Predicting human height by Victorian and genomic methods

Parameters in dynamic models of complex traits are containers of missing heritability

Polymorphisms identified in genome-wide association studies of human traits rarely explain more than a small proportion of the heritable variation, and improving this situation within the current paradigm appears daunting. Given a well-validated dynamic model of a complex physiological trait, a substantial part of the underlying genetic variation must manifest as variation in model parameters. These parameters are themselves phenotypic traits. By linking whole-cell phenotypic variation to genetic variation in a computational model of a single heart cell, incorporating genotype-to-parameter maps, we show that genome-wide association studies on parameters reveal much more genetic variation than when using higher-level cellular phenotypes. The results suggest that letting such studies be guided by computational physiology may facilitate a causal understanding of the genotype-to-phenotype map of complex traits, with strong implications for the development of phenomics technology.

Despite an ever-increasing number of genome locations reported to be associated with complex human diseases or quantitative traits, only a small proportion of phenotypic variations in a typical quantitative trait can be explained by the discovered variants. We argue that this problem can partly be resolved by combining the statistical methods of quantitative genetics with computational biology. We demonstrate this for the in silico genotype-to-phenotype map of a model heart cell in conjunction with publically accessible genomic data. We show that genome wide association studies (GWAS) on model parameters identify more causal variants and can build better prediction models for the higher-level phenotypes than by performing GWAS on the higher-level phenotypes themselves. Since model parameters are in principle measurable physiological phenotypes, our findings suggest that development of future phenotyping technologies could be guided by mathematical models of the biological systems being targeted.

Child development and molecular genetics: 14 years later

Fourteen years ago, the first article on molecular genetics was published in this journal: Child Development, Molecular Genetics, andWhat to Do With Genes Once They Are Found (R. Plomin & M. Rutter, 1998). The goal of the article was to outline what developmentalists can do with genes once they are found. These new directions for developmental research are still relevant today. The problem lies with the phrase “once they are found”: It has been much more difficult than expected to identify genes responsible for the heritability of complex traits and common disorders, the so-called missing heritability problem. The present article considers reasons for the missing heritability problem and possible solutions.

Extent of height variability explained by known height-associated genetic variants in an isolated population of the Adriatic coast of Croatia

BACKGROUND

Human height is a classical example of a polygenic quantitative trait. Recent large-scale genome-wide association studies (GWAS) have identified more than 200 height-associated loci, though these variants explain only 2∼10% of overall variability of normal height. The objective of this study was to investigate the variance explained by these loci in a relatively isolated population of European descent with limited admixture and homogeneous genetic background from the Adriatic coast of Croatia.

METHODOLOGY/PRINCIPAL FINDINGS

In a sample of 1304 individuals from the island population of Hvar, Croatia, we performed genome-wide SNP typing and assessed the variance explained by genetic scores constructed from different panels of height-associated SNPs extracted from five published studies. The combined information of the 180 SNPs reported by Lango Allen el al. explained 7.94% of phenotypic variation in our sample. Genetic scores based on 20~50 SNPs reported by the remaining individual GWA studies explained 3~5% of height variance. These percentages of variance explained were within ranges comparable to the original studies and heterogeneity tests did not detect significant differences in effect size estimates between our study and the original reports, if the estimates were obtained from populations of European descent.

CONCLUSIONS/SIGNIFICANCE

We have evaluated the portability of height-associated loci and the overall fitting of estimated effect sizes reported in large cohorts to an isolated population. We found proportions of explained height variability were comparable to multiple reference GWAS in cohorts of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent.

How useful is BMI in predicting adiposity indicators in a sample of Maya children and women with high levels of stunting?

OBJECTIVES

Body mass index (BMI) is used frequently to estimate adiposity levels in children and adults. However, the applicability of BMI to populations with high levels of stunting has been questioned. Stunted people can have disproportionately short legs, which may increase BMI without increasing body fat because of the relatively larger trunk compared with the legs.

METHODS

A sample of 57 urban Maya schoolchildren, aged 7-9 years (31 boys), and 53 adult women underwent anthropometric assessments and bioelectrical impedance analysis. Multiple linear regression was performed to determine whether the ability of BMI to predict adiposity indicators is altered by stunting and sitting height ratio (SHR). The adiposity indicators were waist circumference, sum of skinfolds, upper arm muscle area, upper arm fat area, and arm fat index.

RESULTS

BMI was the strongest predictor of all adiposity indicators and in most cases, explained more of the variance in adiposity of Maya children than Maya women. Abdominal adiposity was better predicted by BMI than peripheral adiposity in Maya women and Maya children. Stunting was significant in predicting adiposity in some models but never substantially changed the variance explained. SHR was never a significant predictor.

CONCLUSIONS

The relationship between BMI and adiposity indicators is not changed by stunting status or body proportions in this short population of urban Maya children and women. BMI can be used as an indicator of adiposity for these children but not the women. It is recommended that BMI is used in conjunction with other estimates of body composition.

Lactase persistence and milk consumption are associated with body height in Swedish preadolescents and adolescents

BACKGROUND

Body height is a classic polygenic trait. About 80%-90% of height is inherited and 10%-20% owed to environmental factors, of which the most important ones are nutrition and diseases in preadolescents and adolescents.

OBJECTIVE

The aim of this study was to explore potential relations between the LCT (lactase) C>T-13910 polymorphism, milk consumption, and body height in a sample of Swedish preadolescents and adolescents.

DESIGN

In a cross-sectional study, using a random sample of preadolescents and adolescents (n = 597), dietary intakes were determined. Anthropometric measurements including sexual maturity (Tanner stage) and birth weight were assessed. Parental body height and socio-economic status (SES) were obtained by questionnaires. Genotyping for the LCT C>T-13910 polymorphism that renders individuals lactase persistent (LP) or lactase non-persistent (LNP) was performed by DNA sequencing. Stepwise backward multivariate linear regression was used.

RESULTS

Milk consumption was significantly and positively associated with body height (β = 0.45; 95% CI: 0.040, 0.87, p = 0.032). Adjustments were performed for sex, parental height, birth weight, body mass index (BMI), SES, and Tanner stage. This model explains 90% of the observed variance of body height (adjusted R(2) = 0.89). The presence of the -13910 T allele was positively associated with body height (β = 2.05; 95% CI: 0.18, 3.92, p = 0.032).

CONCLUSIONS

Milk consumption is positively associated with body height in preadolescents and adolescents. We show for the first time that a nutrigenetic variant might be able to explain in part phenotypic variation of body height in preadolescents and adolescents. Due to the small sample size further studies are needed.

Is life law-like?

Genes are generally assumed to be primary biological causes of biological phenotypes and their evolution. In just over a century, a research agenda that has built on Mendel's experiments and on Darwin's theory of natural selection as a law of nature has had unprecedented scientific success in isolating and characterizing many aspects of genetic causation. We revel in these successes, and yet the story is not quite so simple. The complex cooperative nature of genetic architecture and its evolution include teasingly tractable components, but much remains elusive. The proliferation of data generated in our "omics" age raises the question of whether we even have (or need) a unified theory or "law" of life, or even clear standards of inference by which to answer the question. If not, this not only has implications for the widely promulgated belief that we will soon be able to predict phenotypes like disease risk from genes, but also speaks to the limitations in the underlying science itself. Much of life seems to be characterized by ad hoc, ephemeral, contextual probabilism without proper underlying distributions. To the extent that this is true, causal effects are not asymptotically predictable, and new ways of understanding life may be required.

Alzheimer's disease genetics: current knowledge and future challenges

Alzheimer's disease (AD) is highly heritable, but genetically complex. Recently, three large-scale genome-wide association studies have made substantial breakthroughs in disentangling the genetic architecture of the disease. These studies combined include data from over 43 000 independent individuals and provide compelling evidence that variants in four novel susceptibility genes (CLU, PICALM, CR1, BIN1) are associated with disease risk. These findings are tremendously exciting, not only in providing new avenues for exploration, but also highlighting the potential for further gene discovery when larger samples are analysed. Here we discuss progress to date in identifying risk genes for dementia, ways forward and how current findings are refining previous ideas and defining new putative primary disease mechanisms.

From Galton to GWAS: quantitative genetics of human height

Height has been studied in human genetics since the late 1800s. We review what we have learned about the genetic architecture of this trait from the resemblance between relatives and from genetic marker data. All empirical evidence points towards height being highly polygenic, with many loci contributing to variation in the population and most effect sizes appear to be small. Nevertheless, combining new genetic and genomic technologies with phenotypic measures on height on large samples facilitates new answers to old questions, including the basis of assortative mating in humans, estimation of non-additive genetic variation and partitioning between-cohort phenotypic differences into genetic and non-genetic underlying causes.

Improving human forensics through advances in genetics, genomics and molecular biology

Forensic DNA profiling currently allows the identification of persons already known to investigating authorities. Recent advances have produced new types of genetic markers with the potential to overcome some important limitations of current DNA profiling methods. Moreover, other developments are enabling completely new kinds of forensically relevant information to be extracted from biological samples. These include new molecular approaches for finding individuals previously unknown to investigators, and new molecular methods to support links between forensic sample donors and criminal acts. Such advances in genetics, genomics and molecular biology are likely to improve human forensic case work in the near future.

Genome-wide association approaches for identifying loci for human height genes

Human height is a very heritable trait as most of other human anthropometric traits. Genome-wide association studies have thus far identified about 200 genes associated with height with a genome-wide significance. Very large meta-analyses were needed to achieve this. These 200 height genes are involved in various biologically plausible pathways for growth, but yet explain only 10% of the variance in height. So it is obvious, that the GIANT-consortium height meta-analysis leaves, as most of GWA studies, a major part of the genetic variation unexplained. Much work utilizing several different strategies and very large study cohorts are needed to identify more genes for growth.

Genetic risk sum score comprised of common polygenic variation is associated with body mass index

Genome-wide association studies (GWAS) of body mass index (BMI) using large samples have yielded approximately a dozen robustly associated variants and implicated additional loci. Individually these variants have small effects and in aggregate explain a small proportion of the variance. As a result, replication attempts have limited power to achieve genome-wide significance, even with several thousand subjects. Since there is strong prior evidence for genetic influence on BMI for specific variants, alternative approaches to replication can be applied. Instead of testing individual loci sequentially, a genetic risk sum score (GRSS) summarizing the total number of risk alleles can be tested. In the current study, GRSS comprising 56 top variants catalogued from two large meta-analyses was tested for association with BMI in the Molecular Genetics of Schizophrenia controls (2,653 European-Americans, 973 African-Americans). After accounting for covariates known to influence BMI (ancestry, sex, age), GRSS was highly associated with BMI (p value = 3.19 E-06) although explained a limited amount of the variance (0.66%). However, area under receiver operator criteria curve (AUC) estimates indicated that the GRSS and covariates significantly predicted overweight and obesity classification with maximum discriminative ability for predicting class III obesity (AUC = 0.697). The relative contributions of the individual loci to GRSS were examined post hoc and the results were not due to a few highly significant variants, but rather the result of numerous variants of small effect. This study provides evidence of the utility of a GRSS as an alternative approach to replication of common polygenic variation in complex traits.

Variance heterogeneity analysis for detection of potentially interacting genetic loci: method and its limitations

BACKGROUND

Presence of interaction between a genotype and certain factor in determination of a trait's value, it is expected that the trait's variance is increased in the group of subjects having this genotype. Thus, test of heterogeneity of variances can be used as a test to screen for potentially interacting single-nucleotide polymorphisms (SNPs). In this work, we evaluated statistical properties of variance heterogeneity analysis in respect to the detection of potentially interacting SNPs in a case when an interaction variable is unknown.

RESULTS

Through simulations, we investigated type I error for Bartlett's test, Bartlett's test with prior rank transformation of a trait to normality, and Levene's test for different genetic models. Additionally, we derived an analytical expression for power estimation. We showed that Bartlett's test has acceptable type I error in the case of trait following a normal distribution, whereas Levene's test kept nominal Type I error under all scenarios investigated. For the power of variance homogeneity test, we showed (as opposed to the power of direct test which uses information about known interacting factor) that, given the same interaction effect, the power can vary widely depending on the non-estimable direct effect of the unobserved interacting variable. Thus, for a given interaction effect, only very wide limits of power of the variance homogeneity test can be estimated. Also we applied Levene's approach to test genome-wide homogeneity of variances of the C-reactive protein in the Rotterdam Study population (n = 5959). In this analysis, we replicate previous results of Pare and colleagues (2010) for the SNP rs12753193 (n = 21,799).

CONCLUSIONS

Screening for differences in variances among genotypes of a SNP is a promising approach as a number of biologically interesting models may lead to the heterogeneity of variances. However, it should be kept in mind that the absence of variance heterogeneity for a SNP can not be interpreted as the absence of involvement of the SNP in the interaction network.

The heritability of ocular traits

Heritability is the proportion of phenotypic variation in a population that is attributable to genetic variation among individuals. Many ophthalmic disorders and biometric traits are known to have a genetic basis and consequently much work has been published in the literature estimating the heritability of various ocular parameters. We collated and summarized the findings of heritability studies conducted in the field of ophthalmology. We grouped the various studies broadly by phenotype as follows: refraction, primary open-angle glaucoma, age-related macular degeneration (AMD), cataract, diabetic retinopathy, and others. A total of 82 articles were retrieved from the literature relating to estimation of heritability for an ocular disease or biometric trait; of these, 37 papers were concerned with glaucoma, 28 with refraction, 4 with AMD, 5 with diabetic retinopathy, and 4 with cataract. The highest reported heritability for an ophthalmic trait is 0.99 for the phenotype ≥ 20 small hard drusen, indicating that observed variation in this parameter is largely governed by genetic factors. Over 60% of the studies employed a twin study design and a similar percentage utilized variance components methods and structural equation modeling (SEM) to derive their heritability values. Using modern SEM techniques, heritability estimates derived from twin subjects were generally higher than those from family data. Many of the estimates are in the moderate to high range, but to date the majority of genetic variants accounting for these findings have not been uncovered, hence much work remains to be undertaken to elucidate fully their molecular etiology.

The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation

There has long been interest in understanding the genetic basis of human adaptation. To what extent are phenotypic differences among human populations driven by natural selection? With the recent arrival of large genome-wide data sets on human variation, there is now unprecedented opportunity for progress on this type of question. Several lines of evidence argue for an important role of positive selection in shaping human variation and differences among populations. These include studies of comparative morphology and physiology, as well as population genetic studies of candidate loci and genome-wide data. However, the data also suggest that it is unusual for strong selection to drive new mutations rapidly to fixation in particular populations (the 'hard sweep' model). We argue, instead, for alternatives to the hard sweep model: in particular, polygenic adaptation could allow rapid adaptation while not producing classical signatures of selective sweeps. We close by discussing some of the likely opportunities for progress in the field.

Novel genes for QTc interval. How much heritability is explained, and how much is left to find?

The corrected QT (QTc) interval is a complex quantitative trait, believed to be influenced by several genetic and environmental factors. It is a strong prognostic indicator of cardiovascular mortality in patients with and without cardiac disease. More than 700 mutations have been described in 12 genes (LQT1-LQT12) involved in congenital long QT syndrome. However, the heritability (genetic contribution) of QTc interval in the general population cannot be adequately explained by these long QT syndrome genes. In order to further investigate the genetic architecture underlying QTc interval in the general population, genome-wide association studies, in which up to one million single nucleotide polymorphisms are assayed in thousands of individuals, are now being employed and have already led to the discovery of variants in seven novel loci and five loci that are known to cause congenital long or short QT syndrome. Here we show that a combined risk score using 11 of these loci explains about 10% of the heritability of QTc. Additional discovery of both common and rare variants will yield further etiological insight and accelerate clinical applications.

DRD4 and DAT1 in ADHD: Functional neurobiology to pharmacogenetics

Attention deficit/hyperactivity disorder (ADHD) is a common and potentially very impairing neuropsychiatric disorder of childhood. Statistical genetic studies of twins have shown ADHD to be highly heritable, with the combination of genes and gene by environment interactions accounting for around 80% of phenotypic variance. The initial molecular genetic studies where candidates were selected because of the efficacy of dopaminergic compounds in the treatment of ADHD were remarkably successful and provided strong evidence for the role of DRD4 and DAT1 variants in the pathogenesis of ADHD. However, the recent application of non-candidate gene strategies (eg, genome-wide association scans) has failed to identify additional genes with substantial genetic main effects, and the effects for DRD4 and DAT1 have not been replicated. This is the usual pattern observed for most other physical and mental disorders evaluated with current state-of-the-art methods. In this paper we discuss future strategies for genetic studies in ADHD, highlighting both the pitfalls and possible solutions relating to candidate gene studies, genome-wide studies, defining the phenotype, and statistical approaches.

Leg length, body proportion, and health: a review with a note on beauty

Decomposing stature into its major components is proving to be a useful strategy to assess the antecedents of disease, morbidity and death in adulthood. Human leg length (femur + tibia), sitting height (trunk length + head length) and their proportions, for example, (leg length/stature), or the sitting height ratio (sitting height/stature x 100), among others) are associated with epidemiological risk for overweight (fatness), coronary heart disease, diabetes, liver dysfunction and certain cancers. There is also wide support for the use of relative leg length as an indicator of the quality of the environment for growth during infancy, childhood and the juvenile years of development. Human beings follow a cephalo-caudal gradient of growth, the pattern of growth common to all mammals. A special feature of the human pattern is that between birth and puberty the legs grow relatively faster than other post-cranial body segments. For groups of children and youth, short stature due to relatively short legs (i.e., a high sitting height ratio) is generally a marker of an adverse environment. The development of human body proportions is the product of environmental x genomic interactions, although few if any specific genes are known. The HOXd and the short stature homeobox-containing gene (SHOX) are genomic regions that may be relevant to human body proportions. For example, one of the SHOX related disorders is Turner syndrome. However, research with non-pathological populations indicates that the environment is a more powerful force influencing leg length and body proportions than genes. Leg length and proportion are important in the perception of human beauty, which is often considered a sign of health and fertility.

The pursuit of susceptibility genes for Alzheimer's disease: progress and prospects

The recent discoveries in genome-wide association studies (GWAS) of novel susceptibility loci (CLU, CR1 and PICALM) for Alzheimer's disease (AD) have elicited considerable interest in the AD community. But what are the implications of these purely epidemiological findings for our understanding of disease etiology and patient care? In this review, we attempt to place these findings in the context of current and future AD genetics research. CLU, CR1 and PICALM support existing hypotheses about the amyloid, lipid, chaperone and chronic inflammatory pathways in AD pathogenesis. We discuss how these and future findings can be translated into efforts to ameliorate patient care by genetic profiling for risk prediction and pharmacogenetics and by guiding drug development.

Family-based genetic risk prediction of multifactorial disease

Genome-wide association studies have detected dozens of variants underlying complex diseases, although it is uncertain how often these discoveries will translate into clinically useful predictors. Here, to improve genetic risk prediction, we consider including phenotypic and genotypic information from related individuals. We develop and evaluate a family-based liability-threshold prediction model and apply it to a simulation of known Crohn's disease risk variants. We show that genotypes of a relative of known phenotype can be informative for an individual's disease risk, over and above the same locus genotyped in the individual. This approach can lead to better-calibrated estimates of disease risk, although the overall benefit for prediction is typically only very modest.

What role for genetics in the prediction of multiple sclerosis?

For most of us, the foundations of our understanding of genetics were laid by considering Mendelian diseases in which familial recurrence risks are high, and mutant alleles are both necessary and sufficient. One consequence of this deterministic teaching is that our conceptualization of genetics tends to be dominated by the notion that the genetic aspects of disease are caused by rare alleles exerting large effects. Unfortunately, the preconceptions that flow from this training are frequently erroneous and misleading in the context of common traits, where familial recurrence risks are modest, and for the most part the relevant alleles are neither rare, necessary, nor sufficient. For these common traits, the genetic architecture is far more complex, with susceptibility rather than causality resulting from the combined effects of many alleles, each exerting only a modest effect on risk. None of these alleles is sufficient to cause disease on its own, and none is essential for the development of disease. Furthermore, most are carried by large sections of the population, the vast majority of which does not develop the disease. One consequence of our innate belief in the Mendelian paradigm is that we have an inherent expectation that knowledge about the genetic basis for a disease should allow genetic testing and thereby accurate risk prediction. There is an inevitable feeling that the same should be true in complex disease, but is it?

Genetics of human height

Height is correlated with risk to certain diseases and various socio-economic outcomes. As an easy to observe and measure trait, it has been a classic paradigm in the emergence of fundamental concepts regarding inheritance and genetics. Resemblances in height between relatives suggest that 80% of height variation is under genetic control with the rest controlled by environmental factors such as diet and disease exposure. Nearly a century ago it was recognised that many genes were likely to be involved but it is only with recent advances in technology that it has become possible to comprehensively search the human genome for DNA variants that control height. About 50 genes and regions of the genome have been associated with height to date. These begin to explain the biological basis of height, its links to disease and aid our understanding of the evolution of human height. The genes discovered so far have a very small individual effect and hundreds, maybe thousands, more of even smaller effects are still lost in the genome. Despite a successful start to height gene mapping, there remain considerable theoretical, technological, and statistical hurdles to be overcome in order to unravel its full genetic basis.

Education and personalized genomics: deciphering the public's genetic health report

Where do members of the public turn to understand what genetic tests mean in terms of their own health? Now that genome-wide association studies and complete genome sequencing are widely available, the importance of education in personalized genomics cannot be overstated. Although some media have introduced the concept of genetic testing to better understand health and disease, the public's understanding of the scope and impact of genetic variation has not kept up with the pace of the science or technology. Unfortunately, the likely sources to which the public turn to for guidance - their physician and the media - are often no better prepared. We examine several venues for information, including print and online guides for both lay and health-oriented audiences, and summarize selected resources in multiple formats. We also note on the roadblocks to progress and discuss ways to remove them, as urgent action is needed to connect people with their genomes in a meaningful way.

From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of approximately 0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.