Appraising the role of previously reported risk factors in epithelial ovarian cancer risk: A Mendelian randomization analysis. by James Yarmolinsky, Caroline L Relton, Artitaya Lophatananon, Kenneth Muir, Usha Menon, Aleksandra Gentry-Maharaj, Axel Walther, Jie Zheng, Peter Fasching, Wei Zheng, Woo Yin Ling, Sue K Park, Byoung-Gie Kim, Ji-Yeob Choi, Boyoung Park, George Davey Smith, Richard M Martin, Sarah J Lewis

“…SNPs were combined into multi-allelic inverse-variance-weighted fixed or random effects models to generate effect estimates and 95% confidence intervals (CIs). …”
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The Impact of Gut Microbiota on Chronic Obstructive Pulmonary Disease: A Dual-Sample Mendelian Randomization Study by Niu N, Zhao J, Li H, Miao Y, Chen F, Liu J, Cao L, Ji T, Gao F, Xie S, Zhang Y

“…Hence, we conducted a dual-sample Mendelian randomization study to elucidate the impact of gut microbiota on COPD.Methods: We utilized single-nucleotide polymorphisms (SNPs) as instrumental variables, and the Inverse Variance Weighted (IVW) method for primary analysis. …”
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Genome-wide linkage mapping of Fusarium crown rot in common wheat (Triticum aestivum L.) by Faji Li, Can Guo, Qi Zhao, Weie Wen, Shengnan Zhai, Xinyou Cao, Cheng Liu, Dungong Cheng, Jun Guo, Yan Zi, Aifeng Liu, Jianmin Song, Jianjun Liu, Jindong Liu, Haosheng Li

“…These QTLs accounted for 4.6% to 12.8% of the phenotypic variance. Notably, QFCR.caas-5BL and QFCR.caas-6BS had been previously detected, whereas QFCR.caas-3AL, QFCR.caas-3DL, and QFCR.caas-7DS are novel loci. …”
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Investigating the Causal Relationship of C-Reactive Protein with 32 Complex Somatic and Psychiatric Outcomes: A Large-Scale Cross-Consortium Mendelian Randomization Study. by Bram P Prins, Ali Abbasi, Anson Wong, Ahmad Vaez, Ilja Nolte, Nora Franceschini, Philip E Stuart, Javier Guterriez Achury, Vanisha Mistry, Jonathan P Bradfield, Ana M Valdes, Jose Bras, Aleksey Shatunov, PAGE Consortium, International Stroke Genetics Consortium, Systemic Sclerosis consortium, Treat OA consortium, DIAGRAM Consortium, CARDIoGRAMplusC4D Consortium, ALS consortium, International Parkinson’s Disease Genomics Consortium, Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium, CKDGen consortium, GERAD1 Consortium, International Consortium for Blood Pressure, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Inflammation Working Group of the CHARGE Consortium, Chen Lu, Buhm Han, Soumya Raychaudhuri, Steve Bevan, Maureen D Mayes, Lam C Tsoi, Evangelos Evangelou, Rajan P Nair, Struan F A Grant, Constantin Polychronakos, Timothy R D Radstake, David A van Heel, Melanie L Dunstan, Nicholas W Wood, Ammar Al-Chalabi, Abbas Dehghan, Hakon Hakonarson, Hugh S Markus, James T Elder, Jo Knight, Dan E Arking, Timothy D Spector, Bobby P C Koeleman, Cornelia M van Duijn, Javier Martin, Andrew P Morris, Rinse K Weersma, Cisca Wijmenga, Patricia B Munroe, John R B Perry, Jennie G Pouget, Yalda Jamshidi, Harold Snieder, Behrooz Z Alizadeh

“…The first GRS consisted of four single nucleotide polymorphisms (SNPs) in the CRP gene (GRSCRP), and the second consisted of 18 SNPs that were significantly associated with CRP levels in the largest genome-wide association study (GWAS) to date (GRSGWAS). …”
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The Potential Role of <i>rpoS</i> and <i>ompR</i> in the Acid Resistance and Desiccation Tolerance of <i>Cronobacter malonaticus</i> Strains by Abdlrhman M. Alsonosi, Khaled M. Ibrahim, Bassam A. Elgamoudi, Mahmoud B. Agena, Stephen J. Forsythe

“…The strains showed significant variances in acid tolerance when exposed to simulated gastric acid (pH 3.5) for 2 h. …”
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Using genome‐wide associations and host‐by‐pathogen predictions to identify allelic interactions that control disease resistance by Owen Hudson, Jeremy Brawner

“…Dual genome prediction models improved heritability estimates, error variances, and model accuracy while providing predictions for host‐by‐pathogen interactions that may be used to test the significance of SNP–SNP interactions. …”
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Fine mapping of qRC10-2, a quantitative trait locus for cold tolerance of rice roots at seedling and mature stages. by Ning Xiao, Wei-Nan Huang, Xiao-Xiang Zhang, Yong Gao, Ai-Hong Li, Yi Dai, Ling Yu, Guang-Qing Liu, Cun-Hong Pan, Yu-Hong Li, Zheng-Yuan Dai, Jian-Min Chen

“…A total of 113 codominant simple-sequence repeat (SSR) markers were developed, with a parental polymorphism of 17.3%. Two cold-tolerant QTLs, named qRC10-1 and qRC10-2 were detected on chromosome 10 by composite interval mapping. qRC10-1 (LOD = 3.1, RM171-RM1108) was mapped at 148.3 cM, and qRC10-2 (LOD = 6.1, RM25570-RM304) was mapped at 163.3 cM, which accounted for 9.4% and 32.1% of phenotypic variances, respectively. …”
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Genome wide linkage mapping for black point resistance in a recombinant inbred line population of Zhongmai 578 and Jimai 22 by Tiantian Chen, Lei Li, Dan Liu, Yubing Tian, Lingli Li, Jianqi Zeng, Awais Rasheed, Shuanghe Cao, Xianchun Xia, Zhonghu He, Jindong Liu, Yong Zhang

“…A high-density genetic linkage map of the RIL population was constructed with the wheat 50K single nucleotide polymorphism (SNP) array. Six stable QTLs for black point resistance were detected, QBp.caas-2A, QBp.caas-2B1, QBp.caas-2B2, QBp.caas-2D, QBp.caas-3A, and QBp.caas-5B, which explained 2.1–28.8% of the phenotypic variances. …”
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QTL mapping for leaf rust resistance in a recombinant inbred line population from the cross of wheat cultivars Zhongmai 578/Jimai 22 by Yue Zhang, Lei Li, Zaifeng Li, Yubing Tian, Fangping Yang, Shuanghe Cao, Xianchun Xia, Yongjing Ni, Zhonghu He, Yan Zhang, Yong Zhang, Jindong Liu

“…In the present study, 262 recombinant inbred lines derived from a cross between Zhongmai 578 and Jimai 22 were used to map leaf rust resistance loci using the Wheat 50K single-nucleotide polymorphism (SNP) array across four environments. Four quantitative trait loci (QTL) on chromosomes 2B (2), 5B, and 7B were identified through composite interval mapping, designated QLr.caas-2B.1, QLr.caas-2B.2, QLr.caas-5B, and QLr.caas-7B, respectively, explaining 3.7%–19.6% of the phenotypic variances. …”
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Identification of genetic loci for grain yield-related traits in the wheat population Zhongmai 578/Jimai 22 by Dan LIU, De-hui ZHAO, Jian-qi ZENG, Rabiu Sani SHAWAI, Jing-yang TONG, Ming LI, Fa-ji LI, Shuo ZHOU, Wen-li HU, Xian-chun XIA, Yu-bing TIAN, Qian ZHU, Chun-ping WANG, De-sen WANG, Zhong-hu HE, Jin-dong LIU, Yong ZHANG

“…The parents and 262 RILs were genotyped using the wheat 50K single nucleotide polymorphism (SNP) array. A high-density genetic map was constructed with 1 501 non-redundant bin markers, spanning 2 384.95 cM. …”
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Genetic Basis of Seedling Root Traits in Common Wheat (<i>Triticum aestivum</i> L.) Identified by Genome-Wide Linkage Mapping by Xiaole Ma, Juncheng Wang, Hong Zhang, Lirong Yao, Erjing Si, Baochun Li, Yaxiong Meng, Huajun Wang

“…Genotyping was conducted for the RIL population and their parents using the wheat 90K single-nucleotide polymorphism (SNP) chip. In total, three quantitative trait loci (QTLs) for RL (<i>QRL.gau-1DS</i>, <i>QRL.gau-1DL</i> and <i>QRL.gau-4AL</i>), two QTLs for RA (<i>QRA.gau-1D</i> and <i>QRA.gau-2DL</i>), one locus for RV (<i>QRV.gau-6AS</i>), two loci for RW (<i>QRW.gau-2DL</i> and <i>QRW.gau-2AS</i>), and two loci for RT (<i>QRT.gau-3AS</i> and <i>QRT.gau-6DL</i>) were identified, with each explaining 4.5–8.4% of the phenotypic variances, respectively. …”
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