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Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant ( p < 5 × 10 -8 ) and suggestive ( p < 1 × 10 -6 ) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose ( FOXP1, MBOAT4, SKP2, STIM1, STX4 ), brain ( BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A ), and liver ( BRI3, FOXP1 ) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects., Competing Interests: IN is now employed by Celgene. BMP serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. SA is employed by and holds equity in 23andMe, Inc. Authors BK, MW, and CGa were employed by Helmholtz Zentrum München. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 de las Fuentes, Schwander, Brown, Bentley, Winkler, Sung, Munroe, Miller, Aschard, Aslibekyan, Bartz, Bielak, Chai, Cheng, Dorajoo, Feitosa, Guo, Hartwig, Horimoto, Kolčić, Lim, Liu, Manning, Marten, Musani, Noordam, Padmanabhan, Rankinen, Richard, Ridker, Smith, Vojinovic, Zonderman, Alver, Boissel, Christensen, Freedman, Gao, Giulianini, Harris, He, Hsu, Kühnel, Laguzzi, Li, Lyytikäinen, Nolte, Poveda, Rauramaa, Riaz, Robino, Sofer, Takeuchi, Tayo, van der Most, Verweij, Ware, Weiss, Wen, Yanek, Zhan, Amin, Arking, Ballantyne, Boerwinkle, Brody, Broeckel, Campbell, Canouil, Chai, Ida Chen, Chen, Chitrala, Concas, de Faire, de Mutsert, de Silva, de Vries, Do, Faul, Fisher, Floyd, Forrester, Friedlander, Girotto, Gu, Hallmans, Heikkinen, Heng, Homuth, Hunt, Ikram, Jacobs, Kavousi, Khor, Kilpeläinen, Koh, Komulainen, Langefeld, Liang, Liu, Liu, Lohman, Mägi, Manichaikul, McKenzie, Meitinger, Milaneschi, Nauck, Nelson, O’Connell, Palmer, Pereira, Perls, Peters, Polašek, Raitakari, Rice, Rice, Rich, Sabanayagam, Schreiner, Shu, Sidney, Sims, Smith, Starr, Strauch, Tai, Taylor, Tsai, Uitterlinden, van Heemst, Waldenberger, Wang, Wei, Wilson, Xuan, Yao, Yu, Yuan, Zhao, Becker, Bonnefond, Bowden, Cooper, Deary, Divers, Esko, Franks, Froguel, Gieger, Jonas, Kato, Lakka, Leander, Lehtimäki, Magnusson, North, Ntalla, Penninx, Samani, Snieder, Spedicati, van der Harst, Völzke, Wagenknecht, Weir, Wojczynski, Wu, Zheng, Zhu, Bouchard, Chasman, Evans, Fox, Gudnason, Hayward, Horta, Kardia, Krieger, Mook-Kanamori, Peyser, Province, Psaty, Rudan, Sim, Smith, van Dam, van Duijn, Wong, Arnett, Rao, Gauderman, Liu, Morrison, Rotter and Fornage.)

Genomic selection (GS) is transforming plant and animal breeding, but its practical implementation for complex traits and multi-environmental trials remains challenging. To address this issue, this study investigates the integration of environmental information with genotypic information in GS. The study proposes the use of two feature selection methods (Pearson's correlation and Boruta) for the integration of environmental information. Results indicate that the simple incorporation of environmental covariates may increase or decrease prediction accuracy depending on the case. However, optimal incorporation of environmental covariates using feature selection significantly improves prediction accuracy in four out of six datasets between 14.25% and 218.71% under a leave one environment out cross validation scenario in terms of Normalized Root Mean Squared Error, but not relevant gain was observed in terms of Pearson´s correlation. In two datasets where environmental covariates are unrelated to the response variable, feature selection is unable to enhance prediction accuracy. Therefore, the study provides empirical evidence supporting the use of feature selection to improve the prediction power of GS., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Montesinos-López, Crespo-Herrera, Saint Pierre, Bentley, de la Rosa-Santamaria, Ascencio-Laguna, Agbona, Gerard, Montesinos-López and Crossa.)

Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat ( Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R 2 = 0.39-0.47) than genomic data (approximately R 2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered., Competing Interests: JL and EB were employed by KWS UK Ltd; CB and PJ were employed by RAGT UK; SB, EF, and BP were employed by Limagrain UK Ltd; SS and CH were employed by Elsoms Wheat Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Jackson, Buntjer, Bentley, Lage, Byrne, Burt, Jack, Berry, Flatman, Poupard, Smith, Hayes, Barber, Love, Gaynor, Gorjanc, Howell, Mackay, Hickey and Ober.)

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium's Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and 'omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 ( ALDH2 ) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2 , which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2 , which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4 ×current smoking (HDL), PTPRZ1 ×ever-smoking (HDL), SYN2 ×current smoking (pulse pressure), and TMEM116 ×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Majarian, Bentley, Laville, Brown, Chasman, de Vries, Feitosa, Franceschini, Gauderman, Marchek, Levy, Morrison, Province, Rao, Schwander, Sung, Rotimi, Aschard, Gu and Manning.)

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL&#95;M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL&#95;M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL&#95;M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL&#95;M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Montesinos-López, Montesinos-López, Mosqueda-González, Bentley, Lillemo, Varshney and Crossa.)

Variations in lipid levels are attributed partly to genetic factors. Genome-wide association studies (GWASs) mainly performed in European, African American and Asian cohorts have identified variants associated with LDL-C, HDL-C, total cholesterol (TC) and triglycerides (TG), but few studies have been performed in sub-Saharan Africans. This study evaluated the effect of single nucleotide variants (SNVs) in eight candidate loci ( ABCA1 , LCAT , LPL , PON1 , CETP , PCSK9 , MVK , and MMAB ) on lipid levels among 1855 Ghanaian adults. All lipid levels were measured directly using an automated analyser. DNA was extracted and genotyped using the H3Africa SNV array. Linear regression models were used to test the association between SNVs and log-transformed lipid levels, adjusting for sex, age and waist circumference. In addition Bonferroni correction was performed to account for multiple testing. Several variants of CETP , LCAT , PCSK9 , and PON1 (MAF > 0.05) were associated with HDL-C, LDL-C and TC levels at p < 0.05. The lead variants for association with HDL-C were rs17231520 in CETP (β = 0.139, p < 0.0001) and rs1109166 in LCAT (β = -0.044, p = 0.028). Lower LDL-C levels were associated with an intronic variant in PCSK9 (rs11806638 [β = -0.055, p = 0.027]) and increased TC was associated with a variant in PON1 (rs854558 [β = 0.040, p = 0.020]). In silico functional analyses indicated that these variants likely influence gene function through their effect on gene transcription. We replicated a strong association between CETP variants and HDL-C and between PCSK9 variant and LDL-C in West Africans, with two potentially functional variants and identified three novel variants in linkage disequilibrium in PON1 which were associated with increasing TC levels in Ghanaians., (Copyright © 2020 Agongo, Amenga-Etego, Nonterah, Debpuur, Choudhury, Bentley, Oduro, Rotimi, Crowther and Ramsay.)

Genome wide association studies (GWAS) for type 2 diabetes (T2D) undertaken in European and Asian ancestry populations have yielded dozens of robustly associated loci. However, the genomics of T2D remains largely understudied in sub-Saharan Africa (SSA), where rates of T2D are increasing dramatically and where the environmental background is quite different than in these previous studies. Here, we evaluate 106 reported T2D GWAS loci in continental Africans. We tested each of these SNPs, and SNPs in linkage disequilibrium (LD) with these index SNPs, for an association with T2D in order to assess transferability and to fine map the loci leveraging the generally reduced LD of African genomes. The study included 1775 unrelated Africans (1035 T2D cases, 740 controls; mean age 54 years; 59% female) enrolled in Nigeria, Ghana, and Kenya as part of the Africa America Diabetes Mellitus (AADM) study. All samples were genotyped on the Affymetrix Axiom PanAFR SNP array. Forty-one of the tested loci showed transferability to this African sample (p < 0.05, same direction of effect), 11 at the exact reported SNP and 30 others at SNPs in LD with the reported SNP (after adjustment for the number of tested SNPs). TCF7L2 SNP rs7903146 was the most significant locus in this study (p = 1.61 × 10(-8)). Most of the loci that showed transferability were successfully fine-mapped, i.e., localized to smaller haplotypes than in the original reports. The findings indicate that the genetic architecture of T2D in SSA is characterized by several risk loci shared with non-African ancestral populations and that data from African populations may facilitate fine mapping of risk loci. The study provides an important resource for meta-analysis of African ancestry populations and transferability of novel loci.

We surveyed 26 quantitative traits and disease outcomes to understand the proportion of phenotypic variance explained by local ancestry in admixed African Americans. After inferring local ancestry as the number of African-ancestry chromosomes at hundreds of thousands of genotyped loci across all autosomes, we used a linear mixed effects model to estimate the variance explained by local ancestry in two large independent samples of unrelated African Americans. We found that local ancestry at major and polygenic effect genes can explain up to 20 and 8% of phenotypic variance, respectively. These findings provide evidence that most but not all additive genetic variance is explained by genetic markers undifferentiated by ancestry. These results also inform the proportion of health disparities due to genetic risk factors and the magnitude of error in association studies not controlling for local ancestry.