associated with the GH-IGF-I axis can result in short stature. A number of genetic anomalies have also been shown to be associated with overgrowth, some of which involve the GH-IGF-I axis. In patients with overgrowth in combination with an intellectual disability, two predominant gene families, the epigenetic regulator genes, and PI3K/AKT pathway genes, have now been identified. Specific processes should be followed for decisions on which patients require genetic testing and which genes should be examined for anomalies. The decision to carry out genetic testing should be directed by the clinical process, not merely for research purposes. The intention of genetic testing should be to direct the clinical options for management of the growth disorder.

Unravelling the genetic loci for growth and carcass traits in Chinese Bamaxiang pigs based on a 1.4 million SNP array

Bamaxiang pig is from Guangxi province in China, characterized by its small body size and two-end black coat colour. It is an important indigenous breed for local pork market and excellent animal model for biomedical research. In this study, we performed genomewide association studies (GWAS) on 43 growth and carcass traits in 315 purebred Bamaxiang pigs based on a 1.4 million SNP array. We observed considerable phenotypic variability in the growth and carcass traits in the Bamaxiang pigs. The corresponding SNP based heritability varied greatly across the 43 traits and ranged from 9.0% to 88%. Through a conditional GWAS, we identified 53 significant associations for 35 traits at p value threshold of 10^-6 . Among which, 26 associations on chromosome 3, 7, 14 and X passed a genomewide significance threshold of 5 × 10^-8 . The most remarkable loci were at around 30.6 Mb on chromosome 7, which had growth stage-dependent effects on body lengths and cannon circumferences and showed large effects on multiple carcass traits. We discussed HMGA1 NUDT3, EIF2AK1, TMEM132C and AFF2 that near the lead SNP of significant loci as plausible candidate genes for corresponding traits. We also showed that including phenotypic covariate in GWAS can help to reveal additional significant loci for the target traits. The results provide insight into the genetic architecture of growth and carcass traits in Bamaxiang pigs.

The insulin-like growth factor-1 system in the adult mammalian brain and its implications in central maternal adaptation

Our knowledge on the bioavailability and actions of insulin-like growth factor-1 (IGF-1) has markedly expanded in recent years as novel mechanisms were discovered on IGF binding proteins (IGFBPs) and their ability to release IGF-1. The new discoveries allowed a better understanding of the endogenous physiological actions of IGF-1 and also its applicability in therapeutics. The focus of the present review is to summarize novel findings on the neuronal, neuroendocrine and neuroplastic actions of IGF-1 in the adult brain. As most of the new regulatory mechanisms were described in the periphery, their implications on brain IGF system will also be covered. In addition, novel findings on the effects of IGF-1 on lactation and maternal behavior are described. Based on the enormous neuroplastic changes related to the peripartum period, IGF-1 has great but largely unexplored potential in maternal adaptation of the brain, which is highlighted in the present review.

Mutations in MERTK are not associated with age-related macular degeneration

PURPOSE

To assess whether mutations in Mer tyrosine kinase (MERTK) are associated with age-related macular degeneration (AMD).

METHODS

An association study using whole-genome sequencing was performed to determine whether rare variants in MERTK are associated with AMD. The data set included 4787 propensity score-matched case-control samples: 2394 AMD cases and 2393 controls. Whole-genome sequencing was performed and variants in MERTK were identified. Combined annotation-dependent depletion (CADD) scores and allele frequencies were calculated for each variant identified in MERTK. Student's t-test was used to assess the mean number of MERTK variants per subject between case and control cohorts (Bonferroni adjusted α = 0.0125). The number of subjects carrying at least one high CADD score loss-of-function or nonsynonymous mutation in each cohort was compared using Fisher's exact test (p < 0.05).

RESULTS

No significant difference was found in the mean number of MERTK variants in AMD versus control subjects (p = 0.0502). Additionally, there was no significant difference between cohorts in the number of subjects with at least one high CADD score loss-of-function or nonsynonymous variant (p = 0.15 at CADD > 10 and p = 0.91 at CADD > 20).

CONCLUSIONS

The present study provides a meaningfully negative result demonstrating that rare variants in MERTK are not associated with AMD. The study also demonstrates the role of large sample size genetic studies utilizing whole-genome sequencing as a powerful tool that can resolve clinically relevant questions regarding the genetic basis of ophthalmic disease.

Population structure in genetic studies: Confounding factors and mixed models

A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to accurately test for association while correcting for population structure is a computational and statistical challenge. Using laboratory mouse strains as an example, our review characterizes the problem of population structure in association studies and describes how it can cause false positive associations. We then motivate mixed models in the context of unmodeled factors.

Activating mutations of the gp130/JAK/STAT pathway in human diseases

Cytokines of the interleukin-6 (IL-6) family are involved in numerous physiological and pathophysiological processes. Dysregulated and increased activities of its members can be found in practically all human inflammatory diseases including cancer. All cytokines activate several intracellular signaling cascades, including the Jak/STAT, MAPK, PI3K, and Src/YAP signaling pathways. Additionally, several mutations in proteins involved in these signaling cascades have been identified in human patients, which render these proteins constitutively active and result in a hyperactivation of the signaling pathway. Interestingly, some of these mutations are associated with or even causative for distinct human diseases, making them interesting targets for therapy. This chapter describes the basic biology of the gp130/Jak/STAT pathway, summarizes what is known about the molecular mechanisms of the activating mutations, and gives an outlook how this knowledge can be exploited for targeted therapy in human diseases.

Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk

Multiple sclerosis is a complex neurological disease, with ∼20% of risk heritability attributable to common genetic variants, including >230 identified by genome-wide association studies. Multiple strands of evidence suggest that much of the remaining heritability is also due to additive effects of common variants rather than epistasis between these variants or mutations exclusive to individual families. Here, we show in 68,379 cases and controls that up to 5% of this heritability is explained by low-frequency variation in gene coding sequence. We identify four novel genes driving MS risk independently of common-variant signals, highlighting key pathogenic roles for regulatory T cell homeostasis and regulation, IFNγ biology, and NFκB signaling. As low-frequency variants do not show substantial linkage disequilibrium with other variants, and as coding variants are more interpretable and experimentally tractable than non-coding variation, our discoveries constitute a rich resource for dissecting the pathobiology of MS.

Samasy: an automated system for sample selection and robotic transfer

Sample automation and management is increasingly important as the number and size of population-scale and high-throughput projects grow. This is particularly the case in large-scale population studies where sample size is far outpacing the commonly used 96-well plate format. To facilitate management and transfer of samples in this format, we present Samasy, a web-based application for the construction of a sample database, intuitive display of sample and batch information, and facilitation of automated sample transfer or subset. Samasy is designed with ease-of-use in mind, can be quickly set up, and runs in any web browser.

Polygenic adaptation and convergent evolution on growth and cardiac genetic pathways in African and Asian rainforest hunter-gatherers

Different human populations facing similar environmental challenges have sometimes evolved convergent biological adaptations, for example, hypoxia resistance at high altitudes and depigmented skin in northern latitudes on separate continents. The "pygmy" phenotype (small adult body size), characteristic of hunter-gatherer populations inhabiting both African and Asian tropical rainforests, is often highlighted as another case of convergent adaptation in humans. However, the degree to which phenotypic convergence in this polygenic trait is due to convergent versus population-specific genetic changes is unknown. To address this question, we analyzed high-coverage sequence data from the protein-coding portion of the genomes of two pairs of populations: Batwa rainforest hunter-gatherers and neighboring Bakiga agriculturalists from Uganda and Andamanese rainforest hunter-gatherers and Brahmin agriculturalists from India. We observed signatures of convergent positive selection between the rainforest hunter-gatherers across the set of genes with "growth factor binding" functions ([Formula: see text]). Unexpectedly, for the rainforest groups, we also observed convergent and population-specific signatures of positive selection in pathways related to cardiac development (e.g., "cardiac muscle tissue development"; [Formula: see text]). We hypothesize that the growth hormone subresponsiveness likely underlying the adult small body-size phenotype may have led to compensatory changes in cardiac pathways, in which this hormone also plays an essential role. Importantly, in the agriculturalist populations, we did not observe similar patterns of positive selection on sets of genes associated with growth or cardiac development, indicating our results most likely reflect a history of convergent adaptation to the similar ecology of rainforests rather than a more general evolutionary pattern.

Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program

The Million Veteran Program (MVP) was established in 2011 as a national research initiative to determine how genetic variation influences the health of US military veterans. Here we genotyped 312,571 MVP participants using a custom biobank array and linked the genetic data to laboratory and clinical phenotypes extracted from electronic health records covering a median of 10.0 years of follow-up. Among 297,626 veterans with at least one blood lipid measurement, including 57,332 black and 24,743 Hispanic participants, we tested up to around 32 million variants for association with lipid levels and identified 118 novel genome-wide significant loci after meta-analysis with data from the Global Lipids Genetics Consortium (total n > 600,000). Through a focus on mutations predicted to result in a loss of gene function and a phenome-wide association study, we propose novel indications for pharmaceutical inhibitors targeting PCSK9 (abdominal aortic aneurysm), ANGPTL4 (type 2 diabetes) and PDE3B (triglycerides and coronary disease).

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

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

Analysis of Epistasis in Natural Traits Using Model Organisms

The ability to detect and understand epistasis in natural populations is important for understanding how biological traits are influenced by genetic variation. However, identification and characterization of epistasis in natural populations remains difficult due to statistical issues that arise as a result of multiple comparisons, and the fact that most genetic variants segregate at low allele frequencies. In this review, we discuss how model organisms may be used to manipulate genotypic combinations to power the detection of epistasis as well as test interactions between specific genes. Findings from a number of species indicate that statistical epistasis is pervasive between natural genetic variants. However, the properties of experimental systems that enable analysis of epistasis also constrain extrapolation of these results back into natural populations.

Meta-analysis of Icelandic and UK data sets identifies missense variants in SMO, IL11, COL11A1 and 13 more new loci associated with osteoarthritis

Osteoarthritis has a highly negative impact on quality of life because of the associated pain and loss of joint function. Here we describe the largest meta-analysis so far of osteoarthritis of the hip and the knee in samples from Iceland and the UK Biobank (including 17,151 hip osteoarthritis patients, 23,877 knee osteoarthritis patients, and more than 562,000 controls). We found 23 independent associations at 22 loci in the additive meta-analyses, of which 16 of the loci were novel: 12 for hip and 4 for knee osteoarthritis. Two associations are between rare or low-frequency missense variants and hip osteoarthritis, affecting the genes SMO (rs143083812, frequency 0.11%, odds ratio (OR) = 2.8, P = 7.9 × 10^-12, p.Arg173Cys) and IL11 (rs4252548, frequency 2.08%, OR = 1.30, P = 2.1 × 10^-11, p.Arg112His). A common missense variant in the COL11A1 gene also associates with hip osteoarthritis (rs3753841, frequency 61%, P = 5.2 × 10^-10, OR = 1.08, p.Pro1284Leu). In addition, using a recessive model, we confirm an association between hip osteoarthritis and a variant of CHADL¹ (rs117018441, P = 1.8 × 10^-25, OR = 5.9). Furthermore, we observe a complex relationship between height and risk of osteoarthritis.

A large-scale integrative analysis of GWAS and common meQTLs across whole life course identifies genes, pathways and tissue/cell types for three major psychiatric disorders

Attention deficit hyperactivity disorder (ADHD), bipolar disorder (BP) and schizophrenia (SCZ) are complex psychiatric disorders. We conducted a large-scale integrative analysis of genome-wide association studies (GWAS) and life course consistent methylation quantitative trait loci (meQTLs) datasets. The GWAS data of ADHD (including 20,183 cases and 35,191 controls), BP (including 7481 cases and 9250 controls) and SCZ (including 36,989 cases and 113,075 controls) were derived from published GWAS. Life course consistent meQTLs dataset was obtained from a longitudinal meQTLs analysis of 1018 mother-child pairs. Gene prioritization, pathway and tissue/cell type enrichment analysis were conducted by DEPICT. We identified multiple genes and pathways with common or disease specific effects, such as NISCH (P = 9.87 × 10^-3 for BP and 2.49 × 10^-6 for SCZ), ST3GAL3 (P = 1.19 × 10^-2 for ADHD), and KEGG_MAPK_SIGNALING_PATHWAY (P = 1.56 × 10^-3 for ADHD, P = 4.71 × 10^-2 for BP, P = 4.60 × 10^-4 for SCZ). Our study provides novel clues for understanding the genetic mechanism of ADHD, BP and SCZ.

Large-scale whole-exome sequencing association studies identify rare functional variants influencing serum urate levels

Elevated serum urate levels can cause gout, an excruciating disease with suboptimal treatment. Previous GWAS identified common variants with modest effects on serum urate. Here we report large-scale whole-exome sequencing association studies of serum urate and kidney function among ≤19,517 European ancestry and African-American individuals. We identify aggregate associations of low-frequency damaging variants in the urate transporters SLC22A12 (URAT1; p = 1.3 × 10⁻⁵⁶) and SLC2A9 (p = 4.5 × 10⁻⁷). Gout risk in rare SLC22A12 variant carriers is halved (OR = 0.5, p = 4.9 × 10⁻³). Selected rare variants in SLC22A12 are validated in transport studies, confirming three as loss-of-function (R325W, R405C, and T467M) and illustrating the therapeutic potential of the new URAT1-blocker lesinurad. In SLC2A9, mapping of rare variants of large effects onto the predicted protein structure reveals new residues that may affect urate binding. These findings provide new insights into the genetic architecture of serum urate, and highlight molecular targets in SLC22A12 and SLC2A9 for lowering serum urate and preventing gout.

Elevated serum urate levels are a risk factor for gout. Here, Tin et al. perform whole-exome sequencing in 19,517 individuals and detect low-frequency genetic variants in urate transporter genes, SLC22A12 and SLC2A9, associated with serum urate levels and confirm their damaging nature in vitro and in silico.

Genetik der allgemeinen kognitiven Fähigkeit

Intelligenz ist eines der bestuntersuchten Konstrukte der empirischen Verhaltenswissenschaften und stellt eine allgemeine geistige Kapazität dar, die unter anderem die Fähigkeit zum schlussfolgernden Denken, zum Lösen neuartiger Probleme, zum abstrakten Denken sowie zum schnellen Lernen umfasst. Diese kognitiven Fähigkeiten spielen eine große Rolle in der Erklärung und Vorhersage individueller Unterschiede in zentralen Bereichen des gesellschaftlichen Lebens, wie Schul- und Bildungserfolg, Berufserfolg, sozioökonomischer Status und Gesundheitsverhalten. Verhaltensgenetische Studien zeigen konsistent, dass genetische Einflüsse einen substanziellen Beitrag zur Erklärung individueller Unterschiede leisten, die über 60 % der Intelligenzunterschiede im Erwachsenenalter erklären. In den letzten Jahren konnten in großen genomweiten Assoziationsstudien mit häufigen genetischen Varianten Hunderte mit Intelligenz assoziierte Loci identifiziert werden sowie über 1300 assoziierte Gene mit differentieller Expression überwiegend im Gehirn. Mehrere Signalwege waren angereichert, vor allen für Neurogenese, Regulation der Entwicklung des Nervensystems sowie der synaptischen Struktur und Aktivität. Die Mehrzahl der assoziierten Loci betraf regulatorische Regionen und interessanterweise lag die Hälfte intronisch. Von den über 1300 Genen überlappen nur 9,2 % mit solchen, die mit monogenen neurokognitiven Störungen assoziiert sind. Insgesamt bestätigen die Befunde ein polygenes Modell Tausender additiver Faktoren, wobei die einzelnen Loci eine sehr geringe Effektstärke aufweisen. Insgesamt erklären die jetzigen Befunde ca. 10 % der Gesamtvarianz des Merkmals. Diese Ergebnisse sind ein wichtiger Ausgangspunkt für zukünftige Forschung sowohl in der Genetik als auch den Verhaltenswissenschaften.

Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance via mean imputed r2 at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts vs. single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1%) by 0.5-3.1% for an array of one million sites and 0.7-7.1% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

Accurate Genomic Prediction of Human Height

We construct genomic predictors for heritable but extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). The constructed predictors explain, respectively, ∼40, 20, and 9% of total variance for the three traits, in data not used for training. For example, predicted heights correlate ∼0.65 with actual height; actual heights of most individuals in validation samples are within a few centimeters of the prediction. The proportion of variance explained for height is comparable to the estimated common SNP heritability from genome-wide complex trait analysis (GCTA), and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for SNPs. Thus, our results close the gap between prediction R-squared and common SNP heritability. The ∼20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common variants. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier genome-wide association studies (GWAS) for out-of-sample validation of our results.

A subregion-based burden test for simultaneous identification of susceptibility loci and subregions within

In rare variant association studies, aggregating rare and/or low frequency variants, may increase statistical power for detection of the underlying susceptibility gene or region. However, it is unclear which variants, or class of them, in a gene contribute most to the association. We proposed a subregion-based burden test (REBET) to simultaneously select susceptibility genes and identify important underlying subregions. The subregions are predefined by shared common biologic characteristics, such as the protein domain or functional impact. Based on a subset-based approach considering local correlations between combinations of test statistics of subregions, REBET is able to properly control the type I error rate while adjusting for multiple comparisons in a computationally efficient manner. Simulation studies show that REBET can achieve power competitive to alternative methods when rare variants cluster within subregions. In two case studies, REBET is able to identify known disease susceptibility genes, and more importantly pinpoint the unreported most susceptible subregions, which represent protein domains essential for gene function. R package REBET is available at https://dceg.cancer.gov/tools/analysis/rebet.

Combined linkage and association analysis identifies rare and low frequency variants for blood pressure at 1q31

High blood pressure (BP) is a major risk factor for cardiovascular disease (CVD) and is more prevalent in African Americans as compared to other US groups. Although large, population-based genome-wide association studies (GWAS) have identified over 300 common polymorphisms modulating inter-individual BP variation, largely in European ancestry subjects, most of them do not localize to regions previously identified through family-based linkage studies. This discrepancy has remained unexplained despite the statistical power differences between current GWAS and prior linkage studies. To address this issue, we performed genome-wide linkage analysis of BP traits in African-American families from the Family Blood Pressure Program (FBPP) and genotyped on the Illumina Human Exome BeadChip v1.1. We identified a genomic region on chromosome 1q31 with LOD score 3.8 for pulse pressure (PP), a region we previously implicated in DBP studies of European ancestry families. Although no reported GWAS variants map to this region, combined linkage and association analysis of PP identified 81 rare and low frequency exonic variants accounting for the linkage evidence. Replication analysis in eight independent African ancestry cohorts (N = 16,968) supports this specific association with PP (P = 0.0509). Additional association and network analyses identified multiple potential candidate genes in this region expressed in multiple tissues and with a strong biological support for a role in BP. In conclusion, multiple genes and rare variants on 1q31 contribute to PP variation. Beyond producing new insights into PP, we demonstrate how family-based linkage and association studies can implicate specific rare and low frequency variants for complex traits.

Adjustment for covariates using summary statistics of genome-wide association studies

Linear regression is a standard approach to identify genetic variants associated with continuous traits in genome-wide association studies (GWAS). In a standard epidemiology study, linear regression is often performed with adjustment for covariates to estimate the independent effect of a predictor variable or to improve statistical power by reducing residual variability. However, it is problematic to adjust for heritable covariates in genetic association analysis. Here, we propose a new method that utilizes summary statistics of the covariate from additional samples for reducing the residual variability and hence improves statistical power. Our simulation study showed that the proposed methodology can maintain a good control of Type I error and can achieve much higher power than a simple linear regression. The method is illustrated by an application to the GWAS results from the Genetic Investigation of Anthropometric Traits consortium.

Selective trade-offs maintain alleles underpinning complex trait variation in plants

To understand evolutionary factors that maintain complex trait variation, we sequenced genomes from a single population of the plant Mimulus guttatus, identifying hundreds of nucleotide variants associated with morphological and life history traits. Alleles that delayed flowering also increased size at reproduction, which suggests pervasive antagonistic pleiotropy in this annual plant. The "large and slow" alleles, which were less common in small, rapidly flowering populations, became more abundant in populations with greater plant size. Furthermore, natural selection within the field population favored alternative alleles from year to year. Our results suggest that environmental fluctuations and selective trade-offs maintain polygenic trait variation within populations and also contribute to the geographic divergence in this wildflower species.

Exploring rare and low-frequency variants in the Saguenay-Lac-Saint-Jean population identified genes associated with asthma and allergy traits

The Saguenay–Lac-Saint-Jean (SLSJ) region is located in northeastern Quebec and is known for its unique demographic history and founder effect. As founder populations are enriched with population-specific variants, we characterized the variants distribution in SLSJ and compared it with four European populations (Finnish, Sweden, United Kingdom and France), of which the Finnish population is another founder population. Targeted sequencing of the coding and non-coding immune regulatory regions of the SLSJ asthma familial cohort and the four European populations were performed. Rare and low-frequency coding and non-coding regulatory variants identified in the SLSJ population were then investigated for variant- and gene-level associations with asthma and allergy-related traits (eosinophil percentage, immunoglobulin (Ig) E levels and lung function). Our data showed that (1) rare or deleterious variants were not enriched in the two founder populations as compared with the three non-founder European populations; (2) a larger proportion of founder population-specific variants occurred with higher frequencies; and (3) low-frequency variants appeared to be more deleterious. Furthermore, a rare variant, rs1386931, located in the 3ʹ-UTR of CXCR6 and intron of FYCO1 was found to be associated with eosinophil percentage. Gene-based analyses identified NRP2, MRPL44 and SERPINE2 to be associated with various asthma and allergy-related traits. Our study demonstrated the usefulness of using a founder population to identify new genes associated with asthma and allergy-related traits; thus better understand the genes and pathways implicated in pathophysiology.

Insights and Implications of Genome-Wide Association Studies of Height

CONTEXT

In the last decade, genome-wide association studies (GWASs) have catalyzed our understanding of the genetics of height and have identified hundreds of regions of the genome associated with adult height and other height-related body measurements.

EVIDENCE ACQUISITION

GWASs related to height were identified via PubMed search and a review of the GWAS catalog.

EVIDENCE SYNTHESIS

The GWAS results demonstrate that height is highly polygenic: that is, many thousands of genetic variants distributed across the genome each contribute to an individual's height. These height-associated regions of the genome are enriched for genes in known biological pathways involved in growth, such as fibroblast growth factor signaling, as well as for genes expressed in relevant tissues, such as the growth plate. GWASs can also uncover previously unappreciated biological pathways, such as the STC2/PAPPA/IGFBP4 pathway. The genes implicated by GWASs are often the same genes that are the genetic causes of Mendelian growth disorders or skeletal dysplasias, and GWAS results can provide complementary information about these disorders.

CONCLUSIONS

Here, we review the rationale behind GWASs and what we have learned from GWASs for height, including how it has enhanced our understanding of the underlying biology of human growth. We also highlight the implications of GWASs in terms of prediction of adult height and our understanding of Mendelian growth disorders.

Towards broadening Forensic DNA Phenotyping beyond pigmentation: Improving the prediction of head hair shape from DNA

Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci of which 8 were novel and introducing a prediction model for Europeans based on 14 SNPs. In the present study, we evaluated the capacity of DNA-based head hair shape prediction by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation. Prediction model building was carried out in 9674 subjects (6068 from Europe, 2899 from Asia and 707 of admixed European and Asian ancestries), used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype and phenotype data were newly collected in 2415 independent subjects (2138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci we identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans; the statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits.

The insulin-like growth factor 2 gene and locus in nonmammalian vertebrates: Organizational simplicity with duplication but limited divergence in fish

The small, secreted peptide, insulin-like growth factor 2 (IGF2), is essential for fetal and prenatal growth in humans and other mammals. Human IGF2 and mouse Igf2 genes are located within a conserved linkage group and are regulated by parental imprinting, with IGF2/Igf2 being expressed from the paternally derived chromosome, and H19 from the maternal chromosome. Here, data retrieved from genomic and gene expression repositories were used to examine the Igf2 gene and locus in 8 terrestrial vertebrates, 11 ray-finned fish, and 1 lobe-finned fish representing >500 million years of evolutionary diversification. The analysis revealed that vertebrate Igf2 genes are simpler than their mammalian counterparts, having fewer exons and lacking multiple gene promoters. Igf2 genes are conserved among these species, especially in protein-coding regions, and IGF2 proteins also are conserved, although less so in fish than in terrestrial vertebrates. The Igf2 locus in terrestrial vertebrates shares additional genes with its mammalian counterparts, including tyrosine hydroxylase (Th), insulin (Ins), mitochondrial ribosomal protein L23 (Mrpl23), and troponin T3, fast skeletal type (Tnnt3), and both Th and Mrpl23 are present in the Igf2 locus in fish. Taken together, these observations support the idea that a recognizable Igf2 was present in the earliest vertebrate ancestors, but that other features developed and diversified in the gene and locus with speciation, especially in mammals. This study also highlights the need for correcting inaccuracies in genome databases to maximize our ability to accurately assess contributions of individual genes and multigene families toward evolution, physiology, and disease.

Relevance of polymorphisms in MC4R and BDNF in short normal stature

Background

Variation in genes of the leptinergic-melanocortinergic system influence both body weight and height. Because short normal stature (SNS) is characterized by reduced body height, delayed maturation and leanness, allelic variation of genes in this pathway are hypothesized to affect this common condition.

Methods

We analyzed the coding regions of LEP, MC4R, MRAP2 and BDNF in 185 children with SNS (height < 5th percentile) to search for non-synonymous and frameshift variants. For association studies (two-sided χ²-tests) population-based data sets (ExAC, EVS and KORA) were used. Cyclic AMP accumulation, cell surface expression, central expression and MAP kinase activation were assayed in vitro to determine the functional implications of identified variants.

Results

We detected eleven variants predicted to be protein-altering, four in MC4R, four in BDNF, and three in MRAP2. No variants were found in LEP. In vitro analysis implied reduced function for the MC4R variant p.Met215Ile. Loss-of-function is contrary to expectations based on obesity studies, and thus does not support that this variant is relevant for SNS. The minor SNP alleles at MC4R p.Val103Ile and BDNF p.Val66Met were nominally associated with SNS.

Conclusion

Taken together, although genes of the leptinergic-melanocortinergic system are important for normal growth, our data do not support the involvement of rare mutations in LEP, MC4R, MRAP2 or BDNF in short normal stature.

Electronic supplementary material

The online version of this article (10.1186/s12887-018-1245-1) contains supplementary material, which is available to authorized users.

Functional annotation of genomic variants in studies of late-onset Alzheimer's disease

Motivation

Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied.

Results

In this work, we outline an annotation process motivated by the Alzheimer's Disease Sequencing Project, illustrate the impact of including tissue-specific transcript sets and sources of gene regulatory information and assess the potential impact of changing genomic builds on the annotation process. While these factors only impact a small proportion of total variant annotations (∼5%), they influence the potential analysis of a large fraction of genes (∼25%).

Availability and implementation

Individual variant annotations are available via the NIAGADS GenomicsDB, at https://www.niagads.org/genomics/ tools-and-software/databases/genomics-database. Annotations are also available for bulk download at https://www.niagads.org/datasets. Annotation processing software is available at http://www.icompbio.net/resources/software-and-downloads/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of nine new susceptibility loci for endometrial cancer

Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins (SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study.

COL4A3 Gene Variants and Diabetic Kidney Disease in MODY

BACKGROUND AND OBJECTIVES

Despite advances in identifying genetic factors of diabetic kidney disease (DKD), much of the heritability remains unexplained. Nine maturity-onset diabetes in young (MODY) probands with kidney biopsy-proven DKD were selected and included in this study. The probands had more severe DKD compared with their parents with MODY, with overt proteinuria or rapid progression to ESKD. We aimed to explore the contribution of the variants in susceptibility genes of DKD to the severity of kidney phenotype between the probands and their parents.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Whole-exome sequencing was performed to identify suspected MODY probands and their families. Known DKD susceptibility genes were reviewed. Variants reported to be associated with DKD, or those with minor allele frequency <0.05 and predicted to be pathogenic, were selected and analyzed. Immunofluorescence staining of COL4α3 was performed in kidney specimens of patients with DKD with or without R408H and M1209I of COL4A3 variants.

RESULTS

HNF1B-MODY, CEL-MODY, PAX4-MODY, and WFS1-MODY were diagnosed among nine families. We identified 196 selected variants of 25 DKD susceptibility genes among the participants. Analysis of phenotype between probands and parents, gene function, and protein-protein interaction networks revealed that COL4A3 variants were involved in the progression of DKD. Weak granular staining of COL4α3 was observed in the glomerular basement membrane of patients with the R408H and M1209I variants, whereas strong consecutive staining was observed in patients without these variants. Moreover, more number of DKD variants were identified in probands than in their parents with MODY.

CONCLUSIONS

The genetic effect of more pathogenic variants in various DKD susceptibility genes, especially variants in the COL4A3 gene, partially explained the more severe kidney phenotype in probands with kidney biopsy-proven DKD.

Gene hunting with hidden Markov model knockoffs

Modern scientific studies often require the identification of a subset of explanatory variables. Several statistical methods have been developed to automate this task, and the framework of knockoffs has been proposed as a general solution for variable selection under rigorous Type I error control, without relying on strong modelling assumptions. In this paper, we extend the methodology of knockoffs to problems where the distribution of the covariates can be described by a hidden Markov model. We develop an exact and efficient algorithm to sample knockoff variables in this setting and then argue that, combined with the existing selective framework, this provides a natural and powerful tool for inference in genome-wide association studies with guaranteed false discovery rate control. We apply our method to datasets on Crohn's disease and some continuous phenotypes.

IGFBP-4 and PAPP-A in normal physiology and disease

Insulin-like growth factor (IGF) binding protein-4 (IGFBP-4) is a modulator of the IGF system, exerting both inhibitory and stimulatory effects on IGF-induced cellular growth. IGFBP-4 is the principal substrate for the enzyme pregnancy-associated plasma protein-A (PAPP-A). Through IGF-dependent cleavage of IGFBP-4 in the vicinity of the IGF receptor, PAPP-A is able to increase IGF bioavailability and stimulate IGF-mediated growth. Recently, the stanniocalcins (STCs) were identified as novel inhibitors of PAPP-A proteolytic activity, hereby adding additional members to the seemingly endless list of proteins belonging to the IGF family. Our understanding of these proteins has advanced throughout recent years, and there is evidence to suggest that the role of IGFBP-4 and PAPP-A in defining the relationship between total IGF and IGF bioactivity can be linked to a number of pathological conditions. This review provides an overview of the experimental and clinical findings on the IGFBP-4/PAPP-A/STC axis as a regulator of IGF activity and examines the conundrum surrounding extrapolation of circulating concentrations to tissue action of these proteins. The primary focus will be on the biological significance of IGFBP-4 and PAPP-A in normal physiology and in pathophysiology with emphasis on metabolic disorders, cardiovascular diseases, and cancer. Finally, the review assesses current new trajectories of IGFBP-4 and PAPP-A research.

Genetic causes of proportionate short stature

Human growth is a very complex phenomenon influenced by genetic, hormonal, nutritional and environmental factors, from fetal life to puberty. Although the GH-IGF axis has a central role with specific actions on growth, numerous genes are involved in the control of stature. Genome-wide association studies have identified >600 variants associated with human height, still explaining only a small fraction of phenotypic variation. Since short stature in childhood is a common reason for referral, pediatric endocrinologists must be aware of the multifactorial and polygenic contributions to height. Multiple disorders characterized by growth failure of prenatal and/or postnatal onset due to single gene defects have been described. Their early diagnosis, facilitated by advances in genomic technologies, is of upmost importance for their clinical management and to provide genetic counseling. Here we review the current clinical and genetic information regarding different syndromes and hormone abnormalities with proportionate short stature as the main feature, and provide an update of the approach for diagnosis and management.

New developments in the genetic diagnosis of short stature

PURPOSE OF REVIEW

Genome-wide approaches including genome-wide association studies as well as exome and genome sequencing represent powerful new approaches that have improved our ability to identify genetic causes of human disorders. The purpose of this review is to describe recent advances in the genetic causes of short stature.

RECENT FINDINGS

In addition to SHOX deficiency which is one of the most common causes of isolated short stature, PAPPA2, ACAN, NPPC, NPR2, PTPN11 (and other rasopathies), FBN1, IHH and BMP2 have been identified in isolated growth disorders with or without other mild skeletal findings. In addition, novel genetic causes of syndromic short stature have been discovered, including pathogenic variants in BRCA1, DONSON, AMMECR1, NFIX, SLC25A24, and FN1.

SUMMARY

Isolated growth disorders are often monogenic. Specific genetic causes typically have specific biochemical and/or phenotype characteristics which are diagnostically helpful. Identification of additional subjects with a specific genetic cause of short stature often leads to a broadening of the known clinical spectrum for that condition. The identification of novel genetic causes of short stature has provided important insights into the underlying molecular mechanisms of growth failure.

Identification of 613 new loci associated with heel bone mineral density and a polygenic risk score for bone mineral density, osteoporosis and fracture

Low bone mineral density (BMD) leads to osteoporosis, and is a risk factor for bone fractures, including stress fractures. Using data from UK Biobank, a genome-wide association study identified 1,362 independent SNPs that clustered into 899 loci of which 613 are new. These data were used to train a genetic algorithm using 22,886 SNPs as predictors and showing a correlation with heel bone mineral density of 0.415. Combining this genetic algorithm with height, weight, age and sex resulted in a correlation with heel bone mineral density of 0.496. Individuals with low scores (2.2% of total) showed a change in BMD of -1.16 T-score units, an increase in risk for osteoporosis of 17.4 fold and an increase in risk for fracture of 1.87 fold. Genetic predictors could assist in the identification of individuals at risk for osteoporosis or fractures.

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

It is of great interest to estimate the joint effects of multiple variants from large scale meta-analyses, in order to fine-map causal variants and understand the genetic architecture for complex traits. The summary association statistics from participating studies in a meta-analysis often contain missing values at some variant sites, as the imputation methods may not work well and the variants with low imputation quality will be filtered out. Missingness is especially likely when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing methods for conditional meta-analysis do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly inflated type-I errors for conditional analysis, which will result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.

IGF-binding proteins

Insulin-like growth factor-binding proteins (IGFBPs) 1-6 bind IGFs but not insulin with high affinity. They were initially identified as serum carriers and passive inhibitors of IGF actions. However, subsequent studies showed that, although IGFBPs inhibit IGF actions in many circumstances, they may also potentiate these actions. IGFBPs are widely expressed in most tissues, and they are flexible endocrine and autocrine/paracrine regulators of IGF activity, which is essential for this important physiological system. More recently, individual IGFBPs have been shown to have IGF-independent actions. Mechanisms underlying these actions include (i) interaction with non-IGF proteins in compartments including the extracellular space and matrix, the cell surface and intracellular space, (ii) interaction with and modulation of other growth factor pathways including EGF, TGF-β and VEGF, and (iii) direct or indirect transcriptional effects following nuclear entry of IGFBPs. Through these IGF-dependent and IGF-independent actions, IGFBPs modulate essential cellular processes including proliferation, survival, migration, senescence, autophagy and angiogenesis. They have been implicated in a range of disorders including malignant, metabolic, neurological and immune diseases. A more complete understanding of their cellular roles may lead to the development of novel IGFBP-based therapeutic opportunities.

PAPP-A and cancer

The zinc metalloproteinase, PAPP-A, enhances local insulin-like growth factor (IGF) action through cleavage of inhibitory IGF-binding proteins, thereby increasing IGF available for IGF receptor-mediated cell proliferation, migration and survival. In many tumors, enhanced IGF receptor signaling is associated with tumor growth, invasion and metastasis. We will first discuss PAPP-A structure and function, and post-translational inhibitors of PAPP-A expression or proteolytic activity. We will then review the evidence supporting an important role for PAPP-A in many cancers, including breast, ovarian and lung cancer, and Ewing sarcoma.