Genetically-regulated transcriptomics & copy number variation of proctitis points to altered mitochondrial and DNA repair mechanisms in individuals of European ancestry.

BACKGROUND: Proctitis is an inflammation of the rectum and may be induced by radiation treatment for cancer. The genetic heritability of developing radiotoxicity and prior role of genetic variants as being associated with side-effects of radiotherapy necessitates further investigation for underlying molecular mechanisms. In this study, we investigated gene expression regulated by genetic variants, and copy number variation in prostate cancer survivors with radiotoxicity. METHODS: We investigated proctitis as a radiotoxic endpoint in prostate cancer patients who received radiotherapy (n = 222). We analyzed the copy number variation and genetically regulated gene expression profiles of whole-blood and prostate tissue associated with proctitis. The SNP and copy number data were genotyped on Affymetrix® Genome-wide Human SNP Array 6.0. Following QC measures, the genotypes were used to obtain gene expression by leveraging GTEx, a reference dataset for gene expression association based on genotype and RNA-seq information for prostate (n = 132) and whole-blood tissue (n = 369). RESULTS: In prostate tissue, 62 genes were significantly associated with proctitis, and 98 genes in whole-blood tissue. Six genes - CABLES2, ATP6AP1L, IFIT5, ATRIP, TELO2, and PARD6G were common to both tissues. The copy number analysis identified seven regions associated with proctitis, one of which (ALG1L2) was also associated with proctitis based on transcriptomic profiles in the whole-blood tissue. The genes identified via transcriptomics and copy number variation association were further investigated for enriched pathways and gene ontology. Some of the enriched processes were DNA repair, mitochondrial apoptosis regulation, cell-to-cell signaling interaction processes for renal and urological system, and organismal injury. CONCLUSIONS: We report gene expression changes based on genetic polymorphisms. Integrating gene-network information identified these genes to relate to canonical DNA repair genes and processes. This investigation highlights genes involved in DNA repair processes and mitochondrial malfunction possibly via inflammation. Therefore, it is suggested that larger studies will provide more power to infer the extent of underlying genetic contribution for an individual's susceptibility to developing radiotoxicity.

Differentiation of Hispanic biogeographic ancestry with 80 ancestry informative markers.

Ancestry informative single nucleotide polymorphisms (SNPs) can identify biogeographic ancestry (BGA); however, population substructure and relatively recent admixture can make differentiation difficult in heterogeneous Hispanic populations. Utilizing unrelated individuals from the Genomic Origins and Admixture in Latinos dataset (GOAL, n = 160), we designed an 80 SNP panel (Setser80) that accurately depicts BGA through STRUCTURE and PCA. We compared our Setser80 to the Seldin and Kidd panels via resampling simulations, which models data based on allele frequencies. We incorporated Admixed American 1000 Genomes populations (1000 G, n = 347), into a combined populations dataset to determine robustness. Using multinomial logistic regression (MLR), we compared the 3 panels on the combined dataset and found overall MLR classification accuracies: 93.2% Setser80, 87.9% Seldin panel, 71.4% Kidd panel. Naïve Bayesian classification had similar results on the combined dataset: 91.5% Setser80, 84.7% Seldin panel, 71.1% Kidd panel. Although Peru and Mexico were absent from panel design, we achieved high classification accuracy on the combined populations for Peru (MLR = 100%, naïve Bayes = 98%), and Mexico (MLR = 90%, naïve Bayes = 83.4%) as evidence of the portability of the Setser80. Our results indicate the Setser80 SNP panel can reliably classify BGA for individuals of presumed Hispanic origin.

Development and validation of a novel multiplexed DNA analysis system, InnoTyper® 21.

We report here a novel multiplexed DNA analysis system consisting of 20 Alu markers and Amelogenin for analysis of highly degraded forensic biological samples. The key to the success of the system in obtaining results from degraded samples is the primer design yielding small amplicon size (60-125bp) for all 20 markers. The markers included in the InnoTyper® 21 system are bi-allelic, having two possible allelic states (insertion or null) and thus termed INNULs. The markers are short interspersed nuclear elements (SINEs), a category of retrotransposable elements (REs) which are non-coding genomic DNA repeat sequences, or "mobile insertion elements," comprising approximately 40% of the human genome. Alu elements are primate specific SINEs that have reached a copy number in excess of one million in the human genome, which makes these markers highly sensitive and desirable for forensic samples with extremely degraded DNA. Until now however, due to the inherent size difference associated with insertion and no insertion alleles, the use of Alu REs has not been practical for forensic applications. The novel primer design described herein has allowed the development of a multiplexed Alu system yielding fragment sizes amenable to degraded DNA samples, as frequently encountered in missing persons cases or forensic samples such as hair shafts. Although use of Alus in human identity has been studied using single marker amplification and reported before, we report for the first time development and validation of a system with multiplexed RE markers. Studies performed include PCR optimization, species specificity, sensitivity, degradation and inhibition, precision and accuracy, nonprobative samples, mixture, and population database studies. A population study using 592 samples including five populations was performed using InnoTyper 21. The data indicated the random match probability for the combination of these 20 Alu markers was greater than 1 in 3.8 million for the populations studied, indicating the greater statistical power of these autosomal nuclear DNA markers over haplotype systems typically used in such degraded samples. Results demonstrate the system is successful in obtaining results from highly degraded DNA. A sensitivity study performed demonstrated at least 95% recovery of alleles from as low as 50pg of total input DNA, and partial profiles from as low as 25pg. This study has demonstrated that the bi-allelic INNULs in the InnoTyper 21 system provide a sensitivity of detection and a power of discrimination that makes them useful for human identification of extremely degraded samples.

Paternity calculations in a di-spermy case.

In a criminal paternity case, which involved analysis of the product of conception, a rare circumstance was observed. The product of conception was triploidy, apparently due to an egg fertilized by two sperm. Since there is little guidance on how to calculate the probability of the DNA evidence given some basic hypotheses, the formulae were derived and are presented herein. These approaches could provide guidance for similar situations if they arise.

Selection pressure on human STR loci and its relevance in repeat expansion disease.

Short Tandem Repeats (STRs) comprise repeats of one to several base pairs. Because of the high mutability due to strand slippage during DNA synthesis, rapid evolutionary change in the number of repeating units directly shapes the range of repeat-number variation according to selection pressure. However, the remaining questions include: Why are STRs causing repeat expansion diseases maintained in the human population; and why are these limited to neurodegenerative diseases? By evaluating the genome-wide selection pressure on STRs using the database we constructed, we identified two different patterns of relationship in repeat-number polymorphisms between DNA and amino-acid sequences, although both patterns are evolutionary consequences of avoiding the formation of harmful long STRs. First, a mixture of degenerate codons is represented in poly-proline (poly-P) repeats. Second, long poly-glutamine (poly-Q) repeats are favored at the protein level; however, at the DNA level, STRs encoding long poly-Qs are frequently divided by synonymous SNPs. Furthermore, significant enrichments of apoptosis and neurodevelopment were biological processes found specifically in genes encoding poly-Qs with repeat polymorphism. This suggests the existence of a specific molecular function for polymorphic and/or long poly-Q stretches. Given that the poly-Qs causing expansion diseases were longer than other poly-Qs, even in healthy subjects, our results indicate that the evolutionary benefits of long and/or polymorphic poly-Q stretches outweigh the risks of long CAG repeats predisposing to pathological hyper-expansions. Molecular pathways in neurodevelopment requiring long and polymorphic poly-Q stretches may provide a clue to understanding why poly-Q expansion diseases are limited to neurodegenerative diseases.

Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry.

Atopic dermatitis (AD), food allergy, allergic rhinitis, and asthma are common atopic disorders of complex etiology. The frequently observed atopic march from early AD to asthma, allergic rhinitis, or both later in life and the extensive comorbidity of atopic disorders suggest common causal mechanisms in addition to distinct ones. Indeed, both disease-specific and shared genomic regions exist for atopic disorders. Their prevalence also varies among races; for example, AD and asthma have a higher prevalence in African Americans when compared with European Americans. Whether this disparity stems from true genetic or race-specific environmental risk factors or both is unknown. Thus far, the majority of the genetic studies on atopic diseases have used populations of European ancestry, limiting their generalizability. Large-cohort initiatives and new analytic methods, such as admixture mapping, are currently being used to address this knowledge gap. Here we discuss the unique and shared genetic risk factors for atopic disorders in the context of ancestry variations and the promise of high-throughput "-omics"-based systems biology approach in providing greater insight to deconstruct their genetic and nongenetic etiologies. Future research will also focus on deep phenotyping and genotyping of diverse racial ancestry, gene-environment, and gene-gene interactions.

Effects of the Ion PGM™ Hi-Q™ sequencing chemistry on sequence data quality.

Massively parallel sequencing (MPS) offers substantial improvements over current forensic DNA typing methodologies such as increased resolution, scalability, and throughput. The Ion PGM™ is a promising MPS platform for analysis of forensic biological evidence. The system employs a sequencing-by-synthesis chemistry on a semiconductor chip that measures a pH change due to the release of hydrogen ions as nucleotides are incorporated into the growing DNA strands. However, implementation of MPS into forensic laboratories requires a robust chemistry. Ion Torrent's Hi-Q™ Sequencing Chemistry was evaluated to determine if it could improve on the quality of the generated sequence data in association with selected genetic marker targets. The whole mitochondrial genome and the HID-Ion STR 10-plex panel were sequenced on the Ion PGM™ system with the Ion PGM™ Sequencing 400 Kit and the Ion PGM™ Hi-Q™ Sequencing Kit. Concordance, coverage, strand balance, noise, and deletion ratios were assessed in evaluating the performance of the Ion PGM™ Hi-Q™ Sequencing Kit. The results indicate that reliable, accurate data are generated and that sequencing through homopolymeric regions can be improved with the use of Ion Torrent's Hi-Q™ Sequencing Chemistry. Overall, the quality of the generated sequencing data supports the potential for use of the Ion PGM™ in forensic genetic laboratories.

Empirical testing of a 23-AIMs panel of SNPs for ancestry evaluations in four major US populations.

Ancestry informative markers (AIMs) can be used to determine population affiliation of the donors of forensic samples. In order to examine ancestry evaluations of the four major populations in the USA, 23 highly informative AIMs were identified from the International HapMap project. However, the efficacy of these 23 AIMs could not be fully evaluated in silico. In this study, these 23 SNPs were multiplexed to test their actual performance in ancestry evaluations. Genotype data were obtained from 189 individuals collected from four American populations. One SNP (rs12149261) on chromosome 16 was removed from this panel because it was duplicated on chromosome 1. The resultant 22-AIMs panel was able to empirically resolve the four major populations as in the in silico study. Eight individuals were assigned to a different group than indicated on their samples. The assignments of the 22 AIMs for these samples were consistent with AIMs results from the ForenSeq(TM) panel. No departures from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were detected for all 22 SNPs in four US populations (after removing the eight problematic samples). The principal component analysis (PCA) results indicated that 181 individuals from these populations were assigned to the expected groups. These 22 SNPs can contribute to the candidate AIMs pool for potential forensic identification purposes in major US populations.

Selection of highly informative SNP markers for population affiliation of major US populations.

Ancestry informative markers (AIMs) can be used to detect and adjust for population stratification and predict the ancestry of the source of an evidence sample. Autosomal single nucleotide polymorphisms (SNPs) are the best candidates for AIMs. It is essential to identify the most informative AIM SNPs across relevant populations. Several informativeness measures for ancestry estimation have been used for AIMs selection: absolute allele frequency differences (δ), F statistics (F ST), and informativeness for assignment measure (In). However, their efficacy has not been compared objectively, particularly for determining affiliations of major US populations. In this study, these three measures were directly compared for AIMs selection among four major US populations, i.e., African American, Caucasian, East Asian, and Hispanic American. The results showed that the F ST panel performed slightly better for population resolution based on principal component analysis (PCA) clustering than did the δ panel and both performed better than the In panel. Therefore, the 23 AIMs selected by the F ST measure were used to characterize the four major American populations. Genotype data of nine sample populations were used to evaluate the efficiency of the 23-AIMs panel. The results indicated that individuals could be correctly assigned to the major population categories. Our AIMs panel could contribute to the candidate pool of AIMs for potential forensic identification purposes.

Role of genomics in eliminating health disparities.

The Texas Center for Health Disparities, a National Institute on Minority Health and Health Disparities Center of Excellence, presents an annual conference to discuss prevention, awareness education, and ongoing research about health disparities both in Texas and among the national population. The 2014 Annual Texas Conference on Health Disparities brought together experts in research, patient care, and community outreach on the "Role of Genomics in Eliminating Health Disparities." Rapid advances in genomics and pharmacogenomics are leading the field of medicine to use genetics and genetic risk to build personalized or individualized medicine strategies. We are at a critical juncture of ensuring such rapid advances benefit diverse populations. Relatively few forums have been organized around the theme of the role of genomics in eliminating health disparities. The conference consisted of three sessions addressing "Gene-Environment Interactions and Health Disparities," "Personalized Medicine and Elimination of Health Disparities," and "Ethics and Public Policy in the Genomic Era." This article summarizes the basic science, clinical correlates, and public health data presented by the speakers.

Neonatal variables, altitude of residence and Aymara ancestry in northern Chile.

Studies performed in the Andean plateau, one of the highest inhabited areas in the world, have reported that reduced availability of oxygen is associated to fetal growth retardation and lower birth weight, which are established predictors of morbidity and mortality during the first year of life. To test this hypothesis, perinatal variables of neonates born at the Juan Noé Hospital of Arica, Chile, were analyzed in relation to altitude of residence and Aymara ancestry of their mothers. The study population comprised the offspring of 5,295 mothers born between February 2004 and August 2010. Information included birth weight, height, head circumference, gestational age, altitude of residence and socioeconomic status, and was obtained from medical records. Mother´s ancestry was assessed based on surnames which were linked to percentages of Aymara admixture estimates relying on 40 selected ancestry informative markers. After correcting for the effect of multicollinearity among predictor variables, neonates born to mothers with an increased component of Aymara ancestry showed significantly higher birth weight and height at sea level, a marginally significant (p-value 0.06) decrease of birth weight and a significant decrease of height with altitude in comparison with the offspring of mothers with low Aymara ancestry. Since observed tendencies are suggestive of a possible genetic adaptation to hypoxia of the Chilean Aymara, we discuss briefly preliminary evidence related to fetal oxygen transport, particularly polymorphisms in the promoters of the HBG1 and HBG2 genes that are modulators of HbF synthesis, obtained in this ethnic group.

Genetic and linguistic correlation of the Kra-Dai-speaking groups in Thailand.

The Kra-Dai linguistic family includes Thai and Lao as well as a great number of languages spoken by ethnic minorities in Southeast Asia. In Thailand, a dozen of other Kra-Dai languages are spoken in addition to Thai, the national language. The genetic structure of the Kra-Dai-speaking populations in Thailand has been studied extensively using uniparentally inherited markers. To extend this line of genetic investigation, this study used 15 autosomal microsatellites of 500 individuals from 11 populations, belonging to nine Kra-Dai ethnicities, namely, the Kaleung, Phu Thai, Saek, Nyo, Lao Isan, Yuan, Black Tai, Phuan and Lue. These ethnolinguistic groups are dispersed in three different geographic regions of Thailand, that is, Northern, Northeastern and Central. The results show a very low average of pairwised F(st) (0.0099), as well as no population substructure based on STRUCTURE analysis, indicating genetic homogeneity within the Kra-Dai-speaking group, possibly owing to shared linguistic ancestry. The Mantel test, an analysis of molecular variance, and the approximate Bayesian computation procedure employed to evaluate potential factors for driving genetic diversity revealed that language is the predominant factor affecting genetic variations, whereas geography is not. The result of distance-based clustering analyses and spatial analysis of molecular variance revealed genetic distinctions of some populations, reflecting the effects of genetic drift and gene flow on allele frequency within populations, in concordance with the result of R-matrix regression. The genetic and linguistic affiliations of the contemporary Kra-Dai-speaking groups are consistent with each other despite certain deviation due to various evolutionary factors that may have occurred during their migrations and resettlements.

Characterization of 114 insertion/deletion (INDEL) polymorphisms, and selection for a global INDEL panel for human identification.

Bi-Allelic Insertions and Deletions (INDELs) are a powerful set of genetic markers for Human Identification (HID). They have certain desirable features, such as low mutation rates, no stutter, and potentially small amplicon sizes that could prove effective in some circumstances. In this study, we analyzed the distribution of 114 INDELs in four North American populations (Caucasian, African American, Southwest Hispanic, and Asian) to estimate their distribution in major global populations. Of the 114 INDELs a primary panel of 38 candidate markers was selected that met the criteria of (1) a minimum allele frequency of greater than 0.20 across the populations studied; (2) general concordance with Hardy-Weinberg equilibrium (HWE) expectations; (3) relatively low FST based on the major populations; (4) physical distance between markers greater than 40 Mbp; and (5) a lack of linkage disequilibria between syntenic markers. Additionally, another 11 supplemental markers were selected for an expanded panel of 49 markers which met the above criteria, with the exception that they are separated at least by 20 Mbp. The resulting panels had Random Match Probabilities that were at least 10(-16) and 10(-19), respectively, and combined FST values of approximately 0.02. Given these findings, these INDELs should be useful for HID.

Rank-based genome-wide analysis reveals the association of ryanodine receptor-2 gene variants with childhood asthma among human populations.

BACKGROUND: The standard approach to determine unique or shared genetic factors across populations is to identify risk alleles in one population and investigate replication in others. However, since populations differ in DNA sequence information, allele frequencies, effect sizes, and linkage disequilibrium patterns, SNP association using a uniform stringent threshold on p values may not be reproducible across populations. Here, we developed rank-based methods to investigate shared or population-specific loci and pathways for childhood asthma across individuals of diverse ancestry. We performed genome-wide association studies on 859,790 SNPs genotyped in 527 affected offspring trios of European, African, and Hispanic ancestry using publically available asthma database in the Genotypes and Phenotypes database. RESULTS: Rank-based analyses showed that there are shared genetic factors for asthma across populations, more at the gene and pathway levels than at the SNP level. Although the top 1,000 SNPs were not shared, 11 genes (RYR2, PDE4D, CSMD1, CDH13, ROBO2, RBFOX1, PTPRD, NPAS3, PDE1C, SEMA5A, and CTNNA2) mapped by these SNPs were shared across populations. Ryanodine receptor 2 (RYR2, a statin response-related gene) showed the strongest association in European (p value=2.55×10(-7)) and was replicated in African (2.57×10(-4)) and Hispanic (1.18 × 10(-3)) Americans. Imputation analyses based on the 1000 Genomes Project uncovered additional RYR2 variants associated with asthma. Network and functional ontology analyses revealed that RYR2 is an integral part of dermatological or allergic disorder biological networks, specifically in the functional classes involving inflammatory, eosinophilic, and respiratory diseases. CONCLUSION: Our rank-based genome-wide analysis revealed for the first time an association of RYR2 variants with asthma and replicated previously discovered PDE4D asthma gene across human populations. The replication of top-ranked asthma genes across populations suggests that such loci are less likely to be false positives and could indicate true associations. Variants that are associated with asthma across populations could be used to identify individuals who are at high risk for asthma regardless of genetic ancestry.

Uncertainties in estimating health risks associated with exposure to ionising radiation.

The information for the present discussion on the uncertainties associated with estimation of radiation risks and probability of disease causation was assembled for the recently published NCRP Report No. 171 on this topic. This memorandum provides a timely overview of the topic, given that quantitative uncertainty analysis is the state of the art in health risk assessment and given its potential importance to developments in radiation protection. Over the past decade the increasing volume of epidemiology data and the supporting radiobiology findings have aided in the reduction of uncertainty in the risk estimates derived. However, it is equally apparent that there remain significant uncertainties related to dose assessment, low dose and low dose-rate extrapolation approaches (e.g. the selection of an appropriate dose and dose-rate effectiveness factor), the biological effectiveness where considerations of the health effects of high-LET and lower-energy low-LET radiations are required and the transfer of risks from a population for which health effects data are available to one for which such data are not available. The impact of radiation on human health has focused in recent years on cancer, although there has been a decided increase in the data for noncancer effects together with more reliable estimates of the risk following radiation exposure, even at relatively low doses (notably for cataracts and cardiovascular disease). New approaches for the estimation of hereditary risk have been developed with the use of human data whenever feasible, although the current estimates of heritable radiation effects still are based on mouse data because of an absence of effects in human studies. Uncertainties associated with estimation of these different types of health effects are discussed in a qualitative and semi-quantitative manner as appropriate. The way forward would seem to require additional epidemiological studies, especially studies of low dose and low dose-rate occupational and perhaps environmental exposures and for exposures to x rays and high-LET radiations used in medicine. The development of models for more reliably combining the epidemiology data with experimental laboratory animal and cellular data can enhance the overall risk assessment approach by providing biologically refined data to strengthen the estimation of effects at low doses as opposed to the sole use of mathematical models of epidemiological data that are primarily driven by medium/high doses. NASA's approach to radiation protection for astronauts, although a unique occupational group, indicates the possible applicability of estimates of risk and their uncertainty in a broader context for developing recommendations on: (1) dose limits for occupational exposure and exposure of members of the public; (2) criteria to limit exposures of workers and members of the public to radon and its short-lived decay products; and (3) the dosimetric quantity (effective dose) used in radiation protection.

Modeling metabolic syndrome through structural equations of metabolic traits, comorbid diseases, and GWAS variants.

OBJECTIVE: To provide a quantitative map of relationships between metabolic traits, genome-wide association studies (GWAS) variants, metabolic syndrome (MetS), and metabolic diseases through factor analysis and structural equation modeling (SEM). DESIGN AND METHODS: Cross-sectional data were collected on 1,300 individuals from an eastern Adriatic Croatian island, including 14 anthropometric and biochemical traits, and diagnoses of type 2 diabetes, coronary heart disease, gout, kidney disease, and stroke. MetS was defined based on Adult Treatment Panel III criteria. Forty widely replicated GWAS variants were genotyped. Correlated quantitative traits were reduced through factor analysis; relationships between factors, genetic variants, MetS, and metabolic diseases were determined through SEM. RESULTS: MetS was associated with obesity (P < 0.0001), dyslipidemia (P < 0.0001), glycated hemoglobin (HbA1c; P = 0.0013), hypertension (P < 0.0001), and hyperuricemia (P < 0.0001). Of metabolic diseases, MetS was associated with gout (P = 0.024), coronary heart disease was associated with HbA1c (P < 0.0001), and type 2 diabetes was associated with HbA1c (P < 0.0001) and obesity (P = 0.008). Eleven GWAS variants predicted metabolic variables, MetS, and metabolic diseases. Notably, rs7100623 in HHEX/IDE was associated with HbA1c (ß = 0.03; P < 0.0001) and type 2 diabetes (ß = 0.326; P = 0.0002), underscoring substantial impact on glucose control. CONCLUSIONS: Although MetS was associated with obesity, dyslipidemia, glucose control, hypertension, and hyperuricemia, limited ability of MetS to indicate metabolic disease risk is suggested.

Signatures of natural selection on genetic variants affecting complex human traits.

It has recently been hypothesized that polygenic adaptation, resulting in modest allele frequency changes at many loci, could be a major mechanism behind the adaptation of complex phenotypes in human populations. Here we leverage the large number of variants that have been identified through genome-wide association (GWA) studies to comprehensively study signatures of natural selection on genetic variants associated with complex traits. Using population differentiation based methods, such as FST and phylogenetic branch length analyses, we systematically examined nearly 1300 SNPs associated with 38 complex phenotypes. Instead of detecting selection signatures at individual variants, we aimed to identify combined evidence of natural selection by aggregating signals across many trait associated SNPs. Our results have revealed some general features of polygenic selection on complex traits associated variants. First, natural selection acting on standing variants associated with complex traits is a common phenomenon. Second, characteristics of selection for different polygenic traits vary both temporarily and geographically. Third, some studied traits (e.g. height and urate level) could have been the primary targets of selection, as indicated by the significant correlation between the effect sizes and the estimated strength of selection in the trait associated variants; however, for most traits, the allele frequency changes in trait associated variants might have been driven by the selection on other correlated phenotypes. Fourth, the changes in allele frequencies as a result of selection can be highly stochastic, such that, polygenic adaptation may accelerate differentiation in allele frequencies among populations, but generally does not produce predictable directional changes. Fifth, multiple mechanisms (pleiotropy, hitchhiking, etc) may act together to govern the changes in allele frequencies of genetic variants associated with complex traits.

Finding missing heritability in less significant Loci and allelic heterogeneity: genetic variation in human height.

Genome-wide association studies (GWAS) have identified many common variants associated with complex traits in human populations. Thus far, most reported variants have relatively small effects and explain only a small proportion of phenotypic variance, leading to the issues of 'missing' heritability and its explanation. Using height as an example, we examined two possible sources of missing heritability: first, variants with smaller effects whose associations with height failed to reach genome-wide significance and second, allelic heterogeneity due to the effects of multiple variants at a single locus. Using a novel analytical approach we examined allelic heterogeneity of height-associated loci selected from SNPs of different significance levels based on the summary data of the GIANT (stage 1) studies. In a sample of 1,304 individuals collected from an island population of the Adriatic coast of Croatia, we assessed the extent of height variance explained by incorporating the effects of less significant height loci and multiple effective SNPs at the same loci. Our results indicate that approximately half of the 118 loci that achieved stringent genome-wide significance (p-value<5×10(-8)) showed evidence of allelic heterogeneity. Additionally, including less significant loci (i.e., p-value<5×10(-4)) and accounting for effects of allelic heterogeneity substantially improved the variance explained in height.