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Summary The recently completed second phase of the Human Microbiome Project has highlighted the relationship between dynamic changes in the microbiome and disease, motivating new microbiome study designs based on longitudinal sampling. Yet, analysis of such data is hindered by presence of technical noise, high dimensionality, and data sparsity. Here, we introduce LUMINATE (longitudinal microbiome inference and zero detection), a fast and accurate method for inferring relative abundances from noisy read count data. We demonstrate that LUMINATE is orders of magnitude faster than current approaches, with better or similar accuracy. We further show that LUMINATE can accurately distinguish biological zeros, when a taxon is absent from the community, from technical zeros, when a taxon is below the detection threshold. We conclude by demonstrating the utility of LUMINATE on a real dataset, showing that LUMINATE smooths trajectories observed from noisy data. LUMINATE is freely available from https://github.com/tyjo/luminate .

The recently completed second phase of the Human Microbiome Project has highlighted the relationship between dynamic changes in the microbiome and disease, motivating new microbiome study designs based on longitudinal sampling. Yet, analysis of such data is hindered by presence of technical noise, high dimensionality, and data sparsity. To address these challenges, we propose LUMINATE (LongitUdinal Microbiome INference And zero deTEction), a fast and accurate method for inferring relative abundances from noisy read count data. We demonstrate on synthetic data that LUMINATE is orders of magnitude faster than current approaches, with better or similar accuracy. This translates to feasibility of analyzing data at the requisite dimensionality for current studies. We further show that LUMINATE can accurately distinguish biological zeros, when a taxon is absent from the community, from technical zeros, when a taxon is below the detection threshold. We conclude by demonstrating the utility of LUMINATE for downstream analysis by using estimates of latent relative abundances to fit the parameters of a dynamical system, leading to more accurate predictions of community dynamics.Code availabilityhttps://github.com/tyjo/luminate

Evidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liability score. In the presence of heterogeneity from distinct etiologies, confounding by covariates, or mislabeling, these correlation patterns are altered predictably. We further extend our method to two additional association study designs: CLiP-X for quantitative predictors in applications such as transcriptome-wide association, and CLiP-Y for quantitative phenotypes, where there is no clear distinction between cases and controls. Through simulations, we demonstrate that CLiP and its extensions reliably distinguish between homogeneous and heterogeneous cohorts when the PRS explains as low as 5% of variance on the liability scale and cohorts comprise 50, 000 − 100, 000 samples, an increasingly practical size for modern GWAS. We apply CLiP to heterogeneity detection in schizophrenia cohorts totaling > 50, 000 cases and controls collected by the Psychiatric Genomics Consortium. We observe significant heterogeneity in mega-analysis of the combined PGC data (p-value 8.54e-4), as well as in individual cohorts meta-analyzed using Fisher’s method (p-value 0.03), based on significantly associated variants.

Despite impressive improvement in the next-generation sequencing technology, reliable detection of indels is still a difficult endeavour. Recognition of true indels is of prime importance in many applications, such as, personalized health care, disease genomics, population genetics etc. Recently, advanced machine learning techniques have been successfully applied to classification problems with large-scale data. In this paper, we present SICaRiO, a gradient boosting classifier for reliable detection of true indels, trained with gold-standard dataset from genome-in-a-bottle (GIAB) consortium. Our filtering scheme significantly improves the performance of each variant calling pipeline used in GIAB and beyond. SICaRiO uses genomic features which can be computed from publicly available resources, hence, we can apply it on any indel callsets not having sequencing pipeline-specific information (e.g., read depth). This study also sheds lights on prior genomic contexts responsible for indel calling error made by sequencing platforms. We have compared prediction difficulty for three indel categories over different sequencing pipelines. We have also ranked genomic features according to their predictivity in determining false indel calls.

Carrier screening programs that identify the presence of known mutations have been effective for reducing the incidence of autosomal recessive conditions in the Ashkenazi Jewish (AJ) population and other populations. Yet, these programs have not realized their full potential. Furthermore, many known autosomal recessive and dominant conditions are not screened for and the molecular basis of other conditions for which screening might be offered is unknown. Through literature review and annotation of full sequenced genomes from healthy individuals, we expanded the list of mutations. Mutations were identified in a sample of 128 fully sequenced AJ genomes that were filtered through clinical databases and curated manually for clinical validity and utility using the American College of Medical Genetics and Genomics scoring (ACMG) system. Other known mutations were identified through literature review. A panel of 163 mutations was identified for 76 autosomal recessive, 24 autosomal dominant, and 3 X-linked disorders. Screening for a broader range of disorders not only could further reduce the incidence of autosomal recessive disorders but also could offer the benefits of early or presymptomatic diagnosis. Genet Med 18 5, 522–528.

Human leukocyte antigen (HLA) locus makes up the major compatibility complex (MHC) and plays a critical role in host response to disease, including cancers and autoimmune disorders. In the clinical setting, HLA typing is necessary for determining tissue compatibility. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between the HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional splicing and bias due to amplification. Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two field precision for MHC class I genes, and over 99.7% for MHC class II. Importantly, arcasHLA takes as its input pre-aligned BAM files, and outputs three-field resolution for all HLA genes in less than 2 minutes. Finally, we discuss evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. arcasHLA is available at https://github.com/RabadanLab/arcasHLA.

Detecting familial matches Recent advances in DNA technology and companies that provide array-based testing have led to services that collect, share, and analyze volunteered genomic information. Privacy concerns have been raised, especially in light of the use of these services by law enforcement to identify suspects in criminal cases. Testing models of relatedness, Erlich et al. show that many individuals of European ancestry in the United States—even those that have not undergone genetic testing—can be identified on the basis of available genetic information. These results indicate a need for procedures to help maintain genetic privacy for individuals. Science , this issue p. 690

Consumer genomics databases reached the scale of millions of individuals. Recently, law enforcement investigators have started to exploit some of these databases to find distant familial relatives, which can lead to a complete re-identification. Here, we leveraged genomic data of 600,000 individuals tested with consumer genomics to investigate the power of such long-range familial searches. We project that half of the searches with European-descent individuals will result with a third cousin or closer match and will provide a search space small enough to permit re-identification using common demographic identifiers. Moreover, in the near future, virtually any European-descent US person could be implicated by this technique. We propose a potential mitigation strategy based on cryptographic signature that can resolve the issue and discuss policy implications to human subject research.

Cognitive deficits and reduced educational achievement are common in psychiatric illness; understanding the genetic basis of cognitive and educational deficits may be informative about the etiology of psychiatric disorders. A recent, large genome-wide association study (GWAS) reported a genome-wide significant locus for years of education, which subsequently demonstrated association to general cognitive ability (“g”) in overlapping cohorts. The current study was designed to test whether GWAS hits for educational attainment are involved in general cognitive ability in an independent, large-scale collection of cohorts. Using cohorts in the Cognitive Genomics Consortium (COGENT; up to 20,495 healthy individuals), we examined the relationship between g and variants associated with educational attainment. We next conducted meta-analyses with 24,189 individuals with neurocognitive data from the educational attainment studies, and then with 53,188 largely independent individuals from a recent GWAS of cognition. A SNP (rs1906252) located at chromosome 6q16.1, previously associated with years of schooling, was significantly associated with g (P = 1.47 × 10−4) in COGENT. The first joint analysis of 43,381 non-overlapping individuals for this a priori-designated locus was strongly significant (P = 4.94 × 10−7), and the second joint analysis of 68,159 non-overlapping individuals was even more robust (P = 1.65 × 10−9). These results provide independent replication, in a large-scale dataset, of a genetic locus associated with cognitive function and education. As sample sizes grow, cognitive GWAS will identify increasing numbers of associated loci, as has been accomplished in other polygenic quantitative traits, which may be relevant to psychiatric illness. © 2015 Wiley Periodicals, Inc.

Crohn’s disease (CD), a form of inflammatory bowel disease, has a higher prevalence in Ashkenazi Jewish than in non-Jewish European populations. To define the role of nonsynonymous mutations, we performed exome sequencing of Ashkenazi Jewish patients with CD, followed by array-based genotyping and association analysis in 2066 CD cases and 3633 healthy controls. We detected association signals in the LRRK2 gene that conferred risk for CD (N2081D variant, P = 9.5 × 10−10) or protection from CD (N551K variant, tagging R1398H-associated haplotype, P = 3.3 × 10−8). These variants affected CD age of onset, disease location, LRRK2 activity, and autophagy. Bayesian network analysis of CD patient intestinal tissue further implicated LRRK2 in CD pathogenesis. Analysis of the extended LRRK2 locus in 24,570 CD cases, patients with Parkinson’s disease (PD), and healthy controls revealed extensive pleiotropy, with shared genetic effects between CD and PD in both Ashkenazi Jewish and non-Jewish cohorts. The LRRK2 N2081D CD risk allele is located in the same kinase domain as G2019S, a mutation that is the major genetic cause of familial and sporadic PD. Like the G2019S mutation, the N2081D variant was associated with increased kinase activity, whereas neither N551K nor R1398H variants on the protective haplotype altered kinase activity. We also confirmed that R1398H, but not N551K, increased guanosine triphosphate binding and hydrolyzing enzyme (GTPase) activity, thereby deactivating LRRK2. The presence of shared LRRK2 alleles in CD and PD provides refined insight into disease mechanisms and may have major implications for the treatment of these two seemingly unrelated diseases.

While increasingly large reference panels for genome-wide imputation have been recently made available, the degree to which imputation accuracy can be enhanced by population-specific reference panels remains an open question. Here, we sequenced at full-depth (≥ 30×), across two platforms (Illumina X Ten and Complete Genomics, Inc.), a moderately large (n = 738) cohort of samples drawn from the Ashkenazi Jewish population. We developed a series of quality control steps to optimize sensitivity, specificity, and comprehensiveness of variant calls in the reference panel, and then tested the accuracy of imputation against target cohorts drawn from the same population. Quality control (QC) thresholds for the Illumina X Ten platform were identified that permitted highly accurate calling of single nucleotide variants across 94% of the genome. QC procedures also identified numerous regions that are poorly mapped using current reference or alternate assemblies. After stringent QC, the population-specific reference panel produced more accurate and comprehensive imputation results relative to publicly available, large cosmopolitan reference panels, especially in the range of rare variants that may be most critical to further progress in mapping of complex phenotypes. The population-specific reference panel also permitted enhanced filtering of clinically irrelevant variants from personal genomes.

BackgroundWhile increasingly large reference panels for genome-wide imputation have been recently made available, the degree to which imputation accuracy can be enhanced by population-specific reference panels remains an open question. In the present study, we sequenced at full-depth (≥30x) a moderately large (n=738) cohort of samples drawn from the Ashkenazi Jewish population across two platforms (Illumina X Ten and Complete Genomics, Inc.). We developed and refined a series of quality control steps to optimize sensitivity, specificity, and comprehensiveness of variant calls in the reference panel, and then tested the accuracy of imputation against target cohorts drawn from the same population.ResultsFor samples sequenced on the Illumina X Ten platform, quality thresholds were identified that permitted highly accurate calling of single nucleotide variants across 94% of the genome. The Complete Genomics, Inc. platform was more conservative (fewer variants called) compared to the Illumina platform, but also demonstrated relatively greater numbers of false positives that needed to be filtered. Quality control procedures also permitted detection of novel genome reads that are not mapped to current reference or alternate assemblies. After stringent quality control, the population-specific reference panel produced more accurate and comprehensive imputation results relative to publicly available, large cosmopolitan reference panels. The population-specific reference panel also permitted enhanced filtering of clinically irrelevant variants from personal genomes.ConclusionsOur primary results demonstrate enhanced accuracy of a population-specific imputation panel relative to cosmopolitan panels, especially in the range of infrequent (

The recent series of large genome-wide association studies in European and Japanese cohorts established that Parkinson disease (PD) has a substantial genetic component. To further investigate the genetic landscape of PD, we performed a genome-wide scan in the largest to date Ashkenazi Jewish cohort of 1130 Parkinson patients and 2611 pooled controls. Motivated by the reduced disease allele heterogeneity and a high degree of identical-by-descent (IBD) haplotype sharing in this founder population, we conducted a haplotype association study based on mapping of shared IBD segments. We observed significant haplotype association signals at three previously implicated Parkinson loci: LRRK2 (OR = 12.05, P = 1.23 × 10(-56)), MAPT (OR = 0.62, P = 1.78 × 10(-11)) and GBA (multiple distinct haplotypes, OR > 8.28, P = 1.13 × 10(-11) and OR = 2.50, P = 1.22 × 10(-9)). In addition, we identified a novel association signal on chr2q14.3 coming from a rare haplotype (OR = 22.58, P = 1.21 × 10(-10)) and replicated it in a secondary cohort of 306 Ashkenazi PD cases and 2583 controls. Our results highlight the power of our haplotype association method, particularly useful in studies of founder populations, and reaffirm the benefits of studying complex diseases in Ashkenazi Jewish cohorts.

Objective Obesity and diabetes are particularly high in indigenous populations exposed to a Western diet and lifestyle. The prevalence of obesity, diabetes, hyperglycemia, dyslipidemia, and hypertension in one such population, the Micronesian island of Kosrae was described. Design and Methods Longitudinal screenings for metabolic traits were conducted on adult Kosraens ≥ 20 years of age in 1994 and again in 2001. Data was obtained on 3,106 Kosraens, comprising ∼80% of the adult population. Diabetes was diagnosed using World Health Organization guidelines. Prevalences of obesity, hyperglycemia, dyslipidemia, and hypertension were assessed. Results The overall age-adjusted prevalence of diabetes increased from 14 to 21%. The most significant change observed in the population was increases in obesity and hyperglycemia, especially among young Kosraens and women. Obesity age-adjusted prevalence increased from 45 to 62%, and hyperglycemia age-adjusted prevalence increased from 19 to 44%. Of note, Kosraens as a group had unusually low high density lipoprotein (HDL) levels with 80% classified as low HDL by NCEP-ATPIII criteria, despite lacking the usually accompanying increase in triglycerides. Comparison to reports from other populations shows that Kosrae experiences one of the highest rates of obesity, hyperglycemia, and low HDL globally while maintaining relatively healthy levels of triglycerides. Conclusion Our study shows a dramatic increase in obesity and hyperglycemia in Kosrae in just 7 years and forebodes significantly increased health risks for this part of the world.

MotivationMicrobiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones, and therefore lie in-between them in cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.Contactwjj2106@columbia.edu

SummaryGermline determinants of gene expression in tumors are infrequently studied due to the complexity of transcript regulation caused by somatically acquired alterations. We performed expression quantitative trait locus (eQTL)-based analyses using the multi-level information provided in The Cancer Genome Atlas (TCGA). Of the factors we measured, cis-acting eQTLs accounted for 1.2% of the total variation of tumor gene expression, while somatic copy-number alteration and CpG methylation accounted for 7.3% and 3.3%, respectively. eQTL analyses of 15 previously reported breast cancer risk loci resulted in the discovery of three variants that are significantly associated with transcript levels (false discovery rate [FDR] < 0.1). Our trans-based analysis identified an additional three risk loci to act through ESR1, MYC, and KLF4. These findings provide a more comprehensive picture of gene expression determinants in breast cancer as well as insights into the underlying biology of breast cancer risk loci.

Background & Aims Crohn's disease (CD) has the highest prevalence in Ashkenazi Jewish populations. We sought to identify rare, CD-associated frameshift variants of high functional and statistical effects. Methods We performed exome sequencing and array-based genotype analyses of 1477 Ashkenazi Jewish individuals with CD and 2614 Ashkenazi Jewish individuals without CD (controls). To validate our findings, we performed genotype analyses of an additional 1515 CD cases and 7052 controls for frameshift mutations in the colony-stimulating factor 2–receptor β common subunit gene ( CSF2RB ). Intestinal tissues and blood samples were collected from patients with CD; lamina propria leukocytes were isolated and expression of CSF2RB and granulocyte-macrophage colony–stimulating factor–responsive cells were defined by adenomatous polyposis coli (APC) time-of-flight mass cytometry (CyTOF analysis). Variants of CSF2RB were transfected into HEK293 cells and the expression and functions of gene products were compared. Results In the discovery cohort, we associated CD with a frameshift mutation in CSF2RB ( P = 8.52 × 10 -4 ); the finding was validated in the replication cohort (combined P = 3.42 × 10 -6 ). Incubation of intestinal lamina propria leukocytes with granulocyte-macrophage colony–stimulating factor resulted in high levels of phosphorylation of signal transducer and activator of transcription (STAT5) and lesser increases in phosphorylation of extracellular signal–regulated kinase and AK straining transforming (AKT). Cells co-transfected with full-length and mutant forms of CSF2RB had reduced pSTAT5 after stimulation with granulocyte-macrophage colony–stimulating factor, compared with cells transfected with control CSF2RB, indicating a dominant-negative effect of the mutant gene. Monocytes from patients with CD who were heterozygous for the frameshift mutation (6% of CD cases analyzed) had reduced responses to granulocyte-macrophage colony–stimulating factor and markedly decreased activity of aldehyde dehydrogenase; activity of this enzyme has been associated with immune tolerance. Conclusions In a genetic analysis of Ashkenazi Jewish individuals, we associated CD with a frameshift mutation in CSF2RB . Intestinal monocytes from carriers of this mutation had reduced responses to granulocyte-macrophage colony–stimulating factor, providing an additional mechanism for alterations to the innate immune response in individuals with CD.

Crohn's disease (CD), a form of inflammatory bowel disease, has a higher prevalence in Ashkenazi Jewish than in non-Jewish European populations. To define the role of nonsynonymous mutations, we performed exome sequencing of Ashkenazi Jewish patients with CD, followed by array-based genotyping and association analysis in 2066 CD cases and 3633 healthy controls. We detected association signals in the

The Ashkenazi Jewish (AJ) population is important in genetics due to its high rate of Mendelian disorders. AJ appeared in Europe in the 10th century, and their ancestry is thought to comprise European (EU) and Middle-Eastern (ME) components. However, both the time and place of admixture are subject to debate. Here, we attempt to characterize the AJ admixture history using a careful application of new and existing methods on a large AJ sample. Our main approach was based on local ancestry inference, in which we first classified each AJ genomic segment as EU or ME, and then compared allele frequencies along the EU segments to those of different EU populations. The contribution of each EU source was also estimated using GLOBETROTTER and haplotype sharing. The time of admixture was inferred based on multiple statistics, including ME segment lengths, the total EU ancestry per chromosome, and the correlation of ancestries along the chromosome. The major source of EU ancestry in AJ was found to be Southern Europe (≈60–80% of EU ancestry), with the rest being likely Eastern European. The inferred admixture time was ≈30 generations ago, but multiple lines of evidence suggest that it represents an average over two or more events, pre- and post-dating the founder event experienced by AJ in late medieval times. The time of the pre-bottleneck admixture event, which was likely Southern European, was estimated to ≈25–50 generations ago., Author summary The Ashkenazi Jewish population has resided in Europe for much of its 1000-year existence. However, its ethnic and geographic origins are controversial, due to the scarcity of reliable historical records. Previous genetic studies have found links to Middle-Eastern and European ancestries, but the admixture history has not been studied in detail yet, partly due to technical difficulties in disentangling signals from multiple admixture events. Here, we present an in-depth analysis of the sources of European gene flow and the time of admixture events by using multiple new and existing methods and extensive simulations. Our results suggest a model of at least two events of European admixture. One event slightly pre-dated a late medieval founder event and was likely from a Southern European source. Another event post-dated the founder event and likely occurred in Eastern Europe. These results, as well as the methods introduced, will be highly valuable for geneticists and other researchers interested in Ashkenazi Jewish origins.

During tumor initiation and progression, cancer cells acquire a selective advantage, allowing them to outcompete their normal counterparts. Identification of the genetic changes that underlie these tumor acquired traits can provide deeper insights into the biology of tumorigenesis. Regions of copy number alterations and germline DNA variants are some of the elements subject to selection during tumor evolution. Integrated examination of inherited variation and somatic alterations holds the potential to reveal specific nucleotide alleles that a tumor “prefers” to have amplified. Next-generation sequencing of tumor and matched normal tissues provides a high-resolution platform to identify and analyze such somatic amplicons. Within an amplicon, examination of informative (e.g., heterozygous) sites deviating from a 1:1 ratio may suggest selection of that allele. A naive approach examines the reads for each heterozygous site in isolation; however, this ignores available valuable linkage information across sites. We, therefore, present a novel hidden Markov model-based method—Haplotype Amplification in Tumor Sequences (HATS)—that analyzes tumor and normal sequence data, along with training data for phasing purposes, to infer amplified alleles and haplotypes in regions of copy number gain. Our method is designed to handle rare variants and biases in read data. We assess the performance of HATS using simulated amplified regions generated from varying copy number and coverage levels, followed by amplicons in real data. We demonstrate that HATS infers the amplified alleles more accurately than does the naive approach, especially at low to intermediate coverage levels and in cases (including high coverage) possessing stromal contamination or allelic bias.

Modern day Latin America resulted from the encounter of Europeans with the indigenous peoples of the Americas in 1492, followed by waves of migration from Europe and Africa. As a result, the genomic structure of present day Latin Americans was determined both by the genetic structure of the founding populations and the numbers of migrants from these different populations. Here, we analyzed DNA collected from two well-established communities in Colorado (33 unrelated individuals) and Ecuador (20 unrelated individuals) with a measurable prevalence of the BRCA1 c.185delAG and the GHR c.E180 mutations, respectively, using Affymetrix Genome-wide Human SNP 6.0 arrays to identify their ancestry. These mutations are thought to have been brought to these communities by Sephardic Jewish progenitors. Principal component analysis and clustering methods were employed to determine the genome-wide patterns of continental ancestry within both populations using single nucleotide polymorphisms, complemented by determination of Y-chromosomal and mitochondrial DNA haplotypes. When examining the presumed European component of these two communities, we demonstrate enrichment for Sephardic Jewish ancestry not only for these mutations, but also for other segments as well. Although comparison of both groups to a reference Hispanic/Latino population of Mexicans demonstrated proximity and similarity to other modern day communities derived from a European and Native American two-way admixture, identity-by-descent and Y-chromosome mapping demonstrated signatures of Sephardim in both communities. These findings are consistent with historical accounts of Jewish migration from the realms that comprise modern Spain and Portugal during the Age of Discovery. More importantly, they provide a rationale for the occurrence of mutations typically associated with the Jewish Diaspora in Latin American communities.

Producción Científica, Background & Aims Drug-induced liver injury (DILI), especially from antimicrobial agents, is an important cause of serious liver disease. Amoxicillin-clavulanate (AC) is a leading cause of idiosyncratic DILI, but little is understood about genetic susceptibility to this adverse reaction. Methods We performed a genome-wide association study using 822,927 single-nucleotide polymorphism (SNP) markers from 201 White European and US cases of AC-DILI and 532 population controls, matched for genetic background. Results AC-DILI was associated with many loci in the major histocompatibility complex. The strongest effect was with a human leukocyte antigen (HLA) class II SNP (rs9274407, P=4.8×10−14), which correlated with rs3135388, a tag SNP of HLA-DRB1*1501-DQB1*0602 that was previously associated with AC-DILI. Conditioned on rs3135388, rs9274407 is still significant (P=1.1×10−4). An independent association was observed in the class I region (rs2523822, P=1.8×10−10), related to HLA-A*0201. The most significant class I and II SNPs showed statistical interaction (P=0.0015). High-resolution HLA genotyping (177 cases and 219 controls) confirmed associations of HLA-A*0201 (P=2×10−6) and HLA-DQB1*0602 (P=5×10−10), and their interaction (P=0.005). Additional, population-dependent effects were observed in HLA alleles with nominal significance. In an analysis of auto-immunerelated genes, rs2476601 in the gene PTPN22 was associated (P=1.3×10−4). Conclusions Class I and II HLA genotypes affect susceptibility to AC-DILI, indicating the importance of the adaptive immune response in pathogenesis. The HLA genotypes identified will be useful in studies of the pathogenesis of AC-DILI, but have limited utility as predictive or diagnostic biomarkers because of the low positive-predictive values.

The potential benefits of using population isolates in genetic mapping, such as reduced genetic, phenotypic and environmental heterogeneity, are offset by the challenges posed by the large amounts of direct and cryptic relatedness in these populations confounding basic assumptions of independence. We have evaluated four representative specialized methods for association testing in the presence of relatedness; (i) within-family (ii) within- and between-family and (iii) mixed-models methods, using simulated traits for 2906 subjects with known genome-wide genotype data from an extremely isolated population, the Island of Kosrae, Federated States of Micronesia. We report that mixed models optimally extract association information from such samples, demonstrating 88% power to rank the true variant as among the top 10 genome-wide with 56% achieving genome-wide significance, a >80% improvement over the other methods, and demonstrate that population isolates have similar power to non-isolate populations for observing variants of known effects. We then used the mixed-model method to reanalyze data for 17 published phenotypes relating to metabolic traits and electrocardiographic measures, along with another 8 previously unreported. We replicate nine genome-wide significant associations with known loci of plasma cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, thyroid stimulating hormone, homocysteine, C-reactive protein and uric acid, with only one detected in the previous analysis of the same traits. Further, we leveraged shared identity-by-descent genetic segments in the region of the uric acid locus to fine-map the signal, refining the known locus by a factor of 4. Finally, we report a novel associations for height (rs17629022, P< 2.1 × 10−8).

For more than a century, Jews and non-Jews alike have tried to define the relatedness of contemporary Jewish people. Previous genetic studies of blood group and serum markers suggested that Jewish groups had Middle Eastern origin with greater genetic similarity between paired Jewish populations. However, these and successor studies of monoallelic Y chromosomal and mitochondrial genetic markers did not resolve the issues of within and between-group Jewish genetic identity. Here, genome-wide analysis of seven Jewish groups (Iranian, Iraqi, Syrian, Italian, Turkish, Greek, and Ashkenazi) and comparison with non-Jewish groups demonstrated distinctive Jewish population clusters, each with shared Middle Eastern ancestry, proximity to contemporary Middle Eastern populations, and variable degrees of European and North African admixture. Two major groups were identified by principal component, phylogenetic, and identity by descent (IBD) analysis: Middle Eastern Jews and European/Syrian Jews. The IBD segment sharing and the proximity of European Jews to each other and to southern European populations suggested similar origins for European Jewry and refuted large-scale genetic contributions of Central and Eastern European and Slavic populations to the formation of Ashkenazi Jewry. Rapid decay of IBD in Ashkenazi Jewish genomes was consistent with a severe bottleneck followed by large expansion, such as occurred with the so-called demographic miracle of population expansion from 50,000 people at the beginning of the 15th century to 5,000,000 people at the beginning of the 19th century. Thus, this study demonstrates that European/Syrian and Middle Eastern Jews represent a series of geographical isolates or clusters woven together by shared IBD genetic threads.

Drug-induced liver injury (DILI) is an important cause of serious liver disease. The antimicrobial agent flucloxacillin is a common cause of DILI, but the genetic basis for susceptibility remains unclear. We conducted a genome-wide association (GWA) study using 866,399 markers in 51 cases of flucloxacillin DILI and 282 controls matched for sex and ancestry. The GWA showed an association peak in the major histocompatibility complex (MHC) region with the strongest association (P = 8.7 x 10(-33)) seen for rs2395029[G], a marker in complete linkage disequilibrium (LD) with HLA-B*5701. Further MHC genotyping, which included 64 flucloxacillin-tolerant controls, confirmed the association with HLA-B*5701 (OR = 80.6, P = 9.0 x 10(-19)). The association was replicated in a second cohort of 23 cases. In HLA-B*5701 carrier cases, rs10937275 in ST6GAL1 on chromosome 3 also showed genome-wide significance (OR = 4.1, P = 1.4 x 10(-8)). These findings provide new insights into the mechanism of flucloxacillin DILI and have the potential to substantially improve diagnosis of this serious disease.

Background— Variation in LDL-cholesterol (LDL-C) among individuals is a complex genetic trait involving multiple genes and gene-environment interactions. Methods and Results— In a genome-wide association study (GWAS) to identify genetic variants influencing LDL-C in an isolated population from Kosrae, we observed associations for SNPs in the gene encoding 3hydroxy-3-methylglutaryl (HMG)-coenzyme A (CoA) reductase (HMGCR). Three of these SNPs (rs7703051, rs12654264, and rs3846663) met the statistical threshold of genome-wide significance when combined with data from the Diabetes Genetics Initiative GWAS. We followed up the association results and identified a functional SNP in intron13 (rs3846662), which was in linkage disequilibrium with the SNPs of genome-wide significance and affected alternative splicing of HMGCR mRNA. In vitro studies in human lymphoblastoid cells demonstrated that homozygosity for the rs3846662 minor allele was associated with up to 2.2-fold lower expression of alternatively spliced HMGCR mRNA lacking exon13, and minigene transfection assays confirmed that allele status at rs3846662 directly modulated alternative splicing of HMGCR exon13 (42.9±3.9 versus 63.7±1.0%Δexon13/total HMGCR mRNA, P =0.02). Further, the alternative splice variant could not restore HMGCR activity when expressed in HMGCR deficient UT-2 cells. Conclusion— We identified variants in HMGCR that are associated with LDL-C across populations and affect alternative splicing of HMGCR exon13.

We present GERMLINE, a robust algorithm for identifying segmental sharing indicative of recent common ancestry between pairs of individuals. Unlike methods with comparable objectives, GERMLINE scales linearly with the number of samples, enabling analysis of whole-genome data in large cohorts. Our approach is based on a dictionary of haplotypes that is used to efficiently discover short exact matches between individuals. We then expand these matches using dynamic programming to identify long, nearly identical segmental sharing that is indicative of relatedness. We use GERMLINE to comprehensively survey hidden relatedness both in the HapMap as well as in a densely typed island population of 3000 individuals. We verify that GERMLINE is in concordance with other methods when they can process the data, and also facilitates analysis of larger scale studies. We bolster these results by demonstrating novel applications of precise analysis of hidden relatedness for (1) identification and resolution of phasing errors and (2) exposing polymorphic deletions that are otherwise challenging to detect. This finding is supported by concordance of detected deletions with other evidence from independent databases and statistical analyses of fluorescence intensity not used by GERMLINE.

A haplotype is an m-long binary vector. The XOR-genotype of two haplotypes is the m-vector of their coordinate-wise XOR. We study the following problem: Given a set of XOR-genotypes, reconstruct their haplotypes so that the set of resulting haplotypes can be mapped onto a perfect phylogeny (PP) tree. The question is motivated by studying population evolution in human genetics, and is a variant of the perfect phylogeny haplotyping problem that has received intensive attention recently. Unlike the latter problem, in which the input is "full" genotypes, here we assume less informative input, and so may be more economical to obtain experimentally. Building on ideas of Gusfield, we show how to solve the problem in polynomial time, by a reduction to the graph realization problem. The actual haplotypes are not uniquely determined by that tree they map onto, and the tree itself may or may not be unique. We show that tree uniqueness implies uniquely determined haplotypes, up to inherent degrees of freedom, and give a sufficient condition for the uniqueness. To actually determine the haplotypes given the tree, additional information is necessary. We show that two or three full genotypes suffice to reconstruct all the haplotypes, and present a linear algorithm for identifying those genotypes.

Genetic-based optimization of treatment prescription is becoming a central research focus in the management of chronic diseases, such as multiple sclerosis, which incur a prolonged drug-regimen adjustment. This study was aimed to identify genetic markers that can predict response to glatiramer acetate (Copaxone) immunotherapy for relapsing multiple sclerosis. For this purpose, we genotyped fractional cohorts of two glatiramer acetate clinical trials for HLA-DRB1*1501 and 61 single nucleotide polymorphisms within a total of 27 candidate genes. Statistical analyses included single nucleotide polymorphism-by-single nucleotide polymorphism and haplotype tests of drug-by-genotype effects in drug-treated versus placebo-treated groups. We report the detection of a statistically significant association between glatiramer acetate response and a single nucleotide polymorphism in a T-cell receptor beta (TRB@) variant replicated in the two independent cohorts (odds ratio=6.85). Findings in the Cathepsin S (CTSS) gene (P=0.049 corrected for all single nucleotide polymorphisms and definitions tested, odds ratio=11.59) in one of the cohorts indicate a possible association that needs to be further investigated. Additionally, we recorded nominally significant associations of response with five other genes, MBP, CD86, FAS, IL1R1 and IL12RB2, which are likely to be involved in glatiramer acetate's mode-of-action, both directly and indirectly. Each of these association signals in and of itself is consistent with the no-association null-hypothesis, but the number of detected associations is surprising vis-à-vis chance expectation. Moreover, the restriction of these associations to the glatiramer acetate-treated group, rather than the placebo group, clearly demonstrates drug-specific genetic effects. These findings provide additional progress toward development of pharmacogenetics-based personalized treatment for multiple sclerosis.

The rate at which human genomes mutate is a central biological parameter that has many implications for our ability to understand demographic and evolutionary phenomena. We present a method for inferring mutation and gene-conversion rates by using the number of sequence differences observed in identical-by-descent (IBD) segments together with a reconstructed model of recent population-size history. This approach is robust to, and can quantify, the presence of substantial genotyping error, as validated in coalescent simulations. We applied the method to 498 trio-phased sequenced Dutch individuals and inferred a point mutation rate of 1.66 × 10(-8) per base per generation and a rate of 1.26 × 10(-9) for

The rate at which human genomes mutate is a central biological parameter that has many implications for our ability to understand demographic and evolutionary phenomena. We present a method for inferring mutation and gene conversion rates using the number of sequence differences observed in identical-by-descent (IBD) segments together with a reconstructed model of recent population size history. This approach is robust to, and can quantify, the presence of substantial genotyping error, as validated in coalescent simulations. We applied the method to 498 trio-phased Dutch individuals from the Genome of the Netherlands (GoNL) project, sequenced at an average depth of 13×. We infer a point mutation rate of 1.66 ± 0.04 × 10-8per base per generation, and a rate of 1.26 ± 0.06 × 10-9for -6, consistent with recent reports. We find that recombination does not have observable mutagenic effects after gene conversion is accounted for, and that local gene conversion rates reflect recombination rates. We detect a strong enrichment for recent deleterious variation among mismatching variants found within IBD regions, and observe summary statistics of local IBD sharing to closely match previously proposed metrics of background selection, but find no significant effects of selection on our estimates of mutation rate. We detect no evidence for strong variation of mutation rates in a number of genomic annotations obtained from several recent studies.

Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25-100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.

The much-anticipated fixed-array, genome-wide SNP genotyping technologies make large-scale genome-wide association scans now possible for large numbers of subjects. In this paper we reconsider the problem (Satagopan and Elston [2003] Genet Epidemiol 25:149-157) of optimizing a two-stage genotyping design to deal with important new issues that are relevant when studies are expanded from candidate gene size to a genome-wide scale. We investigate how the basic two-stage genotyping approach, in which all markers are genotyped in an initial group of subjects (stage I) and only the promising markers are genotyped in additional subjects (stage II), can be used to reduce genotyping cost in a genome-wide case-control association study even after allowing for much higher per genotype costs using specially designed assays in stage II, compared to the fixed array of SNPs used in stage I. In addition, we consider the problem of using measured SNPs to make (imperfect) prediction of unmeasured SNPs for association tests of all SNPs (measured or unmeasured) genome wide and the utility of expanding genotyping densities in stage II in the regions where significant associations were detected in stage I. Under a set of reasonable but conservative assumptions, we derive optimal two-stage design configurations (sample sizes and the thresholds of significance in both stages) with these optimal designs depending both on the total number of markers tested and upon the ratios of cost in stage II versus stage I. In addition we show how existing software for power and sample size calculations can be used for the purpose of designing two-stage studies, for a wide range of assumptions about the number of markers genotyped and the costs of genotyping in each stage of the study.

Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution. © 2005 Nature Publishing Group., link_to_OA_fulltext

The genetic mapping of drug-response traits is often characterised by a poor signal-to-noise ratio that is placebo related and which distinguishes pharmacogenetic association studies from classical case-control studies for disease susceptibility. The goal of this study was to evaluate the statistical power of candidate gene association studies under different pharmacogenetic scenarios, with special emphasis on the placebo effect. Genotype/phenotype data were simulated, mimicking samples from clinical trials, and response to the drug was modelled as a binary trait. Association was evaluated by a logistic regression model. Statistical power was estimated as a function of the number of single nucleotide polymorphisms (SNPs) genotyped, the frequency of the placebo 'response', the genotype relative risk (GRR) of the response polymorphism, the strategy for selecting SNPs for genotyping, the number of individuals in the trial and the ratio of placebo-treated to drugtreated patients. We show that: (i) the placebo 'response' strongly affects the statistical power of association studies -- even a highly penetrant drug-response allele requires at least a 500-patient trial in order to reach 80 per cent power, several-fold more than the value estimated by standard tools that are not calibrated to pharmacogenetics; (ii) the power of a pharmacogenetic association study depends primarily on the penetrance of the response genotype and, when this penetrance is fixed, power decreases for larger placebo effects; (iii) power is dramatically increased when adding markers; (iv) an optimal study design includes a similar number of placebo- and drugtreated patients; and (v) in this setting, straightforward haplotype analysis does not seem to have an advantage over single marker analysis.

Prospects for large-scale association studies rely on economical methods and powerful analysis. Representing available SNPs by small subsets and measuring allele frequencies on pooled DNA samples each improve genotyping cost effectiveness, while haplotype analysis may highlight associations in otherwise underpowered studies. This manuscript provides the mathematical framework to integrate these methodologies.

Perfect phylogeny is one of the fundamental models for studying evolution. We investigate the following variant of the model: The input is a species-characters matrix. The characters are binary and directed; i.e., a species can only gain characters. The difference from standard perfect phylogeny is that for some species the states of some characters are unknown. The question is whether one can complete the missing states in a way that admits a perfect phylogeny. The problem arises in classical phylogenetic studies, when some states are missing or undetermined. Quite recently, studies that infer phylogenies using inserted repeat elements in DNA gave rise to the same problem. Extant solutions for it take time O(n2m) for n species and m characters. We provide a graph theoretic formulation of the problem as a graph sandwich problem, and give near-optimal $\tilde{O}(nm)$-time algorithms for the problem. We also study the problem of finding a single, general solution tree, from which any other solution can be obtained by node splitting. We provide an algorithm to construct such a tree, or determine that none exists.

Re-sequencing, the identification of the specific variants in a sequence of interest compared with a known genomic sequence, is a ubiquitous task in today’s biology. Universal arrays, which interrogate all possible oligonucleotides of a certain length in a target sequence, have been suggested for computationally determining a polynucleotide sequence from its oligonucleotide content. We present here new methods that use such arrays for re-sequencing. Our methods are applied to data obtained by the polymerase signaling assay, which arrays single-based primer extension reactions for either universal or partial arrays of pentanucleotides. The computational analysis uses the spectrum alignment algorithm, which is refined and enhanced here in order to overcome noise incurred by the use of such short primers. We present accurate re-sequencing results for both synthetic and amplified DNA molecules.

Universal arrays contain all possible oligonucleotides of a certain length, typically 6–10 bases. They can determine in a single experiment all substrings of that length that occur along a target sequence. That information, also called the spectrum of the sequence, is not sufficient to uniquely reconstruct a sequence longer than a few hundred bases. We have devised a polynomial algorithm that reconstructs the sequence, given the spectrum and an additional reference sequence, homologous to the target sequence. Such a reference is available, for example, in the identification of single-nucleotide polymorphisms. The algorithm can handle errors in the spectrum as well as substitutions, insertions, and deletions in the target sequence. We present extensive simulation results, which show that the algorithm correctly reconstructs target sequences of >2,000 nucleotides from error-prone 8-mer spectra when realistic levels of single-nucleotide polymorphisms are present.