PLOS ONE: [sortOrder=DATE_NEWEST_FIRST, from=editorLink, sort=Relevance, q=editor:"Greg Gibson"]PLOShttps://journals.plos.org/plosone/webmaster@plos.orgaccelerating the publication of peer-reviewed sciencehttps://journals.plos.org/plosone/search/feed/atom?sortOrder=DATE_NEWEST_FIRST&unformattedQuery=editor:%22Greg%20Gibson%22&from=editorLink&sort=RelevanceAll PLOS articles are Open Access.https://journals.plos.org/plosone/resource/img/favicon.icohttps://journals.plos.org/plosone/resource/img/favicon.ico2024-03-28T10:33:47ZHuman and pathogen genotype-by-genotype interactions in the light of coevolution theoryLars Råberg10.1371/journal.pgen.10106852023-04-06T14:00:00Z2023-04-06T14:00:00Z<p>by Lars Råberg</p>
Antagonistic coevolution (i.e., reciprocal adaptation and counter-adaptation) between hosts and pathogens has long been considered an important driver of genetic variation. However, direct evidence for this is still scarce, especially in vertebrates. The wealth of data on genetics of susceptibility to infectious disease in humans provides an important resource for understanding host–pathogen coevolution, but studies of humans are rarely framed in coevolutionary theory. Here, I review data from human host–pathogen systems to critically assess the evidence for a key assumption of models of host–pathogen coevolution—the presence of host genotype-by-pathogen genotype interactions (G×G). I also attempt to infer whether observed G×G fit best with “gene-for-gene” or “matching allele” models of coevolution. I find that there are several examples of G×G in humans (involving, e.g., <i>ABO</i>, <i>HBB</i>, <i>FUT2</i>, <i>SLC11A1</i>, and HLA genes) that fit assumptions of either gene-for-gene or matching allele models. This means that there is potential for coevolution to drive polymorphism also in humans (and presumably other vertebrates), but further studies are required to investigate how widespread this process is.A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran ProgramSridharan RaghavanJie HuangCatherine TcheandjieuJennifer E. HuffmanElizabeth LitkowskiChang LiuYuk-Lam A. HoHaley Hunter-ZinckHongyu ZhaoEirini MarouliKari E. Norththe VA Million Veteran ProgramEthan LangeLeslie A. LangeBenjamin F. VoightJ. Michael GazianoSaiju PyarajanElizabeth R. HauserPhilip S. TsaoPeter W. F. WilsonKyong-Mi ChangKelly ChoChristopher J. O’DonnellYan V. SunThemistocles L. Assimes10.1371/journal.pgen.10101932022-06-02T14:00:00Z2022-06-02T14:00:00Z<p>by Sridharan Raghavan, Jie Huang, Catherine Tcheandjieu, Jennifer E. Huffman, Elizabeth Litkowski, Chang Liu, Yuk-Lam A. Ho, Haley Hunter-Zinck, Hongyu Zhao, Eirini Marouli, Kari E. North, the VA Million Veteran Program , Ethan Lange, Leslie A. Lange, Benjamin F. Voight, J. Michael Gaziano, Saiju Pyarajan, Elizabeth R. Hauser, Philip S. Tsao, Peter W. F. Wilson, Kyong-Mi Chang, Kelly Cho, Christopher J. O’Donnell, Yan V. Sun, Themistocles L. Assimes</p>
Background <p>Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank.</p> Methods and findings <p>Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n = 222,300) and non-Hispanic Black (AA, n = 58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders.</p> Conclusions <p>We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.</p>Genetic redundancy fuels polygenic adaptation in <i>Drosophila</i>Neda BarghiRaymond ToblerViola NolteAna Marija JakšićFrançois MallardKathrin Anna OtteMarlies DolezalThomas TausRobert KoflerChristian Schlötterer10.1371/journal.pbio.30001282019-02-04T14:00:00Z2019-02-04T14:00:00Z<p>by Neda Barghi, Raymond Tobler, Viola Nolte, Ana Marija Jakšić, François Mallard, Kathrin Anna Otte, Marlies Dolezal, Thomas Taus, Robert Kofler, Christian Schlötterer</p>
The genetic architecture of adaptive traits is of key importance to predict evolutionary responses. Most adaptive traits are polygenic—i.e., result from selection on a large number of genetic loci—but most molecularly characterized traits have a simple genetic basis. This discrepancy is best explained by the difficulty in detecting small allele frequency changes (AFCs) across many contributing loci. To resolve this, we use laboratory natural selection to detect signatures for selective sweeps and polygenic adaptation. We exposed 10 replicates of a <i>Drosophila simulans</i> population to a new temperature regime and uncovered a polygenic architecture of an adaptive trait with high genetic redundancy among beneficial alleles. We observed convergent responses for several phenotypes—e.g., fitness, metabolic rate, and fat content—and a strong polygenic response (99 selected alleles; mean <i>s</i> = 0.059). However, each of these selected alleles increased in frequency only in a subset of the evolving replicates. We discerned different evolutionary paradigms based on the heterogeneous genomic patterns among replicates. Redundancy and quantitative trait (QT) paradigms fitted the experimental data better than simulations assuming independent selective sweeps. Our results show that natural <i>D</i>. <i>simulans</i> populations harbor a vast reservoir of adaptive variation facilitating rapid evolutionary responses using multiple alternative genetic pathways converging at a new phenotypic optimum. This key property of beneficial alleles requires the modification of testing strategies in natural populations beyond the search for convergence on the molecular level.Role of duplicate genes in determining the tissue-selectivity of hereditary diseasesRuth BarshirIdan HekselmanNetta ShemeshMoran SharonLena NovackEsti Yeger-Lotem10.1371/journal.pgen.10073272018-05-03T14:00:00Z2018-05-03T14:00:00Z<p>by Ruth Barshir, Idan Hekselman, Netta Shemesh, Moran Sharon, Lena Novack, Esti Yeger-Lotem</p>
A longstanding puzzle in human genetics is what limits the clinical manifestation of hundreds of hereditary diseases to certain tissues, while their causal genes are expressed throughout the human body. A general conception is that tissue-selective disease phenotypes emerge when masking factors operate in unaffected tissues, but are specifically absent or insufficient in disease-manifesting tissues. Although this conception has critical impact on the understanding of disease manifestation, it was never challenged in a systematic manner across a variety of hereditary diseases and affected tissues. Here, we address this gap in our understanding via rigorous analysis of the susceptibility of over 30 tissues to 112 tissue-selective hereditary diseases. We focused on the roles of paralogs of causal genes, which are presumably capable of compensating for their aberration. We show for the first time at large-scale via quantitative analysis of omics datasets that, preferentially in the disease-manifesting tissues, paralogs are under-expressed relative to causal genes in more than half of the diseases. This was observed for several susceptible tissues and for causal genes with varying number of paralogs, suggesting that imbalanced expression of paralogs increases tissue susceptibility. While for many diseases this imbalance stemmed from up-regulation of the causal gene in the disease-manifesting tissue relative to other tissues, it was often combined with down-regulation of its paralog. Notably in roughly 20% of the cases, this imbalance stemmed only from significant down-regulation of the paralog. Thus, dosage relationships between paralogs appear as important, yet currently under-appreciated, modifiers of disease manifestation.A population genetic interpretation of GWAS findings for human quantitative traitsYuval B. SimonsKevin BullaugheyRichard R. HudsonGuy Sella10.1371/journal.pbio.20029852018-03-16T14:00:00Z2018-03-16T14:00:00Z<p>by Yuval B. Simons, Kevin Bullaughey, Richard R. Hudson, Guy Sella</p>
Human genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes—notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and because genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed-form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWASs is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWASs for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose shortly before or during the Out-of-Africa bottleneck at sites with selection coefficients around <i>s</i> = 10<sup>−3</sup>.Environmental perturbations lead to extensive directional shifts in RNA processingAllison L. RichardsDonovan WatzaAnthony FindleyAdnan AlaziziXiaoquan WenAthma A. PaiRoger Pique-RegiFrancesca Luca10.1371/journal.pgen.10069952017-10-12T14:00:00Z2017-10-12T14:00:00Z<p>by Allison L. Richards, Donovan Watza, Anthony Findley, Adnan Alazizi, Xiaoquan Wen, Athma A. Pai, Roger Pique-Regi, Francesca Luca</p>
Environmental perturbations have large effects on both organismal and cellular traits, including gene expression, but the extent to which the environment affects RNA processing remains largely uncharacterized. Recent studies have identified a large number of genetic variants associated with variation in RNA processing that also have an important role in complex traits; yet we do not know in which contexts the different underlying isoforms are used. Here, we comprehensively characterized changes in RNA processing events across 89 environments in five human cell types and identified 15,300 event shifts (FDR = 15%) comprised of eight event types in over 4,000 genes. Many of these changes occur consistently in the same direction across conditions, indicative of global regulation by trans factors. Accordingly, we demonstrate that environmental modulation of splicing factor binding predicts shifts in intron retention, and that binding of transcription factors predicts shifts in alternative first exon (AFE) usage in response to specific treatments. We validated the mechanism hypothesized for AFE in two independent datasets. Using ATAC-seq, we found altered binding of 64 factors in response to selenium at sites of AFE shift, including ELF2 and other factors in the ETS family. We also performed AFE QTL mapping in 373 individuals and found an enrichment for SNPs predicted to disrupt binding of the ELF2 factor. Together, these results demonstrate that RNA processing is dramatically changed in response to environmental perturbations through specific mechanisms regulated by trans factors.Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populationsJingjing LiangThu H. LeDigna R. Velez EdwardsBamidele O. TayoKyle J. GaultonJennifer A. SmithYingchang LuRichard A. JensenGuanjie ChenLisa R. YanekKaren SchwanderSalman M. TajuddinTamar SoferWonji KimJames KayimaColin A. McKenzieErvin FoxMichael A. NallsJ. Hunter YoungYan V. SunJacqueline M. LaneSylvia CechovaJie ZhouHua TangMyriam FornageSolomon K. MusaniHeming WangJuyoung LeeAdebowale AdeyemoAlbert W. DreisbachTerrence ForresterPei-Lun ChuAnne CappolaMichele K. EvansAlanna C. MorrisonLisa W. MartinKerri L. WigginsQin HuiWei ZhaoRebecca D. JacksonErin B. WareJessica D. FaulAlex P. ReinerMichael BrayJoshua C. DennyThomas H. MosleyWalter PalmasXiuqing GuoGeorge J. PapanicolaouAlan D. PenmanJoseph F. PolakKenneth RiceKen D. TaylorEric BoerwinkleErwin P. BottingerKiang LiuNeil RischSteven C. HuntCharles KooperbergAlan B. ZondermanCathy C. LaurieDiane M. BeckerJianwen CaiRuth J. F. LoosBruce M. PsatyDavid R. WeirSharon L. R. KardiaDonna K. ArnettSungho WonTodd L. EdwardsSusan RedlineRichard S. CooperD. C. RaoJerome I. RotterCharles RotimiDaniel LevyAravinda ChakravartiXiaofeng ZhuNora Franceschini10.1371/journal.pgen.10067282017-05-12T14:00:00Z2017-05-12T14:00:00Z<p>by Jingjing Liang, Thu H. Le, Digna R. Velez Edwards, Bamidele O. Tayo, Kyle J. Gaulton, Jennifer A. Smith, Yingchang Lu, Richard A. Jensen, Guanjie Chen, Lisa R. Yanek, Karen Schwander, Salman M. Tajuddin, Tamar Sofer, Wonji Kim, James Kayima, Colin A. McKenzie, Ervin Fox, Michael A. Nalls, J. Hunter Young, Yan V. Sun, Jacqueline M. Lane, Sylvia Cechova, Jie Zhou, Hua Tang, Myriam Fornage, Solomon K. Musani, Heming Wang, Juyoung Lee, Adebowale Adeyemo, Albert W. Dreisbach, Terrence Forrester, Pei-Lun Chu, Anne Cappola, Michele K. Evans, Alanna C. Morrison, Lisa W. Martin, Kerri L. Wiggins, Qin Hui, Wei Zhao, Rebecca D. Jackson, Erin B. Ware, Jessica D. Faul, Alex P. Reiner, Michael Bray, Joshua C. Denny, Thomas H. Mosley, Walter Palmas, Xiuqing Guo, George J. Papanicolaou, Alan D. Penman, Joseph F. Polak, Kenneth Rice, Ken D. Taylor, Eric Boerwinkle, Erwin P. Bottinger, Kiang Liu, Neil Risch, Steven C. Hunt, Charles Kooperberg, Alan B. Zonderman, Cathy C. Laurie, Diane M. Becker, Jianwen Cai, Ruth J. F. Loos, Bruce M. Psaty, David R. Weir, Sharon L. R. Kardia, Donna K. Arnett, Sungho Won, Todd L. Edwards, Susan Redline, Richard S. Cooper, D. C. Rao, Jerome I. Rotter, Charles Rotimi, Daniel Levy, Aravinda Chakravarti, Xiaofeng Zhu, Nora Franceschini</p>
Hypertension is a leading cause of global disease, mortality, and disability. While individuals of African descent suffer a disproportionate burden of hypertension and its complications, they have been underrepresented in genetic studies. To identify novel susceptibility loci for blood pressure and hypertension in people of African ancestry, we performed both single and multiple-trait genome-wide association analyses. We analyzed 21 genome-wide association studies comprised of 31,968 individuals of African ancestry, and validated our results with additional 54,395 individuals from multi-ethnic studies. These analyses identified nine loci with eleven independent variants which reached genome-wide significance (P < 1.25×10<sup>−8</sup>) for either systolic and diastolic blood pressure, hypertension, or for combined traits. Single-trait analyses identified two loci (<i>TARID</i>/<i>TCF21</i> and <i>LLPH/TMBIM4</i>) and multiple-trait analyses identified one novel locus (<i>FRMD3)</i> for blood pressure. At these three loci, as well as at <i>GRP20/CDH17</i>, associated variants had alleles common only in African-ancestry populations. Functional annotation showed enrichment for genes expressed in immune and kidney cells, as well as in heart and vascular cells/tissues. Experiments driven by these findings and using angiotensin-II induced hypertension in mice showed altered kidney mRNA expression of six genes, suggesting their potential role in hypertension. Our study provides new evidence for genes related to hypertension susceptibility, and the need to study African-ancestry populations in order to identify biologic factors contributing to hypertension.Investigating the case of human nose shape and climate adaptationArslan A. ZaidiBrooke C. MatternPeter ClaesBrian McEcoyCris HughesMark D. Shriver10.1371/journal.pgen.10066162017-03-16T14:00:00Z2017-03-16T14:00:00Z<p>by Arslan A. Zaidi, Brooke C. Mattern, Peter Claes, Brian McEcoy, Cris Hughes, Mark D. Shriver</p>
The evolutionary reasons for variation in nose shape across human populations have been subject to continuing debate. An import function of the nose and nasal cavity is to condition inspired air before it reaches the lower respiratory tract. For this reason, it is thought the observed differences in nose shape among populations are not simply the result of genetic drift, but may be adaptations to climate. To address the question of whether local adaptation to climate is responsible for nose shape divergence across populations, we use Qst–Fst comparisons to show that nares width and alar base width are more differentiated across populations than expected under genetic drift alone. To test whether this differentiation is due to climate adaptation, we compared the spatial distribution of these variables with the global distribution of temperature, absolute humidity, and relative humidity. We find that width of the nares is correlated with temperature and absolute humidity, but not with relative humidity. We conclude that some aspects of nose shape may indeed have been driven by local adaptation to climate. However, we think that this is a simplified explanation of a very complex evolutionary history, which possibly also involved other non-neutral forces such as sexual selection.Family Based Whole Exome Sequencing Reveals the Multifaceted Role of Notch Signaling in Congenital Heart DiseaseChristoph PreussMelanie CapredonFlorian WünnemannPhilippe ChetailleAndrea PrinceBeatrice GodardSeverine LeclercNara SobreiraHua LingPhilip AwadallaMaryse ThibeaultPaul KhairyMIBAVA Leducq consortiumMark E. SamuelsGregor Andelfinger10.1371/journal.pgen.10063352016-10-19T14:00:00Z2016-10-19T14:00:00Z<p>by Christoph Preuss, Melanie Capredon, Florian Wünnemann, Philippe Chetaille, Andrea Prince, Beatrice Godard, Severine Leclerc, Nara Sobreira, Hua Ling, Philip Awadalla, Maryse Thibeault, Paul Khairy, MIBAVA Leducq consortium , Mark E. Samuels, Gregor Andelfinger</p>
Left-ventricular outflow tract obstructions (LVOTO) encompass a wide spectrum of phenotypically heterogeneous heart malformations which frequently cluster in families. We performed family based whole-exome and targeted re-sequencing on 182 individuals from 51 families with multiple affected members. Central to our approach is the family unit which serves as a reference to identify causal genotype-phenotype correlations. Screening a multitude of 10 overlapping phenotypes revealed disease associated and co-segregating variants in 12 families. These rare or novel protein altering mutations cluster predominantly in genes (<i>NOTCH1</i>, <i>ARHGAP31</i>, <i>MAML1</i>, <i>SMARCA4</i>, <i>JARID2</i>, <i>JAG1</i>) along the Notch signaling cascade. This is in line with a significant enrichment (Wilcoxon, p< 0.05) of variants with a higher pathogenicity in the Notch signaling pathway in patients compared to controls. The significant enrichment of novel protein truncating and missense mutations in <i>NOTCH1</i> highlights the allelic and phenotypic heterogeneity in our pediatric cohort. We identified novel co-segregating pathogenic mutations in <i>NOTCH1</i> associated with left and right-sided cardiac malformations in three independent families with a total of 15 affected individuals. In summary, our results suggest that a small but highly pathogenic fraction of family specific mutations along the Notch cascade are a common cause of LVOTO.Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPDWei SunKaterina KechrisSean JacobsonM. Bradley DrummondGregory A. HawkinsJenny YangTing-huei ChenPedro Miguel QuibreraWayne AndersonR. Graham BarrPatricia V. BastaEugene R. BleeckerTerri BeatyRichard CasaburiPeter CastaldiMichael H. ChoAlejandro ComellasJames D. CrapoGerard CrinerDawn DemeoStephanie A. ChristensonDavid J. CouperJeffrey L. CurtisClaire M. DoerschukChristine M. FreemanNatalia A. GouskovaMeiLan K. HanNicola A. HananiaNadia N. HanselCraig P. HershEric A. HoffmanRobert J. KanerRichard E. KannerEric C. KleerupSharon LutzFernando J. MartinezDeborah A. MeyersStephen P. PetersElizabeth A. ReganStephen I. RennardMary Beth ScholandEdwin K. SilvermanPrescott G. WoodruffWanda K. O’NealRussell P. BowlerSPIROMICS Research GroupCOPDGene Investigators10.1371/journal.pgen.10060112016-08-17T14:00:00Z2016-08-17T14:00:00Z<p>by Wei Sun, Katerina Kechris, Sean Jacobson, M. Bradley Drummond, Gregory A. Hawkins, Jenny Yang, Ting-huei Chen, Pedro Miguel Quibrera, Wayne Anderson, R. Graham Barr, Patricia V. Basta, Eugene R. Bleecker, Terri Beaty, Richard Casaburi, Peter Castaldi, Michael H. Cho, Alejandro Comellas, James D. Crapo, Gerard Criner, Dawn Demeo, Stephanie A. Christenson, David J. Couper, Jeffrey L. Curtis, Claire M. Doerschuk, Christine M. Freeman, Natalia A. Gouskova, MeiLan K. Han, Nicola A. Hanania, Nadia N. Hansel, Craig P. Hersh, Eric A. Hoffman, Robert J. Kaner, Richard E. Kanner, Eric C. Kleerup, Sharon Lutz, Fernando J. Martinez, Deborah A. Meyers, Stephen P. Peters, Elizabeth A. Regan, Stephen I. Rennard, Mary Beth Scholand, Edwin K. Silverman, Prescott G. Woodruff, Wanda K. O’Neal, Russell P. Bowler, SPIROMICS Research Group , COPDGene Investigators </p>
Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10<sup>−10</sup>) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10<sup>−392</sup>) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = <i>GC</i>). Some of these pQTLs [<i>e</i>.<i>g</i>., pQTLs for VDBP, sRAGE (gene = <i>AGER</i>), surfactant protein D (gene = <i>SFTPD</i>), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (<i>cis</i>), but distant (<i>trans</i>) pQTL SNPs in the <i>ABO</i> blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R<sup>2</sup>) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.High Resolution Genomic Scans Reveal Genetic Architecture Controlling Alcohol Preference in Bidirectionally Selected Rat ModelChiao-Ling LoAmy C. LossieTiebing LiangYunlong LiuXiaoling XueiLawrence LumengFeng C. ZhouWilliam M. Muir10.1371/journal.pgen.10061782016-08-04T14:00:00Z2016-08-04T14:00:00Z<p>by Chiao-Ling Lo, Amy C. Lossie, Tiebing Liang, Yunlong Liu, Xiaoling Xuei, Lawrence Lumeng, Feng C. Zhou, William M. Muir</p>
Investigations on the influence of nature vs. nurture on Alcoholism (Alcohol Use Disorder) in human have yet to provide a clear view on potential genomic etiologies. To address this issue, we sequenced a replicated animal model system bidirectionally-selected for alcohol preference (AP). This model is uniquely suited to map genetic effects with high reproducibility, and resolution. The origin of the rat lines (an 8-way cross) resulted in small haplotype blocks (HB) with a corresponding high level of resolution. We sequenced DNAs from 40 samples (10 per line of each replicate) to determine allele frequencies and HB. We achieved ~46X coverage per line and replicate. Excessive differentiation in the genomic architecture between lines, across replicates, termed signatures of selection (SS), were classified according to gene and region. We identified SS in 930 genes associated with AP. The majority (50%) of the SS were confined to single gene regions, the greatest numbers of which were in promoters (284) and intronic regions (169) with the least in exon's (4), suggesting that differences in AP were primarily due to alterations in regulatory regions. We confirmed previously identified genes and found many new genes associated with AP. Of those newly identified genes, several demonstrated neuronal function involved in synaptic memory and reward behavior, e.g. ion channels (<i>Kcnf1</i>, <i>Kcnn3</i>, <i>Scn5a</i>), excitatory receptors (<i>Grin2a</i>, <i>Gria3</i>, <i>Grip1)</i>, neurotransmitters (<i>Pomc</i>), and synapses (<i>Snap29</i>). This study not only reveals the polygenic architecture of AP, but also emphasizes the importance of regulatory elements, consistent with other complex traits.Conservation of Distinct Genetically-Mediated Human Cortical PatternQian PengAndrew SchorkHauke BartschMin-Tzu LoMatthew S. PanizzonPediatric Imaging, Neurocognition and Genetics StudyAlzheimer’s Disease Neuroimaging InitiativeLars T. WestlyeWilliam S. KremenTerry L. JerniganStephanie Le HellardVidar M. SteenThomas EspesethMatt HuentelmanAsta K. HåbergIngrid AgartzSrdjan DjurovicOle A. AndreassenAnders M. DaleNicholas J. SchorkChi-Hua Chen10.1371/journal.pgen.10061432016-07-26T14:00:00Z2016-07-26T14:00:00Z<p>by Qian Peng, Andrew Schork, Hauke Bartsch, Min-Tzu Lo, Matthew S. Panizzon, Pediatric Imaging, Neurocognition and Genetics Study , Alzheimer’s Disease Neuroimaging Initiative , Lars T. Westlye, William S. Kremen, Terry L. Jernigan, Stephanie Le Hellard, Vidar M. Steen, Thomas Espeseth, Matt Huentelman, Asta K. Håberg, Ingrid Agartz, Srdjan Djurovic, Ole A. Andreassen, Anders M. Dale, Nicholas J. Schork, Chi-Hua Chen</p>
The many subcomponents of the human cortex are known to follow an anatomical pattern and functional relationship that appears to be highly conserved between individuals. This suggests that this pattern and the relationship among cortical regions are important for cortical function and likely shaped by genetic factors, although the degree to which genetic factors contribute to this pattern is unknown. We assessed the genetic relationships among 12 cortical surface areas using brain images and genotype information on 2,364 unrelated individuals, brain images on 466 twin pairs, and transcriptome data on 6 postmortem brains in order to determine whether a consistent and biologically meaningful pattern could be identified from these very different data sets. We find that the patterns revealed by each data set are highly consistent (p<10<sup>−3</sup>), and are biologically meaningful on several fronts. For example, close genetic relationships are seen in cortical regions within the same lobes and, the frontal lobe, a region showing great evolutionary expansion and functional complexity, has the most distant genetic relationship with other lobes. The frontal lobe also exhibits the most distinct expression pattern relative to the other regions, implicating a number of genes with known functions mediating immune and related processes. Our analyses reflect one of the first attempts to provide an assessment of the biological consistency of a genetic phenomenon involving the brain that leverages very different types of data, and therefore is not just statistical replication which purposefully use very similar data sets.Gene-Environment Interactions in Stress Response Contribute Additively to a Genotype-Environment InteractionTakeshi MatsuiIan M. Ehrenreich10.1371/journal.pgen.10061582016-07-20T14:00:00Z2016-07-20T14:00:00Z<p>by Takeshi Matsui, Ian M. Ehrenreich</p>
How combinations of gene-environment interactions collectively give rise to genotype-environment interactions is not fully understood. To shed light on this problem, we genetically dissected an environment-specific poor growth phenotype in a cross of two budding yeast strains. This phenotype is detectable when certain segregants are grown on ethanol at 37°C (‘E37’), a condition that differs from the standard culturing environment in both its carbon source (ethanol as opposed to glucose) and temperature (37°C as opposed to 30°C). Using recurrent backcrossing with phenotypic selection, we identified 16 contributing loci. To examine how these loci interact with each other and the environment, we focused on a subset of four loci that together can lead to poor growth in E37. We measured the growth of all 16 haploid combinations of alleles at these loci in all four possible combinations of carbon source (ethanol or glucose) and temperature (30 or 37°C) in a nearly isogenic population. This revealed that the four loci act in an almost entirely additive manner in E37. However, we also found that these loci have weaker effects when only carbon source or temperature is altered, suggesting that their effect magnitudes depend on the severity of environmental perturbation. Consistent with such a possibility, cloning of three causal genes identified factors that have unrelated functions in stress response. Thus, our results indicate that polymorphisms in stress response can show effects that are intensified by environmental stress, thereby resulting in major genotype-environment interactions when multiple of these variants co-occur.A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European PopulationsHan ZhangWilliam WheelerPaula L. HylandYifan YangJianxin ShiNilanjan ChatterjeeKai Yu10.1371/journal.pgen.10061222016-06-30T14:00:00Z2016-06-30T14:00:00Z<p>by Han Zhang, William Wheeler, Paula L. Hyland, Yifan Yang, Jianxin Shi, Nilanjan Chatterjee, Kai Yu</p>
Meta-analysis of multiple genome-wide association studies (GWAS) has become an effective approach for detecting single nucleotide polymorphism (SNP) associations with complex traits. However, it is difficult to integrate the readily accessible SNP-level summary statistics from a meta-analysis into more powerful multi-marker testing procedures, which generally require individual-level genetic data. We developed a general procedure called Summary based Adaptive Rank Truncated Product (sARTP) for conducting gene and pathway meta-analysis that uses only SNP-level summary statistics in combination with genotype correlation estimated from a panel of individual-level genetic data. We demonstrated the validity and power advantage of sARTP through empirical and simulated data. We conducted a comprehensive pathway-based meta-analysis with sARTP on type 2 diabetes (T2D) by integrating SNP-level summary statistics from two large studies consisting of 19,809 T2D cases and 111,181 controls with European ancestry. Among 4,713 candidate pathways from which genes in neighborhoods of 170 GWAS established T2D loci were excluded, we detected 43 T2D globally significant pathways (with Bonferroni corrected p-values < 0.05), which included the insulin signaling pathway and T2D pathway defined by KEGG, as well as the pathways defined according to specific gene expression patterns on pancreatic adenocarcinoma, hepatocellular carcinoma, and bladder carcinoma. Using summary data from 8 eastern Asian T2D GWAS with 6,952 cases and 11,865 controls, we showed 7 out of the 43 pathways identified in European populations remained to be significant in eastern Asians at the false discovery rate of 0.1. We created an R package and a web-based tool for sARTP with the capability to analyze pathways with thousands of genes and tens of thousands of SNPs.Network Analysis of Genome-Wide Selective Constraint Reveals a Gene Network Active in Early Fetal Brain Intolerant of MutationJinmyung ChoiParisa ShooshtariKaitlin E. SamochaMark J. DalyChris Cotsapas10.1371/journal.pgen.10061212016-06-15T14:00:00Z2016-06-15T14:00:00Z<p>by Jinmyung Choi, Parisa Shooshtari, Kaitlin E. Samocha, Mark J. Daly, Chris Cotsapas</p>
Using robust, integrated analysis of multiple genomic datasets, we show that genes depleted for non-synonymous <i>de novo</i> mutations form a subnetwork of 72 members under strong selective constraint. We further show this subnetwork is preferentially expressed in the early development of the human hippocampus and is enriched for genes mutated in neurological Mendelian disorders. We thus conclude that carefully orchestrated developmental processes are under strong constraint in early brain development, and perturbations caused by mutation have adverse outcomes subject to strong purifying selection. Our findings demonstrate that selective forces can act on groups of genes involved in the same process, supporting the notion that purifying selection can act coordinately on multiple genes. Our approach provides a statistically robust, interpretable way to identify the tissues and developmental times where groups of disease genes are active.