Trans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained. by Ying Wu, Lindsay L Waite, Anne U Jackson, Wayne H-H Sheu, Steven Buyske, Devin Absher, Donna K Arnett, Eric Boerwinkle, Lori L Bonnycastle, Cara L Carty, Iona Cheng, Barbara Cochran, Damien C Croteau-Chonka, Logan Dumitrescu, Charles B Eaton, Nora Franceschini, Xiuqing Guo, Brian E Henderson, Lucia A Hindorff, Eric Kim, Leena Kinnunen, Pirjo Komulainen, Wen-Jane Lee, Loic Le Marchand, Yi Lin, Jaana Lindström, Oddgeir Lingaas-Holmen, Sabrina L Mitchell, Narisu Narisu, Jennifer G Robinson, Fred Schumacher, Alena Stančáková, Jouko Sundvall, Yun-Ju Sung, Amy J Swift, Wen-Chang Wang, Lynne Wilkens, Tom Wilsgaard, Alicia M Young, Linda S Adair, Christie M Ballantyne, Petra Bůžková, Aravinda Chakravarti, Francis S Collins, David Duggan, Alan B Feranil, Low-Tone Ho, Yi-Jen Hung, Steven C Hunt, Kristian Hveem, Jyh-Ming J Juang, Antero Y Kesäniemi, Johanna Kuusisto, Markku Laakso, Timo A Lakka, I-Te Lee, Mark F Leppert, Tara C Matise, Leena Moilanen, Inger Njølstad, Ulrike Peters, Thomas Quertermous, Rainer Rauramaa, Jerome I Rotter, Jouko Saramies, Jaakko Tuomilehto, Matti Uusitupa, Tzung-Dau Wang, Michael Boehnke, Christopher A Haiman, Yii-Der I Chen, Charles Kooperberg, Themistocles L Assimes, Dana C Crawford, Chao A Hsiung, Kari E North, Karen L Mohlke

“…At these loci, accounting for all signals led to a 1.3- to 1.8-fold increase in the explained phenotypic variance compared to the strongest signals. Distinct signals across ancestry groups were identified at PCSK9 and APOA5. …”
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Contribution of crossing over to genetic variance in maize and wheat populations by Rex Bernardo

“…Abstract Crossing over breaks linkages and leads to a wider array of allele combinations. My objective was to assess the contribution of crossing over to genetic variance (VG) in maize (Zea mays L.) and wheat (Triticum aestivum L.). …”
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Mining the human phenome using allelic scores that index biological intermediates. by David M Evans, Marie Jo A Brion, Lavinia Paternoster, John P Kemp, George McMahon, Marcus Munafò, John B Whitfield, Sarah E Medland, Grant W Montgomery, GIANT Consortium, CRP Consortium, TAG Consortium, Nicholas J Timpson, Beate St Pourcain, Debbie A Lawlor, Nicholas G Martin, Abbas Dehghan, Joel Hirschhorn, George Davey Smith

“…Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. …”
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A Statistical Variance Components Framework for Mapping Imprinted Quantitative Trait Locus in Experimental Crosses by Gengxin Li, Yuehua Cui

“…We propose a new iQTL variance partition method based on the nature of marker alleles shared identical-by-decent (IBD) in inbred lines. …”
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Analysis of locus estimates of allelic diversity of STR markers in a sample of breeding bulls by V. M. Kuznetsov

“…The first one took into account 59.4 % of the total variance, had the highest loads in intra-breed diversity data and was defined as an «alpha component». …”
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Genetic variance in the spinocerebellar ataxia type 2 (ATXN2) gene in children with severe early onset obesity. by Karla P Figueroa, Sadaf Farooqi, Kristopher Harrup, Johnathan Frank, Stephen O'Rahilly, Stefan M Pulst

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Alleles of Ppd-1 genes that control sensitivity to photoperiod in a number of bread winter wheat genotypes by А. О. Бакума, Г. О. Чеботар, А. В. Ткачук, С. В. Чеботар, Т. З. Москалець, В. В. Москалeць

“…The following methods were used in the work: DNA extraction, allele-specific PCR, agarose and polyacrylamide gel electrophoresis, analysis of variance. …”
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Allelic variation and effects of earliness <i> per se (Eps) </i> genes in wheat cultivars of Pakistan by Benish Iltaf, Humaira Qayyum, Hafiz Muhammad Suleman, Ali Raza, Sana ur-Rehman, Hamna Khalid, Kainat Abbasi, Kinza Tanvir, Misbah Mukhtar, Sima Zulfiqar, Abdul Mujeeb Kazi, Awais Rasheed

“…When vernalization and photoperiod criteria are met, Eps genes account for the variance in flowering time. The objective of the study was to decipher the allelic variations for Eps genes in the wheat cultivars of Pakistan and draw their association with agronomic traits. …”
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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

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The CFTR Met 470 allele is associated with lower birth rates in fertile men from a population isolate. by Gülüm Kosova, Joseph K Pickrell, Joanna L Kelley, Patrick F McArdle, Alan R Shuldiner, Mark Abney, Carole Ober

“…The Met470Val locus explained 4.36% of the phenotypic variance in birth rate, and men homozygous for the Met470 allele had 0.56 fewer children on average compared to Val470 carrier men. …”
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Estimating multiplicity of infection, haplotype frequencies, and linkage disequilibria from multi-allelic markers for molecular disease surveillance. by Henri Christian Junior Tsoungui Obama, Kristan Alexander Schneider

“…<h4>Methods</h4>To avoid bias, we introduce a statistical framework to estimate haplotype frequencies alongside MOI from a pair of multi-allelic molecular markers. Estimates are based on maximum likelihood using the expectation-maximization (EM)-algorithm. …”
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Genome‐Wide Search for Nonadditive Allele Effects Identifies PSKH2 as Involved in the Variability of Factor V Activity by Blandine Gendre, Angel Martinez‐Perez, Marcus E. Kleber, Astrid van Hylckama Vlieg, Anne Boland, Robert Olaso, Marine Germain, Gaëlle Munsch, Angela Patricia Moissl, Pierre Suchon, Juan Carlos Souto, José Manuel Soria, Jean‐François Deleuze, Winfried März, Frits R. Rosendaal, Maria Sabater‐Lleal, Pierre‐Emmanuel Morange, David‐Alexandre Trégouët

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An in-depth characterization of the major psoriasis susceptibility locus identifies candidate susceptibility alleles within an HLA-C enhancer element. by Alex Clop, Anna Bertoni, Sarah L Spain, Michael A Simpson, Venu Pullabhatla, Raul Tonda, Christian Hundhausen, Paola Di Meglio, Pieter De Jong, Adrian C Hayday, Frank O Nestle, Jonathan N Barker, Robert J A Bell, Francesca Capon, Richard C Trembath

“…However, it is unclear whether HLA-Cw*0602 is itself the causal PSORS1 allele, especially as the role of SNPs that may affect its expression has not been investigated. …”
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Identification of favorable alleles from exotic Upland cotton lines for fiber quality improvement using multiple association models by Hrithik Mangla, Min Liu, Deepak Vitrakoti, Rama Vamsi Somala, Tariq Shehzad, Rahul Chandnani, Sayan Das, Jason G. Wallace, John L. Snider, Don C. Jones, Peng W. Chee, Andrew H. Paterson

“…At 15 of these loci, DES56 or the elite allele improved upon all the exotics. For another 15, only a single of the five exotics improved upon the elite allele, suggesting the rare alleles that may not have been sampled in the cotton domestication or improvement. …”
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Effect of Glu-B3 Allelic Variation on Sodium Dodecyl Sulfate Sedimentation Volume in Common Wheat (Triticum aestivum L.) by Hongqi Si, Manli Zhao, Fuxia He, Chuanxi Ma

“…For the bread-making quality, the most desirable alleles Glu-B3b and Glu-B3g become more and more popular and the least desirable alleles Glu-B3a and Glu-B3c got less and less in modern improved cultivars, suggesting that wheat grain quality in China has been significantly improved through breeding effort.…”
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Meta-analysis of genome-wide scans for total body BMD in children and adults reveals allelic heterogeneity and age-specific effects at the WNT16 locus. by Carolina Medina-Gomez, John P Kemp, Karol Estrada, Joel Eriksson, Jeff Liu, Sjur Reppe, David M Evans, Denise H M Heppe, Liesbeth Vandenput, Lizbeth Herrera, Susan M Ring, Claudia J Kruithof, Nicholas J Timpson, M Carola Zillikens, Ole K Olstad, Hou-Feng Zheng, J Brent Richards, Beate St Pourcain, Albert Hofman, Vincent W V Jaddoe, George Davey Smith, Mattias Lorentzon, Kaare M Gautvik, André G Uitterlinden, Robert Brommage, Claes Ohlsson, Jonathan H Tobias, Fernando Rivadeneira

“…The top signal mapping in the surroundings of WNT16 was replicated across studies with a meta-analysis P = 2.6 × 10(-31) and an effect size explaining between 0.6%-1.8% of TB-BMD variance. Conditional analyses on this signal revealed a secondary signal for total body BMD (P = 1.42 × 10(-10)) for rs4609139 and mapping to C7orf58. …”
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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

“…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. The resistance alleles of QBp.caas-2B1 and QBp.caas-2B2 were contributed by Zhongmai 578 while the others were from Jimai 22. …”
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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

“…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. The resistance alleles at QLr.caas-2B.1, QLr.caas-5B, and QLr.caas-7B originated from Zhongmai 578, while that at QLr.caas-2B.2 came from Jimai 22. …”
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Genetic variability for yield and related traits among wild accessions of cowpea and cross-compatibility with cultivated accessions by Olayiwola Richard Oluseyi, Taiwo Bolaji Samuel, Oyetunde Oyeboade Adebiyi, Ariyo Omolayo Johnson

“…Cowpea is an important legume, and its wild relatives could be reliable sources of favorable alleles for genetic diversity and improvement of important traits. …”
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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

“…The favorable alleles of QFCR.caas-3DL and QFCR.caas-5BL were contributed by Zhoumai 16, while the favorable alleles for QFCR.caas-3AL, QFCR.caas-6BS, and QFCR.caas-7DS originated from Gaocheng 8901. …”
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