Exome sequencing and the genetic basis of complex traits. Nat Genet. 2012;44:623-630. [PMID: 22641211 DOI: 10.1038/ng.2303]

The impact of population demography and selection on the genetic architecture of complex traits

Population genetic studies have found evidence for dramatic population growth in recent human history. It is unclear how this recent population growth, combined with the effects of negative natural selection, has affected patterns of deleterious variation, as well as the number, frequency, and effect sizes of mutations that contribute risk to complex traits. Because researchers are performing exome sequencing studies aimed at uncovering the role of low-frequency variants in the risk of complex traits, this topic is of critical importance. Here I use simulations under population genetic models where a proportion of the heritability of the trait is accounted for by mutations in a subset of the exome. I show that recent population growth increases the proportion of nonsynonymous variants segregating in the population, but does not affect the genetic load relative to a population that did not expand. Under a model where a mutation's effect on a trait is correlated with its effect on fitness, rare variants explain a greater portion of the additive genetic variance of the trait in a population that has recently expanded than in a population that did not recently expand. Further, when using a single-marker test, for a given false-positive rate and sample size, recent population growth decreases the expected number of significant associations with the trait relative to the number detected in a population that did not expand. However, in a model where there is no correlation between a mutation's effect on fitness and the effect on the trait, common variants account for much of the additive genetic variance, regardless of demography. Moreover, here demography does not affect the number of significant associations detected. These findings suggest recent population history may be an important factor influencing the power of association tests and in accounting for the missing heritability of certain complex traits.

High burden of private mutations due to explosive human population growth and purifying selection

Background

Recent studies have shown that human populations have experienced a complex demographic history, including a recent epoch of rapid population growth that led to an excess in the proportion of rare genetic variants in humans today. This excess can impact the burden of private mutations for each individual, defined here as the proportion of heterozygous variants in each newly sequenced individual that are novel compared to another large sample of sequenced individuals.

Results

We calculated the burden of private mutations predicted by different demographic models, and compared with empirical estimates based on data from the NHLBI Exome Sequencing Project and data from the Neutral Regions (NR) dataset. We observed a significant excess in the proportion of private mutations in the empirical data compared with models of demographic history without a recent epoch of population growth. Incorporating recent growth into the model provides a much improved fit to empirical observations. This phenomenon becomes more marked for larger sample sizes, e.g. extrapolating to a scenario in which 10,000 individuals from the same population have been sequenced with perfect accuracy, still about 1 in 400 heterozygous sites (or about 6,000 variants) at the 10,001^st individual are predicted to be novel, 18-times as predicted in the absence of recent population growth. The proportion of private mutations is additionally increased by purifying selection, which differentially affect mutations of different functional annotations.

Conclusions

The burden of private mutations for each individual, which are singletons (i.e. appearing in a single copy) in a larger sample that includes this individual, is predicted to be greatly increased by recent population growth, as well as by purifying selection. Comparison with empirical data supports that European populations have experienced recent rapid population growth, consistent with previous studies. These results have important implications to the design and analysis of sequencing-based association studies of complex human disease as they pertain to private and very rare variants. They also imply that personalized genomics will indeed have to be very personal in accounting for the large number of private mutations.

GWAS and drug targets

BACKGROUND

Genome wide association studies (GWAS) have revealed a large number of links between genome variation and complex disease. Among other benefits, it is expected that these insights will lead to new therapeutic strategies, particularly the identification of new drug targets. In this paper, we evaluate the power of GWAS studies to find drug targets by examining how many existing drug targets have been directly 'rediscovered' by this technique, and the extent to which GWAS results may be leveraged by network information to discover known and new drug targets.

RESULTS

We find that only a very small fraction of drug targets are directly detected in the relevant GWAS studies. We investigate two possible explanations for this observation. First, we find evidence of negative selection acting on drug target genes as a consequence of strong coupling with the disease phenotype, so reducing the incidence of SNPs linked to the disease. Second, we find that GWAS genes are substantially longer on average than drug targets and than all genes, suggesting there is a length related bias in GWAS results. In spite of the low direct relationship between drug targets and GWAS reported genes, we found these two sets of genes are closely coupled in the human protein network. As a consequence, machine-learning methods are able to recover known drug targets based on network context and the set of GWAS reported genes for the same disease. We show the approach is potentially useful for identifying drug repurposing opportunities.

CONCLUSIONS

Although GWA studies do not directly identify most existing drug targets, there are several reasons to expect that new targets will nevertheless be discovered using these data. Initial results on drug repurposing studies using network analysis are encouraging and suggest directions for future development.

Functional linear models for association analysis of quantitative traits

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study.

OTX2 duplication is implicated in hemifacial microsomia

Hemifacial microsomia (HFM) is the second most common facial anomaly after cleft lip and palate. The phenotype is highly variable and most cases are sporadic. We investigated the disorder in a large pedigree with five affected individuals spanning eight meioses. Whole-exome sequencing results indicated the absence of a pathogenic coding point mutation. A genome-wide survey of segmental variations identified a 1.3 Mb duplication of chromosome 14q22.3 in all affected individuals that was absent in more than 1000 chromosomes of ethnically matched controls. The duplication was absent in seven additional sporadic HFM cases, which is consistent with the known heterogeneity of the disorder. To find the critical gene in the duplicated region, we analyzed signatures of human craniofacial disease networks, mouse expression data, and predictions of dosage sensitivity. All of these approaches implicated OTX2 as the most likely causal gene. Moreover, OTX2 is a known oncogenic driver in medulloblastoma, a condition that was diagnosed in the proband during the course of the study. Our findings suggest a role for OTX2 dosage sensitivity in human craniofacial development and raise the possibility of a shared etiology between a subtype of hemifacial microsomia and medulloblastoma.

Effective filtering strategies to improve data quality from population-based whole exome sequencing studies

Background

Genotypes generated in next generation sequencing studies contain errors which can significantly impact the power to detect signals in common and rare variant association tests. These genotyping errors are not explicitly filtered by the standard GATK Variant Quality Score Recalibration (VQSR) tool and thus remain a source of errors in whole exome sequencing (WES) projects that follow GATK’s recommended best practices. Therefore, additional data filtering methods are required to effectively remove these errors before performing association analyses with complex phenotypes. Here we empirically derive thresholds for genotype and variant filters that, when used in conjunction with the VQSR tool, achieve higher data quality than when using VQSR alone.

Results

The detailed filtering strategies improve the concordance of sequenced genotypes with array genotypes from 99.33% to 99.77%; improve the percent of discordant genotypes removed from 10.5% to 69.5%; and improve the Ti/Tv ratio from 2.63 to 2.75. We also demonstrate that managing batch effects by separating samples based on different target capture and sequencing chemistry protocols results in a final data set containing 40.9% more high-quality variants. In addition, imputation is an important component of WES studies and is used to estimate common variant genotypes to generate additional markers for association analyses. As such, we demonstrate filtering methods for imputed data that improve genotype concordance from 79.3% to 99.8% while removing 99.5% of discordant genotypes.

Conclusions

The described filtering methods are advantageous for large population-based WES studies designed to identify common and rare variation associated with complex diseases. Compared to data processed through standard practices, these strategies result in substantially higher quality data for common and rare association analyses.

Jumping on the Train of Personalized Medicine: A Primer for Non- Geneticist Clinicians: Part 3. Clinical Applications in the Personalized Medicine Area

The rapid decline of sequencing costs brings hope that personal genome sequencing will become a common feature of medical practice. This series of three reviews aim to help non-geneticist clinicians to jump into the fast-moving field of personalized genetic medicine. In the first two articles, we covered the fundamental concepts of molecular genetics and the methodologies used in genetic epidemiology. In this third article, we discuss the evolution of personalized medicine and illustrate the most recent success in the fields of Mendelian and complex human diseases. We also address the challenges that currently limit the use of personalized medicine to its full potential.

Simulation of Finnish population history, guided by empirical genetic data, to assess power of rare-variant tests in Finland

Finnish samples have been extensively utilized in studying single-gene disorders, where the founder effect has clearly aided in discovery, and more recently in genome-wide association studies of complex traits, where the founder effect has had less obvious impacts. As the field starts to explore rare variants' contribution to polygenic traits, it is of great importance to characterize and confirm the Finnish founder effect in sequencing data and to assess its implications for rare-variant association studies. Here, we employ forward simulation, guided by empirical deep resequencing data, to model the genetic architecture of quantitative polygenic traits in both the general European and the Finnish populations simultaneously. We demonstrate that power of rare-variant association tests is higher in the Finnish population, especially when variants' phenotypic effects are tightly coupled with fitness effects and therefore reflect a greater contribution of rarer variants. SKAT-O, variable-threshold tests, and single-variant tests are more powerful than other rare-variant methods in the Finnish population across a range of genetic models. We also compare the relative power and efficiency of exome array genotyping to those of high-coverage exome sequencing. At a fixed cost, less expensive genotyping strategies have far greater power than sequencing; in a fixed number of samples, however, genotyping arrays miss a substantial portion of genetic signals detected in sequencing, even in the Finnish founder population. As genetic studies probe sequence variation at greater depth in more diverse populations, our simulation approach provides a framework for evaluating various study designs for gene discovery.

AP1S3 mutations are associated with pustular psoriasis and impaired Toll-like receptor 3 trafficking

Adaptor protein complex 1 (AP-1) is an evolutionary conserved heterotetramer that promotes vesicular trafficking between the trans-Golgi network and the endosomes. The knockout of most murine AP-1 complex subunits is embryonically lethal, so the identification of human disease-associated alleles has the unique potential to deliver insights into gene function. Here, we report two founder mutations (c.11T>G [p.Phe4Cys] and c.97C>T [p.Arg33Trp]) in AP1S3, the gene encoding AP-1 complex subunit σ1C, in 15 unrelated individuals with a severe autoinflammatory skin disorder known as pustular psoriasis. Because the variants are predicted to destabilize the 3D structure of the AP-1 complex, we generated AP1S3-knockdown cell lines to investigate the consequences of AP-1 deficiency in skin keratinocytes. We found that AP1S3 silencing disrupted the endosomal translocation of the innate pattern-recognition receptor TLR-3 (Toll-like receptor 3) and resulted in a marked inhibition of downstream signaling. These findings identify pustular psoriasis as an autoinflammatory phenotype caused by defects in vesicular trafficking and demonstrate a requirement of AP-1 for Toll-like receptor homeostasis.

No large-effect low-frequency coding variation found for myocardial infarction

Genome-wide association studies have identified variants, primarily common, that are associated with coronary artery disease or myocardial infarction (MI), but have not tested the majority of the low frequency and rare variation in the genome. We explored the hypothesis that previously untested low frequency (1-5% minor allele frequency) and rare (<1% minor allele frequency) coding variants are associated with MI. We genotyped 2906 MI cases and 6738 non-MI controls from Norway using the Illumina HumanExome Beadchip, allowing for direct genotyping of 85 972 polymorphic coding variants as well as 48 known GWAS SNPs. We followed-up 34 coding variants in an additional 2350 MI cases and 2318 controls from Norway. We evaluated exome array coverage in a subset of these samples using whole exome sequencing (N = 151). The exome array provided successful genotyping for an estimated 72.5% of Norwegian loss-of-function or missense variants with frequency >1% and 66.2% of variants <1% frequency observed more than once. Despite 80% power in the two-stage study (N = 14 312) to detect association with low-frequency variants with high effect sizes [odds ratio (OR) >1.86 and >1.36 for 1 and 5% frequency, respectively], we did not identify any novel genes or single variants that reached significance. This suggests that low-frequency coding variants with large effect sizes (OR >2) may not exist for MI. Larger sample sizes may identify coding variants with more moderate effects.

Genome-wide family-based linkage analysis of exome chip variants and cardiometabolic risk

Linkage analysis of complex traits has had limited success in identifying trait-influencing loci. Recently, coding variants have been implicated as the basis for some biomedical associations. We tested whether coding variants are the basis for linkage peaks of complex traits in 42 African-American (n = 596) and 90 Hispanic (n = 1,414) families in the Insulin Resistance Atherosclerosis Family Study (IRASFS) using Illumina HumanExome Beadchips. A total of 92,157 variants in African Americans (34%) and 81,559 (31%) in Hispanics were polymorphic and tested using two-point linkage and association analyses with 37 cardiometabolic phenotypes. In African Americans 77 LOD scores greater than 3 were observed. The highest LOD score was 4.91 with the APOE SNP rs7412 (MAF = 0.13) with plasma apolipoprotein B (ApoB). This SNP was associated with ApoB (P-value = 4 × 10(-19)) and accounted for 16.2% of the variance in African Americans. In Hispanic families, 104 LOD scores were greater than 3. The strongest evidence of linkage (LOD = 4.29) was with rs5882 (MAF = 0.46) in CETP with HDL. CETP variants were strongly associated with HDL (0.00049 < P-value <4.6 × 10(-12)), accounting for up to 4.5% of the variance. These loci have previously been shown to have effects on the biomedical traits evaluated here. Thus, evidence of strong linkage in this genome wide survey of primarily coding variants was uncommon. Loci with strong evidence of linkage was characterized by large contributions to the variance, and, in these cases, are common variants. Less compelling evidence of linkage and association was observed with additional loci that may require larger family sets to confirm.

Association claims in the sequencing era

Since the completion of the Human Genome Project, the field of human genetics has been in great flux, largely due to technological advances in studying DNA sequence variation. Although community-wide adoption of statistical standards was key to the success of genome-wide association studies, similar standards have not yet been globally applied to the processing and interpretation of sequencing data. It has proven particularly challenging to pinpoint unequivocally disease variants in sequencing studies of polygenic traits. Here, we comment on a number of factors that may contribute to irreproducible claims of association in scientific literature and discuss possible steps that we can take towards cultural change.

The effects of a MAP2K5 microRNA target site SNP on risk for anxiety and depressive disorders

Functional variants that contribute to genomewide association study (GWAS) signals are difficult to identify. MicroRNAs could contribute to some of these gene-trait relationships. We compiled a set of GWAS trait gene SNPs that were predicted to affect microRNA regulation of mRNA. Trait associations were tested in a sample of 6725 European-American (EA) and African-American (AA) subjects that were interviewed using the polydiagnostic SSADDA to diagnose major psychiatric disorders. A predicted miR-330-3p target site SNP (rs41305272) in mitogen-activated protein kinase kinase 5 (MAP2K5) mRNA was in LD (d' = 1.0, r(2) = 0.02) with a reported GWAS-identified variant for restless legs syndrome (RLS), a disorder frequently comorbid with anxiety and depression, possibly because of a shared pathophysiology. We examined the SNP's association with mood and anxiety-related disorders. Rs41305272 was associated with agoraphobia (Ag) in EAs (odds ratio [OR] = 1.95, P = 0.007; 195 cases) and AAs (OR = 3.2, P = 0.03; 148 cases) and major depressive disorder (MDD) in AAs (OR = 2.64, P = 0.01; 427 cases), but not EAs (465 cases). Rs41305272*T carrier frequency was correlated with the number of anxiety and depressive disorders diagnosed per subject. RLS was not evaluated in our subjects. Predicted miR-330-3p target genes were enriched in pathways relevant to psychiatric disorders. These findings suggest that microRNA target site information may be useful in the analysis of GWAS signals for complex traits. MiR-330-3p and MAP2K5 are potentially important contributors to mood and anxiety-related traits. With support from additional studies, these findings could add to the large number of risk genes identified through association to medical disorders that have primary psychiatric effects.

A polygenic burden of rare disruptive mutations in schizophrenia

Schizophrenia is a common disease with a complex aetiology, probably involving multiple and heterogeneous genetic factors. Here, by analysing the exome sequences of 2,536 schizophrenia cases and 2,543 controls, we demonstrate a polygenic burden primarily arising from rare (less than 1 in 10,000), disruptive mutations distributed across many genes. Particularly enriched gene sets include the voltage-gated calcium ion channel and the signalling complex formed by the activity-regulated cytoskeleton-associated scaffold protein (ARC) of the postsynaptic density, sets previously implicated by genome-wide association and copy-number variation studies. Similar to reports in autism, targets of the fragile X mental retardation protein (FMRP, product of FMR1) are enriched for case mutations. No individual gene-based test achieves significance after correction for multiple testing and we do not detect any alleles of moderately low frequency (approximately 0.5 to 1 per cent) and moderately large effect. Taken together, these data suggest that population-based exome sequencing can discover risk alleles and complements established gene-mapping paradigms in neuropsychiatric disease.

Integration of sequence data from a Consanguineous family with genetic data from an outbred population identifies PLB1 as a candidate rheumatoid arthritis risk gene

Integrating genetic data from families with highly penetrant forms of disease together with genetic data from outbred populations represents a promising strategy to uncover the complete frequency spectrum of risk alleles for complex traits such as rheumatoid arthritis (RA). Here, we demonstrate that rare, low-frequency and common alleles at one gene locus, phospholipase B1 (PLB1), might contribute to risk of RA in a 4-generation consanguineous pedigree (Middle Eastern ancestry) and also in unrelated individuals from the general population (European ancestry). Through identity-by-descent (IBD) mapping and whole-exome sequencing, we identified a non-synonymous c.2263G>C (p.G755R) mutation at the PLB1 gene on 2q23, which significantly co-segregated with RA in family members with a dominant mode of inheritance (P = 0.009). We further evaluated PLB1 variants and risk of RA using a GWAS meta-analysis of 8,875 RA cases and 29,367 controls of European ancestry. We identified significant contributions of two independent non-coding variants near PLB1 with risk of RA (rs116018341 [MAF = 0.042] and rs116541814 [MAF = 0.021], combined P = 3.2×10⁻⁶). Finally, we performed deep exon sequencing of PLB1 in 1,088 RA cases and 1,088 controls (European ancestry), and identified suggestive dispersion of rare protein-coding variant frequencies between cases and controls (P = 0.049 for C-alpha test and P = 0.055 for SKAT). Together, these data suggest that PLB1 is a candidate risk gene for RA. Future studies to characterize the full spectrum of genetic risk in the PLB1 genetic locus are warranted.

Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks

Low-frequency coding DNA sequence variants in the proprotein convertase subtilisin/kexin type 9 gene (PCSK9) lower plasma low-density lipoprotein cholesterol (LDL-C), protect against risk of coronary heart disease (CHD), and have prompted the development of a new class of therapeutics. It is uncertain whether the PCSK9 example represents a paradigm or an isolated exception. We used the "Exome Array" to genotype >200,000 low-frequency and rare coding sequence variants across the genome in 56,538 individuals (42,208 European ancestry [EA] and 14,330 African ancestry [AA]) and tested these variants for association with LDL-C, high-density lipoprotein cholesterol (HDL-C), and triglycerides. Although we did not identify new genes associated with LDL-C, we did identify four low-frequency (frequencies between 0.1% and 2%) variants (ANGPTL8 rs145464906 [c.361C>T; p.Gln121*], PAFAH1B2 rs186808413 [c.482C>T; p.Ser161Leu], COL18A1 rs114139997 [c.331G>A; p.Gly111Arg], and PCSK7 rs142953140 [c.1511G>A; p.Arg504His]) with large effects on HDL-C and/or triglycerides. None of these four variants was associated with risk for CHD, suggesting that examples of low-frequency coding variants with robust effects on both lipids and CHD will be limited.

Meta-analysis of gene-level tests for rare variant association

The majority of reported complex disease associations for common genetic variants have been identified through meta-analysis, a powerful approach that enables the use of large sample sizes while protecting against common artifacts due to population structure and repeated small-sample analyses sharing individual-level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the focus of analysis. Here we propose and evaluate new approaches for performing meta-analysis of rare variant association tests, including burden tests, weighted burden tests, variable-threshold tests and tests that allow variants with opposite effects to be grouped together. We show that our approach retains useful features from single-variant meta-analysis approaches and demonstrate its use in a study of blood lipid levels in ∼18,500 individuals genotyped with exome arrays.

Germline mutations in oncogene-induced senescence pathways are associated with multiple sessile serrated adenomas

BACKGROUND & AIMS

Little is known about the genetic factors that contribute to the development of sessile serrated adenomas (SSAs). SSAs contain somatic mutations in BRAF or KRAS early in development. However, evidence from humans and mouse models indicates that these mutations result in oncogene-induced senescence (OIS) of intestinal crypt cells. Progression to serrated neoplasia requires cells to escape OIS via inactivation of tumor suppressor pathways. We investigated whether subjects with multiple SSAs carry germline loss-of function mutations (nonsense and splice site) in genes that regulate OIS: the p16-Rb and ATM-ATR DNA damage response pathways.

METHODS

Through a bioinformatic analysis of the literature, we identified a set of genes that function at the main nodes of the p16-Rb and ATM-ATR DNA damage response pathways. We performed whole-exome sequencing of 20 unrelated subjects with multiple SSAs; most had features of serrated polyposis. We compared sequences with those from 4300 subjects matched for ethnicity (controls). We also used an integrative genomics approach to identify additional genes involved in senescence mechanisms.

RESULTS

We identified mutations in genes that regulate senescence (ATM, PIF1, TELO2,XAF1, and RBL1) in 5 of 20 subjects with multiple SSAs (odds ratio, 3.0; 95% confidence interval, 0.9–8.9; P =.04). In 2 subjects,we found nonsense mutations in RNF43, indicating that it is also associated with multiple serrated polyps (odds ratio, 460; 95% confidence interval, 23.1–16,384; P = 6.8 x 10(-5)). In knockdown experiments with pancreatic duct cells exposed to UV light, RNF43 appeared to function as a regulator of ATMATRDNA damage response.

CONCLUSIONS

We associated germline loss-of-function variants in genes that regulate senescence pathways with the development of multiple SSAs.We identified RNF43 as a regulator of the DNA damage response and associated nonsense variants in this gene with a high risk of developing SSAs.

A method to incorporate prior information into score test for genetic association studies

Background

The interest of the scientific community in investigating the impact of rare variants on complex traits has stimulated the development of novel statistical methodologies for association studies. The fact that many of the recently proposed methods for association studies suffer from low power to identify a genetic association motivates the incorporation of prior knowledge into statistical tests.

Results

In this article we propose a methodology to incorporate prior information into the region-based score test. Within our framework prior information is used to partition variants within a region into several groups, following which asymptotically independent group statistics are constructed and then combined into a global test statistic. Under the null hypothesis the distribution of our test statistic has lower degrees of freedom compared with those of the region-based score statistic. Theoretical power comparison, population genetics simulations and results from analysis of the GAW17 sequencing data set suggest that under some scenarios our method may perform as well as or outperform the score test and other competing methods.

Conclusions

An approach which uses prior information to improve the power of the region-based score test is proposed. Theoretical power comparison, population genetics simulations and the results of GAW17 data analysis showed that for some scenarios power of our method is on the level with or higher than those of the score test and other methods.

Who are the Okinawans? Ancestry, genome diversity, and implications for the genetic study of human longevity from a geographically isolated population

Isolated populations have advantages for genetic studies of longevity from decreased haplotype diversity and long-range linkage disequilibrium. This permits smaller sample sizes without loss of power, among other utilities. Little is known about the genome of the Okinawans, a potential population isolate, recognized for longevity. Therefore, we assessed genetic diversity, structure, and admixture in Okinawans, and compared this with Caucasians, Chinese, Japanese, and Africans from HapMap II, genotyped on the same Affymetrix GeneChip Human Mapping 500K array. Principal component analysis, haplotype coverage, and linkage disequilibrium decay revealed a distinct Okinawan genome-more homogeneity, less haplotype diversity, and longer range linkage disequilibrium. Population structure and admixture analyses utilizing 52 global reference populations from the Human Genome Diversity Cell Line Panel demonstrated that Okinawans clustered almost exclusively with East Asians. Sibling relative risk (λs) analysis revealed that siblings of Okinawan centenarians have 3.11 times (females) and 3.77 times (males) more likelihood of centenarianism. These findings suggest that Okinawans are genetically distinct and share several characteristics of a population isolate, which are prone to develop extreme phenotypes (eg, longevity) from genetic drift, natural selection, and population bottlenecks. These data support further exploration of genetic influence on longevity in the Okinawans.

Searching for missing heritability: designing rare variant association studies

Genetic studies have revealed thousands of loci predisposing to hundreds of human diseases and traits, revealing important biological pathways and defining novel therapeutic hypotheses. However, the genes discovered to date typically explain less than half of the apparent heritability. Because efforts have largely focused on common genetic variants, one hypothesis is that much of the missing heritability is due to rare genetic variants. Studies of common variants are typically referred to as genomewide association studies, whereas studies of rare variants are often simply called sequencing studies. Because they are actually closely related, we use the terms common variant association study (CVAS) and rare variant association study (RVAS). In this paper, we outline the similarities and differences between RVAS and CVAS and describe a conceptual framework for the design of RVAS. We apply the framework to address key questions about the sample sizes needed to detect association, the relative merits of testing disruptive alleles vs. missense alleles, frequency thresholds for filtering alleles, the value of predictors of the functional impact of missense alleles, the potential utility of isolated populations, the value of gene-set analysis, and the utility of de novo mutations. The optimal design depends critically on the selection coefficient against deleterious alleles and thus varies across genes. The analysis shows that common variant and rare variant studies require similarly large sample collections. In particular, a well-powered RVAS should involve discovery sets with at least 25,000 cases, together with a substantial replication set.

Rare variant association testing by adaptive combination of P-values

With the development of next-generation sequencing technology, there is a great demand for powerful statistical methods to detect rare variants (minor allele frequencies (MAFs)<1%) associated with diseases. Testing for each variant site individually is known to be underpowered, and therefore many methods have been proposed to test for the association of a group of variants with phenotypes, by pooling signals of the variants in a chromosomal region. However, this pooling strategy inevitably leads to the inclusion of a large proportion of neutral variants, which may compromise the power of association tests. To address this issue, we extend the -MidP method (Cheung et al., 2012, Genet Epidemiol 36: 675–685) and propose an approach (named ‘adaptive combination of P-values for rare variant association testing’, abbreviated as ‘ADA’) that adaptively combines per-site P-values with the weights based on MAFs. Before combining P-values, we first imposed a truncation threshold upon the per-site P-values, to guard against the noise caused by the inclusion of neutral variants. This ADA method is shown to outperform popular burden tests and non-burden tests under many scenarios. ADA is recommended for next-generation sequencing data analysis where many neutral variants may be included in a functional region.

Neutral genomic regions refine models of recent rapid human population growth

Human populations have experienced dramatic growth since the Neolithic revolution. Recent studies that sequenced a very large number of individuals observed an extreme excess of rare variants and provided clear evidence of recent rapid growth in effective population size, although estimates have varied greatly among studies. All these studies were based on protein-coding genes, in which variants are also impacted by natural selection. In this study, we introduce targeted sequencing data for studying recent human history with minimal confounding by natural selection. We sequenced loci far from genes that meet a wide array of additional criteria such that mutations in these loci are putatively neutral. As population structure also skews allele frequencies, we sequenced 500 individuals of relatively homogeneous ancestry by first analyzing the population structure of 9,716 European Americans. We used very high coverage sequencing to reliably call rare variants and fit an extensive array of models of recent European demographic history to the site frequency spectrum. The best-fit model estimates ∼ 3.4% growth per generation during the last ∼ 140 generations, resulting in a population size increase of two orders of magnitude. This model fits the data very well, largely due to our observation that assumptions of more ancient demography can impact estimates of recent growth. This observation and results also shed light on the discrepancy in demographic estimates among recent studies.

Genomic medicine: a decade of successes, challenges, and opportunities

Genomic medicine--an aspirational term 10 years ago--is gaining momentum across the entire clinical continuum from risk assessment in healthy individuals to genome-guided treatment in patients with complex diseases. We review the latest achievements in genome research and their impact on medicine, primarily in the past decade. In most cases, genomic medicine tools remain in the realm of research, but some tools are crossing over into clinical application, where they have the potential to markedly alter the clinical care of patients. In this State of the Art Review, we highlight notable examples including the use of next-generation sequencing in cancer pharmacogenomics, in the diagnosis of rare disorders, and in the tracking of infectious disease outbreaks. We also discuss progress in dissecting the molecular basis of common diseases, the role of the host microbiome, the identification of drug response biomarkers, and the repurposing of drugs. The significant challenges of implementing genomic medicine are examined, along with the innovative solutions being sought. These challenges include the difficulty in establishing clinical validity and utility of tests, how to increase awareness and promote their uptake by clinicians, a changing regulatory and coverage landscape, the need for education, and addressing the ethical aspects of genomics for patients and society. Finally, we consider the future of genomics in medicine and offer a glimpse of the forces shaping genomic medicine, such as fundamental shifts in how we define disease, how medicine is delivered to patients, and how consumers are managing their own health and affecting change.

Assessing the necessity of confirmatory testing for exome-sequencing results in a clinical molecular diagnostic laboratory

Purpose

Sanger sequencing is currently considered the gold standard methodology for clinical molecular diagnostic testing. However, next generation sequencing (NGS) has already emerged as a much more efficient means to identify genetic variants within gene panels, the exome, or the genome. We sought to assess the accuracy of NGS variant identification in our clinical genomics laboratory with the goal of establishing a quality score threshold for confirmatory Sanger-based testing.

Methods

Confirmation data for reported results from 144 sequential clinical exome sequencing cases (94 unique variants) and an additional set of 16 variants from comparable research samples were analyzed.

Results

103 of 110 total SNVs analyzed had a quality score ≥Q500, 103 (100%) of which were confirmed by Sanger sequencing. Of the remaining 7 variants with quality scores <Q500, 6 were confirmed by Sanger sequencing (85%).

Conclusions

For single nucleotide variants, we predict we will be able to reduce our Sanger confirmation workload going forward by 70–80%. This serves as a proof of principle that as long as sufficient validation and quality control measures are implemented, the volume of Sanger confirmation can be reduced, alleviating a significant amount of the labor and cost burden on clinical laboratories wishing to utilize NGS technology. However, Sanger confirmation of low quality single nucleotide variants and all indels (insertions or deletions less than 10 bp) remains necessary at this time in our laboratory.

Analysis of rare variant population structure in Europeans explains differential stratification of gene-based tests

There is substantial interest in the role of rare genetic variants in the etiology of complex human diseases. Several gene-based tests have been developed to simultaneously analyze multiple rare variants for association with phenotypic traits. The tests can largely be partitioned into two classes - 'burden' tests and 'joint' tests - based on how they accumulate evidence of association across sites. We used the empirical joint site frequency spectra of rare, nonsynonymous variation from a large multi-population sequencing study to explore the effect of realistic rare variant population structure on gene-based tests. We observed an important difference between the two test classes: their susceptibility to population stratification. Focusing on European samples, we found that joint tests, which allow variants to have opposite directions of effect, consistently showed higher levels of P-value inflation than burden tests. We determined that the differential stratification was caused by two specific patterns in the interpopulation distribution of rare variants, each correlating with inflation in one of the test classes. The pattern that inflates joint tests is more prevalent in real data, explaining the higher levels of inflation in these tests. Furthermore, we show that the different sources of inflation between tests lead to heterogeneous responses to genomic control correction and the number of variants analyzed. Our results indicate that care must be taken when interpreting joint and burden analyses of the same set of rare variants, in particular, to avoid mistaking inflated P-values in joint tests for stronger signals of true associations.

Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data

Statistical genetics is undergoing the same transition to big data that all branches of applied statistics are experiencing. With the advent of inexpensive DNA sequencing, the transition is only accelerating. This brief review highlights some modern techniques with recent successes in statistical genetics. These include: (a) lasso penalized regression and association mapping, (b) ethnic admixture estimation, (c) matrix completion for genotype and sequence data, (d) the fused lasso and copy number variation, (e) haplotyping, (f) estimation of relatedness, (g) variance components models, and (h) rare variant testing. For more than a century, genetics has been both a driver and beneficiary of statistical theory and practice. This symbiotic relationship will persist for the foreseeable future.

Association study between SNP rs150689919 in the DNA demethylation gene, TET1, and Parkinson's disease in Chinese Han population

Background

Recent studies suggest that epigenetic factors may play an important role in the pathogenesis of Parkinson’s disease (PD). In our previous work, we sequenced the exomes of sixteen patients from eight Chinese PD families using whole exome sequencing technology, consequently three patients from different pedigrees were found sharing the variant c.1460C > T (rs150689919) in the coding region of the Tet methyl cytosine dioxygenase 1 (TET1) gene.

Methods

In order to evaluate the possible association between sporadic PD and the single nucleotide polymorphism (SNP) rs150689919 in TET1, a case–control cohort study was conducted in 514 sporadic PD patients and 529 normal controls. Genotyping was determined by PCR and direct sequencing. Statistical significance was analyzed by the Chi-squared test.

Results

There was no statistical significance in TET1 rs150689919 genotype or allele frequencies between the PD cases and healthy controls, even after being stratified by gender and age at onset.

Conclusions

Our findings suggest that rs150689919 in TET1 may not be associated with PD in Chinese population. However, due to the limited data in this study, replication studies in larger sample and other populations are required.

Whole-exome sequencing of 2,000 Danish individuals and the role of rare coding variants in type 2 diabetes

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m(2) and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk.

Early spinal cord and brainstem involvement in infantile Leigh syndrome possibly caused by a novel variant

Leigh syndrome, due to a dysfunction of mitochondrial energy metabolism, is a genetically heterogeneous and progressive neurologic disorder that usually occurs in infancy and childhood. Its clinical presentation and neuroimaging findings can be variable, especially early in the course of the disease. This report presents a patient with infantile Leigh syndrome who had atypical radiologic findings on serial neuroimaging studies with early and severe involvement of the cervical spinal cord and brainstem and injury to the thalami and basal ganglia occurring only late in the clinical course. Postmortem microscopic examination supported this timing of injury within the central nervous system. In addition, mitochondrial deoxyribonucleic acid sequencing showed a novel homoplasmic variant that could be responsible for this unique lethal form of Leigh syndrome.

Universality and predictability in molecular quantitative genetics

Molecular traits, such as gene expression levels or protein binding affinities, are increasingly accessible to quantitative measurement by modern high-throughput techniques. Such traits measure molecular functions and, from an evolutionary point of view, are important as targets of natural selection. We review recent developments in evolutionary theory and experiments that are expected to become building blocks of a quantitative genetics of molecular traits. We focus on universal evolutionary characteristics: these are largely independent of a trait's genetic basis, which is often at least partially unknown. We show that universal measurements can be used to infer selection on a quantitative trait, which determines its evolutionary mode of conservation or adaptation. Furthermore, universality is closely linked to predictability of trait evolution across lineages. We argue that universal trait statistics extends over a range of cellular scales and opens new avenues of quantitative evolutionary systems biology.

Genetics of psychiatric disorders in the GWAS era: an update on schizophrenia

The influence of genetic factors in the development of schizophrenia has been convincingly demonstrated by family, twin, and adoption studies. The statistical construct of heritability is generally used for estimating the liability due to genetic factors. Heritability estimates for schizophrenia are reported to be between 60 and 80 %. Due to the technical achievements in whole genome-wide association studies, dissection of the underlying genetic factors was intensified recently, resulting in the conclusion that schizophrenia is essentially a polygenic, complex disorder. Most likely more than 100 genes, each with small effect size, contribute to disease risk. A most recent multi-stage genome-wide association study (Ripke et al. in Nat Genet 2013) identified 22 risk loci and estimated that 8,300 independent single-nucleotide polymorphisms contributed to the risk accounting collectively for 32 % in liability. In addition to this polygenic, complex inheritance, there is also strong indication that in some patients a deletion or insertion of a larger chromosomal region [so-called copy number variation (CNV)] might play a crucial role in pathogenesis. This could be specifically important in sporadic cases with schizophrenia, since a higher frequency of de novo mutations has been associated with these CNVs. Further studies, combining much larger sample sizes as well as application of newer technology, such as deep sequencing technologies will be necessary in order to obtain a more comprehensive understanding of the genetic foundations of schizophrenia.

Impact of exome sequencing in inflammatory bowel disease

Approaches to understanding the genetic contribution to inflammatory bowel disease (IBD) have continuously evolved from family- and population-based epidemiology, to linkage analysis, and most recently, to genome-wide association studies (GWAS). The next stage in this evolution seems to be the sequencing of the exome, that is, the regions of the human genome which encode proteins. The GWAS approach has been very fruitful in identifying at least 163 loci as being associated with IBD, and now, exome sequencing promises to take our genetic understanding to the next level. In this review we will discuss the possible contributions that can be made by an exome sequencing approach both at the individual patient level to aid with disease diagnosis and future therapies, as well as in advancing knowledge of the pathogenesis of IBD.

Genetic architecture of retinal and macular degenerative diseases: the promise and challenges of next-generation sequencing

Inherited retinal degenerative diseases (RDDs) display wide variation in their mode of inheritance, underlying genetic defects, age of onset, and phenotypic severity. Molecular mechanisms have not been delineated for many retinal diseases, and treatment options are limited. In most instances, genotype-phenotype correlations have not been elucidated because of extensive clinical and genetic heterogeneity. Next-generation sequencing (NGS) methods, including exome, genome, transcriptome and epigenome sequencing, provide novel avenues towards achieving comprehensive understanding of the genetic architecture of RDDs. Whole-exome sequencing (WES) has already revealed several new RDD genes, whereas RNA-Seq and ChIP-Seq analyses are expected to uncover novel aspects of gene regulation and biological networks that are involved in retinal development, aging and disease. In this review, we focus on the genetic characterization of retinal and macular degeneration using NGS technology and discuss the basic framework for further investigations. We also examine the challenges of NGS application in clinical diagnosis and management.

Convergent functional genomics of psychiatric disorders

Genetic and gene expression studies, in humans and animal models of psychiatric and other medical disorders, are becoming increasingly integrated. Particularly for genomics, the convergence and integration of data across species, experimental modalities and technical platforms is providing a fit-to-disease way of extracting reproducible and biologically important signal, in contrast to the fit-to-cohort effect and limited reproducibility of human genetic analyses alone. With the advent of whole-genome sequencing and the realization that a major portion of the non-coding genome may contain regulatory variants, Convergent Functional Genomics (CFG) approaches are going to be essential to identify disease-relevant signal from the tremendous polymorphic variation present in the general population. Such work in psychiatry can provide an example of how to address other genetically complex disorders, and in turn will benefit by incorporating concepts from other areas, such as cancer, cardiovascular diseases, and diabetes.

A practical method to detect SNVs and indels from whole genome and exome sequencing data

The recent development of massively parallel sequencing technology has allowed the creation of comprehensive catalogs of genetic variation. However, due to the relatively high sequencing error rate for short read sequence data, sophisticated analysis methods are required to obtain high-quality variant calls. Here, we developed a probabilistic multinomial method for the detection of single nucleotide variants (SNVs) as well as short insertions and deletions (indels) in whole genome sequencing (WGS) and whole exome sequencing (WES) data for single sample calling. Evaluation with DNA genotyping arrays revealed a concordance rate of 99.98% for WGS calls and 99.99% for WES calls. Sanger sequencing of the discordant calls determined the false positive and false negative rates for the WGS (0.0068% and 0.17%) and WES (0.0036% and 0.0084%) datasets. Furthermore, short indels were identified with high accuracy (WGS: 94.7%, WES: 97.3%). We believe our method can contribute to the greater understanding of human diseases.

Applications of high-throughput DNA sequencing to benign hematology

The development of novel technologies for high-throughput DNA sequencing is having a major impact on our ability to measure and define normal and pathologic variation in humans. This review discusses advances in DNA sequencing that have been applied to benign hematologic disorders, including those affecting the red blood cell, the neutrophil, and other white blood cell lineages. Relevant examples of how these approaches have been used for disease diagnosis, gene discovery, and studying complex traits are provided. High-throughput DNA sequencing technology holds significant promise for impacting clinical care. This includes development of improved disease detection and diagnosis, better understanding of disease progression and stratification of risk of disease-specific complications, and development of improved therapeutic strategies, particularly patient-specific pharmacogenomics-based therapy, with monitoring of therapy by genomic biomarkers.

Using false discovery rates to benchmark SNP-callers in next-generation sequencing projects

Sequence alignments form the basis for many comparative and population genomic studies. Alignment tools provide a range of accuracies dependent on the divergence between the sequences and the alignment methods. Despite widespread use, there is no standard method for assessing the accuracy of a dataset and alignment strategy after resequencing. We present a framework and tool for determining the overall accuracies of an input read dataset, alignment and SNP-calling method providing an isolate in that dataset has a corresponding, or closely related reference sequence available. In addition to this tool for comparing False Discovery Rates (FDR), we include a method for determining homozygous and heterozygous positions from an alignment using binomial probabilities for an expected error rate. We benchmark this method against other SNP callers using our FDR method with three fungal genomes, finding that it was able achieve a high level of accuracy. These tools are available at http://cfdr.sourceforge.net/.

Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes

De novo mutations affect risk for many diseases and disorders, especially those with early-onset. An example is autism spectrum disorders (ASD). Four recent whole-exome sequencing (WES) studies of ASD families revealed a handful of novel risk genes, based on independent de novo loss-of-function (LoF) mutations falling in the same gene, and found that de novo LoF mutations occurred at a twofold higher rate than expected by chance. However successful these studies were, they used only a small fraction of the data, excluding other types of de novo mutations and inherited rare variants. Moreover, such analyses cannot readily incorporate data from case-control studies. An important research challenge in gene discovery, therefore, is to develop statistical methods that accommodate a broader class of rare variation. We develop methods that can incorporate WES data regarding de novo mutations, inherited variants present, and variants identified within cases and controls. TADA, for Transmission And De novo Association, integrates these data by a gene-based likelihood model involving parameters for allele frequencies and gene-specific penetrances. Inference is based on a Hierarchical Bayes strategy that borrows information across all genes to infer parameters that would be difficult to estimate for individual genes. In addition to theoretical development we validated TADA using realistic simulations mimicking rare, large-effect mutations affecting risk for ASD and show it has dramatically better power than other common methods of analysis. Thus TADA's integration of various kinds of WES data can be a highly effective means of identifying novel risk genes. Indeed, application of TADA to WES data from subjects with ASD and their families, as well as from a study of ASD subjects and controls, revealed several novel and promising ASD candidate genes with strong statistical support.

The genetic underpinnings of autism spectrum disorder (ASD) have proven difficult to determine, despite a wealth of evidence for genetic causes and ongoing effort to identify genes. Recently investigators sequenced the coding regions of the genomes from ASD children along with their unaffected parents (ASD trios) and identified numerous new candidate genes by pinpointing spontaneously occurring (de novo) mutations in the affected offspring. A gene with a severe (de novo) mutation observed in more than one individual is immediately implicated in ASD; however, the majority of severe mutations are observed only once per gene. These genes create a short list of candidates, and our results suggest about 50% are true risk genes. To strengthen our inferences, we develop a novel statistical method (TADA) that utilizes inherited variation transmitted to affected offspring in conjunction with (de novo) mutations to identify risk genes. Through simulations we show that TADA dramatically increases power. We apply this approach to nearly 1000 ASD trios and 2000 subjects from a case-control study and identify several promising genes. Through simulations and application we show that TADA's integration of sequencing data can be a highly effective means of identifying risk genes.

Neutral and weakly nonneutral sequence variants may define individuality

Large-scale computational analyses of the growing wealth of genome-variation data consistently tell two distinct stories. The first is expected: coding variants reported in disease-related databases significantly alter the function of affected proteins. The second is surprising: the genomes of healthy individuals appear to carry many variants that are predicted to have some effect on function. As long as the complete experimental analysis of all human genome variants remains impossible, computational methods, such as PolyPhen, SNAP, and SIFT, might provide important insights. These methods capture the effects of particular variants very well and can highlight trends in populations of variants. Diseases are, arguably, extreme phenotypic variations and are often attributable to one or a few severely functionally disruptive variants. Our findings suggest a genomic basis of the different nondisease phenotypes. Prediction methods indicate that variants in seemingly healthy individuals tend to be neutral or weakly disruptive for protein molecular function. These variant effects are predicted to be largely either experimentally undetectable or are not deemed significant enough to be published. This may suggest that nondisease phenotypes arise through combinations of many variants whose effects are weakly nonneutral (damaging or enhancing) to the molecular protein function but fall within the wild-type range of overall physiological function.

News from the protein mutability landscape

Some mutations of protein residues matter more than others, and these are often conserved evolutionarily. The explosion of deep sequencing and genotyping increasingly requires the distinction between effect and neutral variants. The simplest approach predicts all mutations of conserved residues to have an effect; however, this works poorly, at best. Many computational tools that are optimized to predict the impact of point mutations provide more detail. Here, we expand the perspective from the view of single variants to the level of sketching the entire mutability landscape. This landscape is defined by the impact of substituting every residue at each position in a protein by each of the 19 non-native amino acids. We review some of the powerful conclusions about protein function, stability and their robustness to mutation that can be drawn from such an analysis. Large-scale experimental and computational mutagenesis experiments are increasingly furthering our understanding of protein function and of the genotype-phenotype associations. We also discuss how these can be used to improve predictions of protein function and pathogenicity of missense variants.

Molecular genetic testing and the future of clinical genomics

Genomic technologies are reaching the point of being able to detect genetic variation in patients at high accuracy and reduced cost, offering the promise of fundamentally altering medicine. Still, although scientists and policy advisers grapple with how to interpret and how to handle the onslaught and ambiguity of genome-wide data, established and well-validated molecular technologies continue to have an important role, especially in regions of the world that have more limited access to next-generation sequencing capabilities. Here we review the range of methods currently available in a clinical setting as well as emerging approaches in clinical molecular diagnostics. In parallel, we outline implementation challenges that will be necessary to address to ensure the future of genetic medicine.

A systematic evaluation of hybridization-based mouse exome capture system

BACKGROUND

Exome sequencing is increasingly used to search for phenotypically-relevant sequence variants in the mouse genome. All of the current hybridization-based mouse exome capture systems are designed based on the genome reference sequences of the C57BL/6 J strain. Given that the substantial sequence divergence exists between C57BL/6 J and other distantly-related strains, the impact of sequence divergence on the efficiency of such capture systems needs to be systematically evaluated before they can be widely applied to the study of those strains.

RESULTS

Using the Agilent SureSelect mouse exome capture system, we performed exome sequencing on F1 generation hybrid mice that were derived by crossing two divergent strains, C57BL/6 J and SPRET/EiJ. Our results showed that the C57BL/6 J-based probes captured the sequences derived from C57BL/6 J alleles more efficiently and that the bias was higher for the target regions with greater sequence divergence. At low sequencing depths, the bias also affected the efficiency of variant detection. However, the effects became negligible when sufficient sequencing depth was achieved.

CONCLUSION

Sufficient sequence depth needs to be planned to match the sequence divergence between C57BL/6 J and the strain to be studied, when the C57BL/6 J-based Agilent SureSelect exome capture system is to be used.

Correlation between relatives given complete genotypes: from identity by descent to identity by function

In classical quantitative genetics, the correlation between the phenotypes of individuals with unknown genotypes and a known pedigree relationship is expressed in terms of probabilities of IBD states. In existing approaches to the inverse problem where genotypes are observed but pedigree relationships are not, dependence between phenotypes is either modeled as Bayesian uncertainty or mapped to an IBD model via inferred relatedness parameters. Neither approach yields a relationship between genotypic similarity and phenotypic similarity with a probabilistic interpretation corresponding to a generative model. We introduce a generative model for diploid allele effect based on the classic infinite allele mutation process. This approach motivates the concept of IBF (Identity by Function). The phenotypic covariance between two individuals given their diploid genotypes is expressed in terms of functional identity states. The IBF parameters define a genetic architecture for a trait without reference to specific alleles or population. Given full genome sequences, we treat a gene-scale functional region, rather than a SNP, as a QTL, modeling patterns of dominance for multiple alleles. Applications demonstrated by simulation include phenotype and effect prediction and association, and estimation of heritability and classical variance components. A simulation case study of the Missing Heritability problem illustrates a decomposition of heritability under the IBF framework into Explained and Unexplained components.

Variant discovery in targeted resequencing using whole genome amplified DNA

Background

Next generation sequencing and advances in genomic enrichment technologies have enabled the discovery of the full spectrum of variants from common to rare alleles in the human population. The application of such technologies can be limited by the amount of DNA available. Whole genome amplification (WGA) can overcome such limitations. Here we investigate applicability of using WGA by comparing SNP and INDEL variant calls from a single genomic/WGA sample pair from two capture separate experiments: a 50 Mbp whole exome capture and a custom capture array of 4 Mbp region on chr12.

Results

Our results comparing variant calls derived from genomic and WGA DNA show that the majority of variant SNP and INDEL calls are common to both callsets, both at the site and genotype level and suggest that allele bias plays a minimal role when using WGA DNA in re-sequencing studies.

Conclusions

Although the results of this study are based on a limited sample size, they suggest that using WGA DNA allows the discovery of the vast majority of variants, and achieves high concordance metrics, when comparing to genomic DNA calls.

Advances in blood pressure genomics

The elucidation of genes implicated in Mendelian forms of hypertension demonstrates rare variants with substantial effects are responsible, and often these genes lie within pathways managing sodium homeostasis. More recently with advances in affordable high-throughput genotyping strategies, multiple common genetic variants with modest effects on blood pressure (<1 mmHg systolic) have been discovered in the population. In aggregate, these common variants explain <3% of the variance of blood pressure. Although these findings may offer new mechanistic insights into the biology of blood pressure, a key question is can these findings translate into patient benefit? It is timely to reflect on recent advances in genomics, and the use of new resources, such as the 1000 Genomes Project and the Encyclopedia of DNA Elements, to annotate likely causal variants, and their relevance to cardiovascular disease. In this review, we discuss the advances in relation to our knowledge of the genetic architecture of blood pressure, and whether gene discoveries might influence cardiovascular risk assessment, help to stratify patient response to medicine, or identify new biological pathways for novel therapeutic targets.

Fine-scale patterns of population stratification confound rare variant association tests

Advances in next-generation sequencing technology have enabled systematic exploration of the contribution of rare variation to Mendelian and complex diseases. Although it is well known that population stratification can generate spurious associations with common alleles, its impact on rare variant association methods remains poorly understood. Here, we performed exhaustive coalescent simulations with demographic parameters calibrated from exome sequence data to evaluate the performance of nine rare variant association methods in the presence of fine-scale population structure. We find that all methods have an inflated spurious association rate for parameter values that are consistent with levels of differentiation typical of European populations. For example, at a nominal significance level of 5%, some test statistics have a spurious association rate as high as 40%. Finally, we empirically assess the impact of population stratification in a large data set of 4,298 European American exomes. Our results have important implications for the design, analysis, and interpretation of rare variant genome-wide association studies.

Common biological networks underlie genetic risk for alcoholism in African- and European-American populations

Alcohol dependence (AD) is a heritable substance addiction with adverse physical and psychological consequences, representing a major health and economic burden on societies worldwide. Genes thus far implicated via linkage, candidate gene and genome-wide association studies (GWAS) account for only a small fraction of its overall risk, with effects varying across ethnic groups. Here we investigate the genetic architecture of alcoholism and report on the extent to which common, genome-wide SNPs collectively account for risk of AD in two US populations, African-Americans (AAs) and European-Americans (EAs). Analyzing GWAS data for two independent case-control sample sets, we compute polymarker scores that are significantly associated with alcoholism (P = 1.64 × 10(-3) and 2.08 × 10(-4) for EAs and AAs, respectively), reflecting the small individual effects of thousands of variants derived from patterns of allelic architecture that are population specific. Simulations show that disease models based on rare and uncommon causal variants (MAF < 0.05) best fit the observed distribution of polymarker signals. When scoring bins were annotated for gene location and examined for constituent biological networks, gene enrichment is observed for several cellular processes and functions in both EA and AA populations, transcending their underlying allelic differences. Our results reveal key insights into the complex etiology of AD, raising the possibility of an important role for rare and uncommon variants, and identify polygenic mechanisms that encompass a spectrum of disease liability, with some, such as chloride transporters and glycine metabolism genes, displaying subtle, modifying effects that are likely to escape detection in most GWAS designs.