Genes mirror geography within Europe.

Understanding the genetic structure of human populations is of fundamental interest to medical, forensic and anthropological sciences. Advances in high-throughput genotyping technology have markedly improved our understanding of global patterns of human genetic variation and suggest the potential to use large samples to uncover variation among closely spaced populations. Here we characterize genetic variation in a sample of 3,000 European individuals genotyped at over half a million variable DNA sites in the human genome. Despite low average levels of genetic differentiation among Europeans, we find a close correspondence between genetic and geographic distances; indeed, a geographical map of Europe arises naturally as an efficient two-dimensional summary of genetic variation in Europeans. The results emphasize that when mapping the genetic basis of a disease phenotype, spurious associations can arise if genetic structure is not properly accounted for. In addition, the results are relevant to the prospects of genetic ancestry testing; an individual's DNA can be used to infer their geographic origin with surprising accuracy-often to within a few hundred kilometres.

Mobile gibberellin directly stimulates Arabidopsis hypocotyl xylem expansion.

Secondary growth of the vasculature results in the thickening of plant structures and continuously produces xylem tissue, the major biological carbon sink. Little is known about the developmental control of this quantitative trait, which displays two distinct phases in Arabidopsis thaliana hypocotyls. The later phase of accelerated xylem expansion resembles the secondary growth of trees and is triggered upon flowering by an unknown, shoot-derived signal. We found that flowering-dependent hypocotyl xylem expansion is a general feature of herbaceous plants with a rosette growth habit. Flowering induction is sufficient to trigger xylem expansion in Arabidopsis. By contrast, neither flower formation nor elongation of the main inflorescence is required. Xylem expansion also does not depend on any particular flowering time pathway or absolute age. Through analyses of natural genetic variation, we found that ERECTA acts locally to restrict xylem expansion downstream of the gibberellin (GA) pathway. Investigations of mutant and transgenic plants indicate that GA and its signaling pathway are both necessary and sufficient to directly trigger enhanced xylogenesis. Impaired GA signaling did not affect xylem expansion systemically, suggesting that it acts downstream of the mobile cue. By contrast, the GA effect was graft transmissible, suggesting that GA itself is the mobile shoot-derived signal.

Genetic Variants of Serum Uric Acid and Gout: An Analysis of > 170,000 Individuals

Background/Purpose: Gout is a common and excruciatingly painful inflammatory arthritis caused by hyperuricemia. In addition to various lifestyle risk factors, a substantial genetic predisposition to gout has long been recognized. The Global Urate Genetics Consortium (GUGC) has aimed to comprehensively investigate the genetics of serum uric acid and gout using data from _ 140,000 individuals of European-ancestry, 8,340 individuals of Indian ancestry, 5,820 African-Americans, and 15,286 Japanese. Methods: We performed discovery GWAS meta-analyses of serum urate levels (n_110,347 individuals) followed by replication analyses (n_32,813 different individuals). Our gout analysis involved 3,151 cases and 68,350 controls, including 1,036 incident gout cases that met the American College of Rheumatology Criteria. We also examined the association of gout with fractional excretion of uric acid (n_6,799). A weighted genetic urate score was constructed based on the number of risk alleles across urate-associated loci, and their association with the risk of gout was evaluated. Furthermore, we examined implicated transcript expression in cis (expression quantitative trait loci databases) for potential insights into the gene underlying the association signal. Finally, in order to further identify urate-associated genomic regions, we performed functional network analyses that incorporated prior knowledge on molecular interactions in which the gene products of implicated genes operate. Results: We identified and replicated 28 genome-wide significant loci in association with serum urate (P 5_10_8), including all previously-reported loci as well as 18 novel genetic loci. Unlike the majority of previouslyidentified loci, none of the novel loci appeared to be obvious candidates for urate transport. Rather, they were mapped to genes that encode for purine production, transcription, or growth factors with broad downstream responses. Besides SLC2A9 and ABCG2, no additional regions contained SNPs that differed significantly (P _ 5_10_8) between sexes. Urateincreasing alleles were associated with an increased risk of gout for all loci. The urate genetic risk score (ranging from 10 to 45) was significantly associated with an increased odds of prevalent gout (OR per unit increase, 1.11; 95% CI, 1.09-1.14) and incident gout (OR, 1.10; 95% CI, 1.08-1.13). Associations for many of the loci were of similar magnitude in individuals of non-European ancestry. Detailed characterization of the loci revealed associations with transcript expression and the fractional excretion of urate. Network analyses implicated the inhibins-activins signaling pathways and glucose metabolism in systemic urate control. Conclusion: The novel genetic candidates identified in this urate/gout consortium study, the largest to date, highlight the importance of metabolic control of urate production and urate excretion. The modulation by signaling processes that influence metabolic pathways such as glycolysis and the pentose phosphate pathway appear to be central mechanisms underpinned by the novel GWAS candidates. These findings may have implications for further research into urate-lowering drugs to treat and prevent gout.

Tandem repeat sequence variation as causative cis-eQTLs for protein-coding gene expression variation: the case of CSTB.

Association studies have revealed expression quantitative trait loci (eQTLs) for a large number of genes. However, the causative variants that regulate gene expression levels are generally unknown. We hypothesized that copy-number variation of sequence repeats contribute to the expression variation of some genes. Our laboratory has previously identified that the rare expansion of a repeat c.-174CGGGGCGGGGCG in the promoter region of the CSTB gene causes a silencing of the gene, resulting in progressive myoclonus epilepsy. Here, we genotyped the repeat length and quantified CSTB expression by quantitative real-time polymerase chain reaction in 173 lymphoblastoid cell lines (LCLs) and fibroblast samples from the GenCord collection. The majority of alleles contain either two or three copies of this repeat. Independent analysis revealed that the c.-174CGGGGCGGGGCG repeat length is strongly associated with CSTB expression (P = 3.14 × 10(-11)) in LCLs only. Examination of both genotyped and imputed single-nucleotide polymorphisms (SNPs) within 2 Mb of CSTB revealed that the dodecamer repeat represents the strongest cis-eQTL for CSTB in LCLs. We conclude that the common two or three copy variation is likely the causative cis-eQTL for CSTB expression variation. More broadly, we propose that polymorphic tandem repeats may represent the causative variation of a fraction of cis-eQTLs in the genome.

New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism.

Birth weight within the normal range is associated with a variety of adult-onset diseases, but the mechanisms behind these associations are poorly understood. Previous genome-wide association studies of birth weight identified a variant in the ADCY5 gene associated both with birth weight and type 2 diabetes and a second variant, near CCNL1, with no obvious link to adult traits. In an expanded genome-wide association meta-analysis and follow-up study of birth weight (of up to 69,308 individuals of European descent from 43 studies), we have now extended the number of loci associated at genome-wide significance to 7, accounting for a similar proportion of variance as maternal smoking. Five of the loci are known to be associated with other phenotypes: ADCY5 and CDKAL1 with type 2 diabetes, ADRB1 with adult blood pressure and HMGA2 and LCORL with adult height. Our findings highlight genetic links between fetal growth and postnatal growth and metabolism.

MADAP, a flexible clustering tool for the interpretation of one-dimensional genome annotation data.

A recurring task in the analysis of mass genome annotation data from high-throughput technologies is the identification of peaks or clusters in a noisy signal profile. Examples of such applications are the definition of promoters on the basis of transcription start site profiles, the mapping of transcription factor binding sites based on ChIP-chip data and the identification of quantitative trait loci (QTL) from whole genome SNP profiles. Input to such an analysis is a set of genome coordinates associated with counts or intensities. The output consists of a discrete number of peaks with respective volumes, extensions and center positions. We have developed for this purpose a flexible one-dimensional clustering tool, called MADAP, which we make available as a web server and as standalone program. A set of parameters enables the user to customize the procedure to a specific problem. The web server, which returns results in textual and graphical form, is useful for small to medium-scale applications, as well as for evaluation and parameter tuning in view of large-scale applications, requiring a local installation. The program written in C++ can be freely downloaded from ftp://ftp.epd.unil.ch/pub/software/unix/madap. The MADAP web server can be accessed at http://www.isrec.isb-sib.ch/madap/.

Genome-wide association and longitudinal analyses reveal genetic loci linking pubertal height growth, pubertal timing and childhood adiposity.

The pubertal height growth spurt is a distinctive feature of childhood growth reflecting both the central onset of puberty and local growth factors. Although little is known about the underlying genetics, growth variability during puberty correlates with adult risks for hormone-dependent cancer and adverse cardiometabolic health. The only gene so far associated with pubertal height growth, LIN28B, pleiotropically influences childhood growth, puberty and cancer progression, pointing to shared underlying mechanisms. To discover genetic loci influencing pubertal height and growth and to place them in context of overall growth and maturation, we performed genome-wide association meta-analyses in 18 737 European samples utilizing longitudinally collected height measurements. We found significant associations (P < 1.67 × 10(-8)) at 10 loci, including LIN28B. Five loci associated with pubertal timing, all impacting multiple aspects of growth. In particular, a novel variant correlated with expression of MAPK3, and associated both with increased prepubertal growth and earlier menarche. Another variant near ADCY3-POMC associated with increased body mass index, reduced pubertal growth and earlier puberty. Whereas epidemiological correlations suggest that early puberty marks a pathway from rapid prepubertal growth to reduced final height and adult obesity, our study shows that individual loci associating with pubertal growth have variable longitudinal growth patterns that may differ from epidemiological observations. Overall, this study uncovers part of the complex genetic architecture linking pubertal height growth, the timing of puberty and childhood obesity and provides new information to pinpoint processes linking these traits.

Seventy-five genetic loci influencing the human red blood cell.

Anaemia is a chief determinant of global ill health, contributing to cognitive impairment, growth retardation and impaired physical capacity. To understand further the genetic factors influencing red blood cells, we carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals. Here we identify 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10(-8), which together explain 4-9% of the phenotypic variance per trait. Using expression quantitative trait loci and bioinformatic strategies, we identify 121 candidate genes enriched in functions relevant to red blood cell biology. The candidate genes are expressed preferentially in red blood cell precursors, and 43 have haematopoietic phenotypes in Mus musculus or Drosophila melanogaster. Through open-chromatin and coding-variant analyses we identify potential causal genetic variants at 41 loci. Our findings provide extensive new insights into genetic mechanisms and biological pathways controlling red blood cell formation and function.

Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

ObjectiveCandidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.Research Design and MethodsBy integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS).Results273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations.ConclusionsUsing a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.

Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits.

Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%-27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p<5×10(-8)) in or near PNPLA3, NCAN, and PPP1R3B. We genotype these and 42 other top CT hepatic steatosis-associated SNPs in 592 subjects with biopsy-proven NAFLD from the NASH Clinical Research Network (NASH CRN). In comparisons with 1,405 healthy controls from the Myocardial Genetics Consortium (MIGen), we observe significant associations with histologic NAFLD at variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B. Variants at these five loci exhibit distinct patterns of association with serum lipids, as well as glycemic and anthropometric traits. We identify common genetic variants influencing CT-assessed steatosis and risk of NAFLD. Hepatic steatosis associated variants are not uniformly associated with NASH/fibrosis or result in abnormalities in serum lipids or glycemic and anthropometric traits, suggesting genetic heterogeneity in the pathways influencing these traits.

A genomewide linkage study on suicidality in major depressive disorder confirms evidence for linkage to 2p12.

Suicidal behavior is commonly associated with depression. Twin studies indicate that both suicidality and major depressive disorder (MDD) are heritable. However, epidemiological evidence suggests that the inheritance of suicidality is likely to be independent of the underlying psychiatric disorder, implying a distinct genetic contribution to suicidality. We conducted a genomewide linkage search aiming to detect genomic loci that may harbor susceptibility genes contributing to risk for suicidality in recurrent MDD. Affected sibling pair (ASP) variance components analysis was performed using the Depression Network cohort of 971 ASPs. The quantitative trait measuring suicidality as a broad phenotype, encompassing ideation and suicide attempts, was established from Schedules for Clinical Assessment in Neuropsychiatry interview items. We examined 1,060 genotyped microsatellite markers with an average spacing of 3.3 cM. Empirical thresholds for linkage evidence were set by whole-genome simulations (LOD = 2.71 for genomewide significance, 1.71 for suggestive linkage). No genomewide significant findings were found. Marker D3S1234 on 3p14 achieved suggestive linkage and yielded a maximum LOD of 1.853 (P = 0.0017), loci 9p24.3 and 18q22-q23 achieved LOD scores >1.5. We found some support for linkage to 2p12 (LOD = 1.2, P = 0.0087) which was previously implicated in linkage studies of suicidality. Our follow-up meta-analysis of five studies showed strong linkage to this region (P = 2 × 10(-6) ). In conclusion, this study analyzed suicidality as a continuous trait in MDD. We found modest evidence for linkage on 3p14. Our meta-analysis supports previous evidence of linkage to suicidality on 2p12. Some candidate genes in these regions may plausibly be implicated in suicidality.

Multiple QTLs influencing triglyceride and HDL and total cholesterol levels identified in families with atherogenic dyslipidemia.

We conducted a genome-wide scan using variance components linkage analysis to localize quantitative-trait loci (QTLs) influencing triglyceride (TG), high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol, and total cholesterol (TC) levels in 3,071 subjects from 459 families with atherogenic dyslipidemia. The most significant evidence for linkage to TG levels was found in a subset of Turkish families at 11q22 [logarithm of the odds ratio (LOD)=3.34] and at 17q12 (LOD=3.44). We performed sequential oligogenic linkage analysis to examine whether multiple QTLs jointly influence TG levels in the Turkish families. These analyses revealed loci at 20q13 that showed strong epistatic effects with 11q22 (conditional LOD=3.15) and at 7q36 that showed strong epistatic effects with 17q12 (conditional LOD=3.21). We also found linkage on the 8p21 region for TG in the entire group of families (LOD=3.08). For HDL-C levels, evidence of linkage was identified on chromosome 15 in the Turkish families (LOD=3.05) and on chromosome 5 in the entire group of families (LOD=2.83). Linkage to QTLs for TC was found at 8p23 in the entire group of families (LOD=4.05) and at 5q13 in a subset of Turkish and Mediterranean families (LOD=3.72). These QTLs provide important clues for the further investigation of genes responsible for these complex lipid phenotypes. These data also indicate that a large proportion of the variance of TG levels in the Turkish population is explained by the interaction of multiple genetic loci.

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

Common variants near MC4R are associated with fat mass, weight and risk of obesity.

To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits.