The need for food products of animal origin is increasing worldwide. Satisfying these needs in a way that has minimal impact on the environment requires cutting-edge technologies and techniques to enhance the genetic quality of cattle. Heat stress (HS), in particular, is affecting dairy cattle with increasing frequency and severity. As future climatic challenges become more evident, identifying dairy cows that are more tolerant to HS will be important for breeding dairy herds that are better adapted to future environmental conditions and for supporting the sustainability of dairy farming. While research into the genetics of HS in the context of the effect of global warming on dairy cattle is gaining momentum, the specific genomic regions involved in heat tolerance are still not well documented. Advances in omics information, QTL mapping, transcriptome profiling and genome-wide association studies (GWAS) have identified genomic regions and variants associated with tolerance to HS. Such studies could provide deeper insights into the genetic basis for response to HS and make an important contribution to future breeding for heat tolerance, which will help to offset the adverse effects of HS in dairy cattle. Overall, there is a great interest in identifying candidate genes and the proportion of genetic variation associated with heat tolerance in dairy cattle, and this area of research is currently very active worldwide. This review provides comprehensive information pertaining to some of the notable recent studies on the genetic architecture of HS in dairy cattle, with particular emphasis on the identified candidate genes associated with heat tolerance in dairy cattle. Since effective breeding programs require optimal knowledge of the impaired immunity and associated health complications caused by HS, the underlying mechanisms by which HS modulates the immune response and renders animals susceptible to various health disorders are explained. In addition, future breeding strategies to relieve HS in dairy cattle and improve their welfare while maintaining milk production are discussed. The cattle population contributes to and is affected by the projected increase in global warming. As global demand for food products of animal origin is expected to rise by 70% by 2050, this demand must be met in a manner that has a negligible impact on the environment by improving the genetic quality of livestock using cutting-edge technologies and techniques . TemperaThe impact of HS in the dairy industry can be mitigated through a combination of different approaches including physical adjustments of the environment, enhanced nutrition, and management practices , 11. HowRecent developments in high-throughput sequencing techniques and emerging genomic data have opened up the opportunity to decipher the genomic variants affecting economically important traits in cattle. For a handful of economic traits, QTL mapping, transcriptome profiling and GWAS were conducted under HS conditions to gain better insight into the genetic basis for HS response in cattle. Several recent GWAS and transcriptome studies have been conducted to uncover candidate genes and causal variants associated with various indicators of heat tolerance traits in a wide range of dairy cattle breeds , 17–24. 2.The effects of HS on dairy cows are profound, and contribute significantly to lower overall milk production, fertility, and impaired health and welfare . In partβ-casein and butyrophilin  in milk and increasing THI in dairy cattle . In the rophilin . Additiorophilin . Heat strophilin . In addiGenetic factors in cattle have been recognized as a main feature that can explain part of the differences in response to HS in dairy cows. For example, Otto et al.  performeMost economically important traits in farm animals are associated with polygenes located at QTLs that are widely distributed throughout the genome. With the availability of high-throughput sequencing technologies, detection of a number of high-impact QTL in dairy is possible, suggesting genomic regions and genes responsible for significant differences in the yield and composition under HS conditions. In addition, a wide range of genomic approaches have been employed to identify genetic variants linked to physiological indicators of response to HS. However, the heat tolerance trait is highly polygenic and influenced by multiple variants, each with small effects on the phenotype, suggesting that the trait may be more suitable for genomic selection tools such as those currently used in the Australian dairy industry , 50, tha3.IL-8), including the mammary gland, where they phagocytose and destroy pathogens , interferon-γ (IFN-γ) and interleukin-12 (IL-12). Polarized T helper cells (Th2), on the other hand, secrete type 2 cytokines  that play a regulatory role during the immune response (IL-10 and TGF- TGF-β) favorite allergic reactions (IL-4 and IL-5), and stimulate B cells for the production of special antibody isotypes (IL-4 and IL-13) to fight extracellular bacteria and helminths (HSPs) and alters the cytokine profile, which in turn alters the Th1:Th2 balance. Several studies have shown that thermal stress suppresses the secretion of Th1 cytokines (cellular immunity) and stimulates the secretion of Th2 cytokines , which impairs cell-mediated immunity, decreases lymphocyte proliferation, and increases the susceptibility of heat-stressed animals to various infectious diseases   secrete anti-inflammatory cytokines such as  (FOXP3) . Althoug (FOXP3) , 74. The (FOXP3)  have rep (FOXP3) . This hi (FOXP3)  but alsofunction .4.It is evident that substantial genetic variation that explains part of the variation in response to heat tolerance exists among animals , 77. OneIn an attempt to evaluate the genetic effect of HS and genetic tolerance for milk production and its components in dairy cattle, genetic parameters were estimated for several indicators of HS phenotype and milk production response as well as resilience indicators traits in dairy cattle , 82–86. 5.The ability of an animal to respond to HS can be assessed by physiological indicators of HS traits, such as RT, RR, and DS, which increase when the animal is exposed to a warm environment , 87. AnoDGAT1, PPP1R16A, FOXH1, and PLEC had the greatest effects on fatty acid across lactations under different conditions of HS in dairy cows  associated with physiological indicators of HS  in Holstein cattle by weighted single-step genome-wide analysis studies (WssGWAS). In the transcriptome study by Czech et al.  associated with RT and RR during HS in lactating Holsteins. Previously, Dikmen et al. , reduction of oxidative stress, modulation of apoptosis process , DNA maintenance (TONSL), and thermotolerance (CRY2). The UCN3 gene that is engaged in the genetic regulation of stress tolerance and oxidative stress in Holstein Friesian was described by  and Minnan cattle (Bos-indicus) resulting from DVL2 mutations could alter the normal transcription and expression of the DVL2 gene and also affect hair growth, allowing it to acclimatize hot-climate of southern China and cold climate of northern China , heat shock proteins , TLR2/4 and IL2/6 (HSF1) binds the promoter regions of HSPs to control their stress-inducible synthesis in response to the environment (HSPs) are highly conserved stress proteins that have been proposed as promising biomarkers of HS, of which HSP70 has been shown to be upregulated in dairy cows’ mammary epithelial cells during HS to increase mammary gland thermotolerance (HSP (HSP70 and HSP90) and cortisol levels compared with non-heat-tolerant dairy cows. Potential candidate genes such as HSPA1B, HSPA1L, HSPA12A, GRXCR1, FKBP4, and IL-6 were reported to have a correlation with thermotolerance in tropically adapted dairy cattle breeds , have been linked to HS  and metabolism (citrate or Krebs cycle) are important factors in elucidating the mechanisms of heat tolerance in dairy cattle, warranting extensive follow-up of functional investigations . Cheruiyo et al.  reported mammals .p < 0.05 and Benjamini-Hochberg false discovery rate (FDR) < 0.05 were used. In terms of biological processes, genes related to HS response as well as biological regulation, metabolic processes, or immune responses were found to be the most represented (p < 0.001), comprising of 10 candidate genes  involved in HS response in dairy cattle . The renin secretion pathway was enriched with 4 candidate genes . It was also observed that the enrichment of genes in the neuroactive ligand-receptor interaction pathway , PI3K-Akt signaling pathway , HIF-1 signaling pathway  and JAK–STAT signaling pathway  with other pathways and stress responses  underlying lipid metabolism in cattle in warm environments. Several other candidate variants and genes that may be responsible for differences in heat tolerance in particular environments and production systems have been discovered in potentially selected regions, including ITGA9, ACAT2, and PLAC8 in dairy cattle  for heat tolerance with high milk yield is a current state of genetic research that could lead to a long-term solution to the problem . Also, t7.1.Bos indicus breed can regulate its body temperature better than the Bos-taurus breed. Thus, Bos-indicus breeds are regularly crossed with Bos-taurus breeds to combine their ability to resist heat and parasites with the production qualities of temperate breeds  to moderate heritability (0.34) estimates , 50, 89,Dairy cattle breeding programs for heat tolerance could be accelerated by GS, which uses genome-wide DNA markers that predict tolerance to HS and is more efficient and appropriate than traditional genetic evaluation using pedigree. These DNA markers are often SNPs used to predict genomic breeding values. In addition, GS can be a cost-effective method to apply an efficient breeding approach, even in low and middle-income countries . Neverth7.3.p. Leu18del in the pre-melanosomal protein 17 (PMEL) gene, known from Galloway and Highland cattle, in Holstein-Friesian cattle, confirmed the causative status of a coat color phenotype mutation to a changed environmental condition in cattle (p. Leu18del) of PMEL that segregates in Highland, Galloway, and Tuli cattle has been proposed as a causative mutation for coat color effects (HSPA1) gene, which provides cellular protection from high temperatures under HS conditions  during embryonic development in response to HS (Histone modification is another important epigenetic mechanism from activation to epigenetic regulation of transcription of HS-related genes (e.g., se to HS . This wose to HS , 147, pose to HS .GNAS and PEG3 influencing growth and carcass traits in cattle (OAR18 influencing body weight, muscle and fat depth measurements in Texel sheep (DLK1 and DIO3 expressed at postnatal stages are associated with non-fresh thermogenesis in brown adipose tissue, which is essential to prevent hypothermia (Gnas, Gnasxl, Ndn, and Dio3 are involved in brown adipose tissue metabolism (Genomic imprinting is another form of epigenetic regulation, in which alterations in gene expression do not require changes in the underlying DNA sequences, but rather the expression of a gene depends on its parental origin . This can cattle  and the el sheep . Imprintothermia , and othtabolism . TherefoIn a nutshell, we illustrate the importance of epigenetic variations and more particularly DNAm as a useful biomarker for environmental stress, as DNAm is sensitive to the environment and is involved in organisms’ plastic and adaptive response to the changing environment . The cur8.HSF1, SPAG17, DNAJC8, HSPA9 and DNAJC18 appears to have been reported in more than one independent studies. Further studies of these genes could help to understand the genetic mechanisms of adaptation to HS, which may help to simultaneously improve heat tolerance and production traits in dairy cattle. In addition, optimal breeding strategies for the genetic development of heat-tolerant dairy cows provide long-term solutions to HS effects, that are essential for addressing the dual challenge of increasing dairy production to feed the increasing human population, while addressing the impacts of global warming. Overall, this review could serve as a valuable resource material for the dairy breeding industry aimed at increasing heat tolerance, while maintaining milk production traits.In this review, we provide a comprehensive overview of the genomic regions and candidate genes associated with heat tolerance that can expand our knowledge to the genetic mechanisms of HS, which could open new avenues for mitigating the impacts of global warming on dairy cows. Many dairy farmers use a variety of strategies to keep their cows cool, such as shade, ventilation, cooling with water, drinking water, cooling in the barn, fans, sprinklers, etc. However, these strategies are quite costly to be of practical use in extensive dairy production systems under smallholder conditions, especially in hot, less developed and agriculture-dependent countries. As the effect of global warming intensifies, even locally adapted dairy breeds could be affected, and future dairy farmers will suffer the greatest economic losses. This will cause farmers to switch from dairy production to other sectors, such as beef production, as dairy cows are more affected by HS than beef cattle. Therefore, breeding dairy cows with greater heat tolerance ability, perhaps through genetic means, and the associated support in these vulnerable regions for food security must be addressed. While identifying heat-tolerant dairy cattle is challenging due to the complex phenomenon of HS and the antagonism between heat tolerance and production traits, the use of genome-wide information could be a way to unravel the genetic mechanisms of heat tolerance in dairy cows, that can be accustomed to the selection programs. Progress has been made in using genomic information to identify candidate genes and causative variants associated with heat tolerance and milk production traits in dairy cattle, that can be used for marker-assisted selection, genomic selection and gene editing programs. The literature shows that heat tolerance is a complex trait influenced by many genes in the genome, with the specific genes DW conceived, conducted the literature search, performed visualization, and wrote the first draft. JH, GM, and BS provided critical appraisal and suggestions. MA involved in conceptualization, writing – review and editing. All authors contributed to the evaluation, editing, and approval of the final version of the manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


These control the traits evaluated at the seedling stage. The combinations L61 x P2 and L71 x P3 were recommended, in view of the more successful performance estimated for traits related to the shoot and root system.Low rainfall rates are becoming increasingly frequent because of climate change, causing droughts and threatening world food security. For popcorn, drought is the most limiting abiotic factor for plant’s growth and development. Thus, the water deficit directly impacts for crop productivity. Based on knowledge of the genetic basis of traits involved in stages of popcorn germination and seedling development under water stress, genotypes with potential for adaptation to adverse growing conditions can be selected early. Therefore, data on genetic effects and combining ability of 10 popcorn parents were compiled to propose breeding strategies for the development of cultivars with greater adaptation to water stress in the early stages. Forty-five diallel hybrids were evaluated under two different water regimes, that is, water stress and full irrigation. This corresponded to a water retention capacity of 25% and 70% of the germination paper. The plants were watered daily as needed for seven days. A range of factors were evaluated, that is, germination traits including the germination speed index and germination on the seventh day; shoot traits including length and dry weight; and root system including length, dry weight, root-to-shoot ratio, maximum root number, root network area, specific and root network length, and root volume. Breeding for drought adaption in the early stages of popcorn development can be successful when hybrids are used, because of the genetic effects of dominance (ϕ It decrFor maize and other cereal species, the impacts of water stress on the development, growth, and final grain yield are not only determined by the variety, but also by the intensity of water stress . In the In Brazil, maize is predominantly cultivated in the second growing season from February to July, after the soybean harvest has occurred . This peTo date, the effects of water stress on trait expression in the early stages of popcorn development have not yet been studied. Therefore, understanding the genetic basis of trait performance in the seed germination and seedling development phases is important for planning breeding programs targeting the early selection of genotypes adapted to water stress . DiallelThe present study addressed the combined ability of 10 popcorn parents which have been pre-selected for agronomic efficiency in water use, and their hybrids, produced according to a diallel mating scheme, under two contrasting water regimes, that is, water stress (WS) and well-watered (WW) conditions. This can then highlight potentially useful traits for early selection for adaptation to water stress, and identify genotypes for strategic breeding for superior segregants with drought tolerance.22.11 hybrids developed using a diallel mating scheme between 10 S7 popcorn lines. The lines were selected from a set of 20 lines that had previously been evaluated in the field under WS. Based on the performance of the 10 lines, four were classified as agronomically efficient in terms of their water use , with four classified as inefficient  and two classified as intermediate (L71 and L76)  .The genealogy of popcorn inbred lines (S7) was derived from germplasm adapted to tropical  and temperate/tropical conditions  .To generate the hybrids, the lines were crossed in pairs, in 6.00 m rows spaced 1.00 m apart, with the plants spaced 0.40 m apart. The ears in each line were covered with plastic bags prior to stigma exertion. The mature tassels were covered with a brown paper bag on the day preceding the crosses to avoid contamination of the crosses with undesired pollen. Approximately 65 d after planting, the plants were hand pollinated. The hybrid seeds were harvested in April 2019, at the Experimental Station of Colégio Estadual Agrícola Antônio Sarlo, in Campos dos Goytacazes, Rio de Janeiro State, Brazil .2.2To evaluate the early hybrid development under each water regime, two experiments were conducted in a complete randomized block design with four replications of 20 seeds per replication. The experiments were conducted at the Crop Science Laboratory of the State University of Northern Rio de Janeiro Darcy Ribeiro (LFIT/UENF). The seeds from the 45 hybrids were germinated under two water regimes (WC). These were: i) adequate substrate moisture (well-watered: WW), where the substrate (germitest paper) was wettened to a moisture content of 70% of the water retention capacity; and ii) water stress (WS), where the substrate was wettened to a moisture content of 25% of the water retention capacity. To adjust the moisture content of each WC, the dry paper was weighed and multiplied by three, which was equivalent to the weight of the water-saturated paper. Thereafter, the moisture was adjusted to 70% and 25% of the substrate retention capacity  and an 8:16 h light–dark photoperiod . Based o2.3−1) was computed using the equation proposed by The traits evaluated were divided into three groups related to germination and the shoot and root systems. In each group of germination traits, germination on the seventh day (GER) and the germination speed index (GSI) were evaluated. Germination on the seventh day was evaluated as the percentage (%) of normal seedlings that emerged on the seventh day. On that day, the number of abnormal seedlings and non-germinated seeds were also counted. Seeds with no sign of germination were considered dead. Those with malformed structures and aspects of abnormalities were considered anomalous. Seedlings with strong structure formation, without any deformities and abnormalities, were recorded as normal . The GSI−1) was measured with a millimeter ruler, considering the distance from the seedling mesocotyl to the tip of the primary leaves (−1). For these evaluations, 10 seedlings that had been oven-dried at 70°C for 48 h were used.The shoot traits of the seedlings evaluated were the shoot length (SL) and the shoot dry weight (SDW). The shoot length (SL) (in cm seedlingy leaves . Shoot d−1) (−1 and used to estimate the maximum root number (MRN), root network area , specific root length , root network length , and root network volume .Root-related traits were evaluated on the same 10 seedlings, namely the root system length (RSL), root dry weight (RDW), and root-to-shoot ratio (RSR). The length of the root system was measured from the collar to the tip of the largest primary root (in cm plant−1) . The roo−1) . The sof−1) after being oven dried at 70°C for 48 h. Based on these values, divided by SDW, the RSR estimates were calculated using the equation: After being photographed, the RDW was estimated by weighing the material  of each variable, based on the comparison between WCs, was calculated using the formula ij = mean value of the hybrid combination (i ≠ j) or the parent (i = j); μ = overall mean; gi = effects of general combining ability of the i-th parent ; gj = effects of general combining ability of the j-th parent ; sij = effect of specific combining ability for crosses between the i-th and j-th parent; and For each WC, diallel analysis was performed separately, according to Griffing’s Method IV, Model 1 , accordig = (GMS – RMS)/2p and ϕs = SMS – RMS, respectively. Here, GMS is the mean square of the general combining ability, SMS is the mean square of the specific combining ability, RMS is the residual mean square, and p is the number of parents. The effects of quadratic components were expressed as a percentage of the total sum of effects.The quadratic components (ϕ) that express the genetic variability, in terms of general (g) and specific combining ability (s), were estimated using: ϕVariance and diallel analysis were performed using the GENES program . The gra33.1e) ranged from 6.73% (GER under WW) to 38.65% (GER under WS). In the combined analysis, the germination traits differed statistically only for the genotype source of variation (G)  Table 1.Of the shoot traits, only SDW under WW differed significantly Table 2.The root-related traits with significant differences were RDW, SRL, and RNV under both WCs and RSR, MRN, RNA, and RNL under WW conditions only. RSL and RSR differed significantly for the three sources of variation  and the traits RDW, MRN, RNA, and SRL only for the source of variation G. Meanwhile, RNL and RNV showed significance for sources of variation G and WC. Comparing WS to the WW condition, there was a decrease in CRS of 15.09%, in RNA of 16.99%, in RNL of 17.60%, and in RNV of 19.16% Figure 13.2s) were the most important in explaining the observed variability in GSI under WS and in GER under WW conditions (Germination traits with significant differences for GCA estimates were GSI under WS and GER under WW conditions Table 2.s) explained most of the variability observed in SL under WS  explained most of the observed variability in RDW, MRN, and SRL under both WCs , L65 (0.42 under WS and 0.98 under WW), P3 (3.45 under WS and 2.41 under WW), and P7 (2.86 under WS and 0.55 under WW). For GER, the parents L63 (1.60 under WS and 0.81 under WW), L65 (5.07 under WS and 6.22 under WW), P2 (4.44 under WS and 2.68 under WW), P3 (5.90 under WS and 7.68 under WW), and P7 (6.94 under WS and 7.06 under WW) had positive GCA effects Figure 3For shoot-related traits, the parents with positive GCA estimates under both WCs were L61 (0.66 under WS and 1.29 under WW) and L63 (0.23 under WS and 0.97 under WW) for SL. For SDW, estimates were positive for L65 (0.01 under WS and 0.04 under WW), P2 (0.0003 under WS and 0.0183 under WW), and P3 (0.0073 under WS and 0.0233 under WW) Figure 3Regarding the root-related traits, the parents with positive GCA effects under both WCs were P2, P3, and P6 for CRS; L76, P2, P3, and P7 for RDW; and L76, P2, P3, and P7 for RSR. For MRN, positive estimates were predicted for P2, P3, and P7. For RNA, the same situation was detected for P2, P3, and P7. In SRL, positive GCA values were found for L61, L71, L75, and P6. For RNL, the parents L76, P2, P3, and P7 had positive GCA estimates and, equally, P2, P3, and P7 in relation to RNV Figure 33.4For germination traits, under both WCs, the hybrids with simultaneously highest SCA estimates and means , were L75 × P6 and L63 × P3 for GSI and L61 × P6, L61 × P7, and L75 × P6 for GER 5.For shoot traits and considering both WCs together, the hybrids with more marked SCA estimates and means among the 15 highest values were L65 × L71 and L71 × P3 for SL and L61 × P2, L71 × P3, and L71 × P7 for SDW 5.When evaluating the root traits together, concomitantly under both WCs, the hybrids with the highest SCA and mean values among the 15 highest values were L63 × P3 and L65 × P6 for CRS; L61 × P2, L71 × P7, and L76 × P3 for RDW; L61 × P7, L63 × P2, and L75 × P3 for RSR; L71 × P3, L71 × P7, L75 × P7, P2 × P6, and P6 × P7 for MRN; L61 × P2, L71 × P3, L76 × P3, and P2 × P6 for RNA; L61 × L75, L63 × L65, L63 × L75, L65 × L76, and L71 × P6 for SRL; L61 × P2, L63 × P7, L71 × P3, and P2 × P6 for RNL; and L61 × P2, L71 × P3, L71 × P7, and P2 × P6 for RNV 5.3.5The main principal components had values exceeding 70%, regardless of the WC Figure 6Under WS, five groups were formed  and specific (s) combining ability, the effects of s explained most of the genetic variability. In these cases, the use of hybrids is recommended, as the beneficial effects of allelic complementation are exploited.Under water stress, the mode of action of agronomically important and other secondary traits is fundamental for the elaboration of more efficient breeding strategies . KnowledRegarding the estimates of the mean square effects, there was no change between the WCs in the most important quadratic component for the traits evaluated in this study. This shows that the mode of trait expression is the same under both WCs. According to 4.3The parents studied in this research had been selected for higher grain yield under drought during physiological maturation. The parents P2 and P3, previously classified as tolerant, stood out with the most successful performances for traits of germination and seedling development, based on the GCA means. Until then, it was unknown how these studied lines would perform in the early growth phase. The observed performance for grain yield, evaluated at the end of the cycle, was consistent with that observed during seed germination and seedling development. In general, the best lines preformed similarly under both tested water regimes Figure 3Based on the general combining ability under both water regimes, for the germination traits, lines L63, L65, P3, and P7 can be recommended as parents of superior hybrids to mitigate the harmful effects of WS in popcorn. For shoot traits, there were no lines that stood out under both WCs together. For the root-related traits, considered together, lines P2, P3, and P7 are recommended as parents of superior hybrids, with a view to reducing the harmful effects of drought on early popcorn development. Overall, for all groups of traits, lines P2 and P3 are recommended as parents of superior hybrids for water stress tolerance.From a broader perspective, the hybrids performed similarly under both the water regimes tested 5. From 1 lines in terms of SCA had high GCA estimates.The hybrids L61 × P2 and L71 × P3 were derived from lines P2 and P3, classified as tolerant, while line L61 was classified as susceptible and L71 as intermediate. This shows that the intolerant and intermediate parents, although undesirable per se for stronger performance in a water-stressed environment, represent hybrids that perform well for adaptation to dry environments, given the favorable allelic complementation observed. According to 4.4In the early stage of popcorn development, the root–shoot ratio, as well as specific root length, shoot length, and shoot dry weight of the seedlings were important traits to differentiate the WS tolerant genotypes. Hybrid P3 × P7 (44) was noted for the high mean RSR; hybrid L71 × P7 (30) with a high mean SRL; hybrid L61 × L75 (4) with high mean SL; and the hybrids L71 × P3 (28) and L76 × P6 (38) with high SDW means. The other traits—GER, GSI, SDW, RSL, RDW, MRN, RNA, RNL, and RNV—seem to be closely related and were not adequate for genotypic discrimination. Meanwhile, under the WW condition, the traits RSL, RSR, SRL, SL, GER, and GSI were important to discriminate the studied genotypes. The hybrids L76 × P3 (37) and P2 × P3 (40) were with high RSL and RSR means; hybrid L71 × P6 (29) was with a high SRL mean; and hybrid L71 × P3 (28) was with high means of SDW, REE and GSI. The traits RSL, RSR, SRL, SL, GER and GSI seem to be closely related and they satisfactorily differentiated genotypes for WS adaptation. Therefore, they can be recommended for future studies to be tested from the perspective of being effectively used in early selection, as applied to popcorn in this study.5Breeding for drought adaption in the early stages of popcorn development can be successful when hybrids are used, because of the genetic effects of dominance (s), which control the traits evaluated in the seedling stage. The combinations L61 × P2 and L71 × P3 were recommended, in view of the more successful performance estimated for traits related to the shoot and root system.For future investigations for the differentiation of popcorn genotypes in the germination and seedling emergence stages, regardless of the water regime applied, the evaluation of the traits specific to root length, root–shoot ratio, and shoot dry weight is strongly suggested.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Conceptualization, SHK. Methodology, CC, SK, JdS, LF, JL, DdS, FV, RS, HV, and SHK. Investigation, CC, SK, JdS, LF, JL, DdS, FV, RS, HV, and SHK. Writing - original draft preparation, CC, and SK; funding acquisition, SK and AdA. All authors contributed to the article and approved the submitted version.


Type 2 diabetes (T2D) and coronary artery disease (CAD) both have known genetic determinants, but the mechanisms through which their associated genetic variants lead to disease onset remain poorly understood.We used large-scale metabolomics data in a two-sample reverse Mendelian randomization (MR) framework to estimate effects of genetic liability to T2D and CAD on 249 circulating metabolites in the UK Biobank . We examined the potential for medication use to distort effect estimates by conducting age-stratified metabolite analyses.Using inverse variance weighted (IVW) models, higher genetic liability to T2D was estimated to decrease high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) , whilst increasing all triglyceride groups and branched chain amino acids (BCAAs). IVW estimates for CAD liability suggested an effect on reducing HDL-C as well as raising very-low density lipoprotein cholesterol (VLDL-C) and LDL-C. In pleiotropy-robust models, T2D liability was still estimated to increase BCAAs, but several estimates for higher CAD liability reversed and supported decreased LDL-C and apolipoprotein-B. Estimated effects of CAD liability differed substantially by age for non-HDL-C traits, with higher CAD liability lowering LDL-C only at older ages when statin use was common.Overall, our results support largely distinct metabolic features of genetic liability to T2D and CAD, illustrating both challenges and opportunities for preventing these commonly co-occurring diseases.10.13039/100010269Wellcome Trust [218495/Z/19/Z], UK MRC , the 10.13039/501100000883University of Bristol, Diabetes UK [17/0005587], 10.13039/501100000321World Cancer Research Fund [IIG_2019_2009]. We searched Medline for epidemiological and Mendelian randomization (MR) studies containing the terms ‘type 2 diabetes’ or ‘coronary artery disease’, and ‘metabolomic’ or ‘metabolites’, among any human population, without date restrictions. Previous cross-sectional and MR studies have identified metabolites, in particular lipids, that may play roles in the pathogenesis of type 2 diabetes (T2D) and coronary artery disease (CAD), yet direct comparisons of the influence of genetic liability to T2D and CAD had not been previously conducted.We present results supporting largely distinct metabolic profiles of genetic liability to T2D and to CAD using pleiotropy-robust MR models and the largest metabolite GWAS data to date.This added knowledge of the differing molecular phenotypes of T2D and CAD has implications for the prediction and prevention of these commonly co-occurring diseases. Future research utilising untargeted metabolomics platforms across other populations will help to further characterise T2D and CAD liabilities at a molecular level. Metabolites associated with genetic liability to disease are candidates for functional follow-up studies and prediction modelling.,,Globally, overall incidence rates of type 2 diabetes (T2D) and coronary artery disease (CAD) are increasing in parallel, together affecting over 500 million adults.Recent advances in metabolomics technologies have enabled detailed insight into the metabolic features of cardiometabolic diseases and their potential underlying mechanisms.,Mendelian randomization (MR), a genetic epidemiological approach that uses genetic variants associated with modifiable exposures to estimate the unbiased effects of such exposures on outcomes,Here, we compare the metabolic features of genetic liability to T2D and to CAD using a reverse-MR framework, with the aim of revealing the metabolic traits which characterise the development of each disease. We used the largest, most recent summary-level GWAS data on circulating metabolites measured using targeted metabolomics  from the population-based UK Biobank cohort study, representing a 5-fold larger sample size than previous studies. To examine any distortions of effect estimates by medication use, we examined the effects of T2D and CAD liability on the use of metformin and statins, and repeated analyses using data from age-stratified GWAS of metabolites, given that medication use differs markedly by age. We also estimated the effects of disease liability on lifestyle-related risk factors including adiposity, smoking, and alcohol consumption to see if our reverse-MR framework would detect these expectedly more distal associations.−8 from a large-scale GWAS meta-analysis combining data from 32 GWAS, including 74,124 T2D cases and 824,006 controls of European ancestry.2 > 0.001, the SNP with the lowest P-value in each set of variants in LD was retained. This left 167 independent SNPs associated with T2D (We identified single nucleotide polymorphisms (SNPs) that were independently associated with T2D at P < 5 × 10with T2D  for incl−8. For variants that were in LD based on R2 > 0.001, the SNP with the lowest P-value in each set of variants in LD was retained, leaving 145 SNPs  spectroscopy .Summary statistics from GWAS of circulating metabolites conducted among European participants of the UK Biobank study were used. Details of the UK Biobank design, participants, genomic quality control (QC) and its strengths and limitations have been reported previously.,Statins and metformin are commonly prescribed medications for the prevention/treatment of CAD and T2D, respectively, particularly among adults identified as having higher risk.For the purposes of further examining distortions by medication use, we used summary statistics from metabolite GWASs that were performed on the same UK Biobank sample but divided into age tertiles , and which used the same standardisation and covariate adjustments. Each tertile created a set of summary statistics to be analysed. Based on previous work in UK Biobank,The samples used to generate genetic instruments for T2D and CAD included participants from UK Biobank, meaning that they overlap with the outcome sample , which may bias estimates. However, strong genetic instruments for both exposures (F-stat >10) mean that bias from sample overlap is likely to be small.Data used for additional outcomes  is described in UK Biobank participants provided written informed consent. Ethical approval was obtained from the North West Multi-centre Research Ethics Committee , as a Research Tissue Bank approval.e2) and represent the normalised SD unit difference in each outcome trait  per doubling of genetic liability to T2D or CAD, based on previous recommendations.To generate MR estimates of the effect of genetic liability to T2D and CAD on metabolites (with ages combined and within age tertiles), and on medication use and additional outcomes noted above, we integrated estimates of the association of SNPs with exposures (T2D and CAD separately), with estimates of the association of those same SNPs with each metabolic trait measured in UK Biobank. Summary statistics were harmonised using the ‘harmonise_data’ function within the TwoSampleMR package.To estimate the direct effects of liability to either disease and avoid any biases present due to shared effect of genetic variants on liability to both diseases, we performed multivariable MR with liability to T2D and CAD as exposures, and metabolites, adiposity, medication, and lifestyle factors as outcomes. This was performed using the MVMR R package,Sensitivity analyses were performed to examine the influence of outlying SNPs on effect estimates. Radial-MR methods were utilised to re-estimate the model excluding all outlying SNPs according to Cochrane's Q statistic, for both T2D liability and CAD liability. This analysis was conducted using the RadialMR R package.TCF7L2 gene is known to increase risk of T2D, but is paradoxically associated with reduced BMI,TCF7L2 SNP (rs7903146) excluded for metabolite, adiposity, medication, and lifestyle outcomes.The ,To correct for multiple testing in a way that accounts for the correlated nature of our metabolic outcome traits, the P-value threshold  for guiding statistical interpretation can be divided by 33 . EstimaThe estimated effects of T2D and CAD liability on several lipids, particularly non-HDL cholesterol, differed markedly by age . Among tEffects on fatty acid traits were largely opposite for T2D liability and CAD liability . For exaThe effects of T2D liability on fatty acids and amino acids as estimated by IVW models were fairly consistent with age, exceptions including total fatty acids, ratio of saturated to total fatty acids, and histidine . For hisAs expected, T2D liability consistently increased glucose  across sensitivity models ; yet theEstimates of the effects of T2D and CAD liability were largely consistent across age tertiles for glycolysis traits and ketone bodies, except for CAD liability on lactate and pyruvate which was positive in the youngest and null in the intermediate and oldest tertiles . The effHigher genetic liability to T2D was estimated to increase all adiposity outcomes according to IVW estimates . There wMultivariable MR results, representing the direct effects of liability to either disease, were similar to univariable MR results across all metabolites, adiposity, medication and lifestyle outcomes .Radial-MR sensitivity analyses, which were performed to identify and exclude SNPs with outlying effects, showed little difference in effect estimates after outlier exclusion for T2D liability, although the estimates with the outliers excluded were more precise . HoweverTCF7L2 gene excluded were similar to effect estimates generated using the full SNP set for T2D liability , the slope of the regression line was −0.49 with an R2 of 0.11. Together, this indicates a weak association between profiles, giving further evidence that genetic liability to T2D and to CAD have distinct metabolic features.Lastly, estimates of the effects of T2D and CAD liability on each metabolite (ages combined), adiposity, and medication use were visualized in an XY plot to compare the overall pattern of effects of liability to either disease across outcomes . To bett,In this study, we directly compared the metabolic profiles of genetic liability to T2D and CAD, two diseases which commonly co-occur but which involve different pathophysiology and clinical presentation. We applied a reverse-MR framework,,Statins and metformin are commonly used for the prevention/treatment of CAD and T2D, respectively. The overall prevalence of statin use in UK Biobank is 16%, with men almost twice as likely to be taking statins as women, and use increasing markedly across age tertiles.,,Increasing HDL-C has been hypothesized as a mechanism to reduce CAD risk, based largely on conventional non-genetic epidemiology studies.Our results suggest that T2D and CAD liability confer opposite effects on fatty acid metabolism, which were largely consistent across pleiotropy/outlier-robust sensitivity models. A study that used a nontargeted mass spectrometry-based approach found that palmitic acid and linoleic acid levels were elevated in severe coronary heart disease.,Consistent with previous studies, we found consistent evidence across ages and sensitivity models that T2D liability robustly increased total BCAAs,Although both T2D and CAD are associated with inflammation,It is well established that excess adiposity is a causal risk factor for T2D,,The samples used to generate genetic instruments for T2D and CAD included participants from UK Biobank, the same sample from which we obtained genetic association estimates for metabolic traits, medication use, and lifestyle-related outcomes . This may have led to bias in the results in the direction of the observational association, however, given the strong genetic instruments for T2D and CAD liability, bias from sample overlap is likely small.Our most robust findings based on pleiotropy-robust models and the largest metabolite GWAS data to date suggest that T2D liability increases BCAAs across mid to late adulthood, whilst CAD liability decreases LDL-C and apolipoprotein B in older adults only. Such paradoxically favourable effects of CAD liability in older adults likely reflect mediation by statin use in adulthood. Overall, our results support largely distinct metabolic profiles of genetic liability to T2D and to CAD, illustrating both challenges and opportunities for preventing these commonly co-occurring diseases.JAB, ELA and CJB conceived and planned the study and supervised analyses. MLS conducted the analyses and wrote the first draft. MLS and JAB verified the underlying data. JAB, ELA, CJB, ES, GDS and MVH critically reviewed the intellectual content of manuscript drafts and with MLS approved the final version for submission.https://gwas.mrcieu.ac.uk/.All summary level GWAS results are publicly available through the IEU-OpenGWAS platform, accessible at MVH has collaborated with Boehringer Ingelheim in research, and in adherence to the University of Oxford's Clinical Trial Service Unit & Epidemiological Studies Unit (CSTU) staff policy, did not accept personal honoraria or other payments from pharmaceutical companies. MVH became a full-time employee of 23andMe during the study and owns stock in 23andMe. GDS reports Scientific Advisory Board Membership for Relation Therapeutics and Insitro. No other conflicts of interest to declare.


Background: Autozygosity, the proportion of the genome that is homozygous by descent, has been associated with variation in multiple health-related traits impacting evolutionary fitness. Autozygosity (FROH) is typically measured from runs of homozygosity (ROHs) that arise when identical-by-descent (IBD) haplotypes are inherited from each parent. Population isolates with a small set of common founders have elevated autozygosity relative to outbred populations. Methods: In this study, we examined whether degree of autozygosity was associated with variation in 96 cardiometabolic traits among 7221 Old Order Amish individuals residing in Lancaster County, PA. We estimated the average length of an ROH segment to be 6350 KB, with each individual having on average 17.2 segments 1.5 KB or larger. Measurements of genome-wide and regional FROH were used as the primary predictors of trait variation in association analysis. Results: In genome-wide FROH analysis, we did not identify any associations that withstood Bonferroni-correction (p = 0.0005). However, on regional FROH analysis, we identified associations exceeding genome-wide thresholds for two traits: serum bilirubin levels, which were significantly associated with a region on chromosome 2 localized to a region surrounding UGT1A10 (p = 1 × 10− 43), and HbA1c levels, which were significantly associated with a region on chromosome 8 localized near CHRNB3 (p = 8 × 10− 10). Conclusions: These analyses highlight the potential value of autozygosity mapping in founder populations.The online version contains supplementary material available at 10.1186/s12864-023-09208-5. Autozygosity, which is defined as the probability that a region is homozygous due to the inheritance of alleles identical-by-descent (IBD), is determined by the presence of extended homozygosity in that region. Since runs of homozygosity (ROHs) are a recognized signature of recessive inheritance, homozygosity mapping using ROH has commonly been used to map recessive disorders when there is suspicion that parents may share a common haplotype. .More recently, studies have evaluated the impact of overall autozygosity on variation in common complex traits, where overall autozygosity is estimated as the proportion of ROH across the entire genome (genome-wide autozygosity). Genetic homogeneity, estimated as overall autozygosity, has previously been linked to adverse health outcomes in multiple traits impacting evolutionary fitness. Populations with increased autozygosity are more likely to experience inbreeding depression, or reduced fitness, which has been linked to a range of phenotypic consequences including cardiovascular disease, , 3 shortElevated autozygosity is seen in population isolates that have a small set of common founders.  Due to lSubjects in this study were recruited through multiple protocols carried out between 2003 and 2019 as part of the Amish Research Program. Study participants were recruited through the University of Maryland Amish Research Clinic (ARC) in Lancaster, PA. Studies were designed to assess determinants of cardio-metabolic and bone health in the community, and enrollment was open to volunteers throughout the Lancaster Amish community. , 14 RecrClinical examinations were performed by trained nursing staff at the Amish Research Clinic in Lancaster, PA or in the homes of study participants. For this report, we restricted analysis to a set of phenotypes measured in common across all  protocols. These include basic anthropometrics, blood pressure, fasting blood lipids, glucose and insulin, hemoglobin a1c (glycated hemoglobin), basic blood chemistries, and medical histories. A complete list of the 96 phenotypes analyzed is provided in Supplemental Table 1.Genotyping was performed at the Regeneron Genetics Center using the Illumina Global Screening Array, which included 490k single nucleotide polymorphisms (SNPs) that passed quality control parameters. Samples with > 5% missingness were removed from analysis as were variants with > 2% missingness or high levels of Mendelian errors. We also applied a minor allele frequency threshold of 0.01. For these analyses we used only the unimputed genotype data.Genome-wide autozygosity was estimated as the proportion of the genome that fell into ROHs. SNPs with more than 3% missingness across individuals and with a minor allele frequency less than 5% were excluded from ROH calculations. ROH estimates were made using the IBD tool implemented in Plink, which has several built-in arguments.  The key GARLIC software, which outputs ROH and length class information in UCSC’s plain-text BED format. [Regional autozygosity computation was performed using the  format.  AutozygoAPOB p.Arg3527Gln and APOC3 p.Arg19Ter) previously identified in this Amish community affecting lipid levels. [ROH was used as the primary predictor of the traits of interest. The genome wide significance threshold after Bonferroni correction was p = 0.0005 (0.05/96 traits). As a secondary analysis, we tested for sex-specific effects of autozygosity on trait variation using an autozygosity*sex interaction term. For regional analysis, an independent association analysis was performed for each of 300k SNPs using ROH status as the primary predictor of the 96 traits of interest. We adjusted the significance threshold to account for 170k independent SNPs among these 300k using an LD threshold of r2 = 0.50. The Bonferroni adjusted significance threshold was p = 3.1 × 10− 9.We tested for association of the autozygosity estimates  with trait variation using a linear mixed model with phenotype as the outcome and autozygosity as the independent variable. As fixed covariates for each trait analysis, we included age, sex, and genotypes for two large effect SNPs  had ROH on chromosome 11 in a region harboring FOLH1 and OR4A47 is consistent with previous findings. [There are several regions of the genome for which the frequency of individuals with SNPs in ROH segments was particularly high  , but the lead association  with genome-wide FROH was with electrocardiogram (EKG) QT interval, followed by serum levels of CO2 , urea nitrogen , and thyroid hormone measurement (tsh) . In a sex-stratified analysis, we did not identify any sex-specific effects of FROH.Measurements of F− 9)  with increased FROH at a region of chromosome two that includes the UGT1A10 gene. As we [UGT1A1, a gene encoding an enzyme that converts bilirubin into a more water-soluble form that the body is better able to excrete, we adjusted for the SNP as an additional fixed effect but found the ROH association to be essentially unchanged . Three additional traits were associated with increased FROH, HbA1c  with a region on chromosome eight surrounding the CHRNB3 gene, C-reactive protein  with the intergenic region of FBXO33 and thyroid hormone levels  with the intergenic region of LRRC3B . To our knowledge, no genes previously linked to these traits are close to these signals.We analyzed 96 traits for associations with regional autozygosity levels and identified four trait associations at genome-wide significance following Bonferroni correction (p = 3.1 × 10 9) Fig. a-d. Incre. As we  and othee. As we  have pre− 464) between Bilirubin levels and SNPs within the same region that was detected with ROH analysis. HbA1c levels were not strongly associated (p > 9.6 × 10− 4) with any SNPs in the region when assessing using a recessive model.To test whether the regions associated with bilirubin and HbA1c identified through ROH analysis would be detected with other methods, we set up a recessive mode association analysis. We detected a strong association , although an association between HbA1c levels and chromosome 8 has not previously been reported.Population isolates have an increased burden of ROH and can uncover genomic regions associated with complex phenotypes that may not have been identified in populations with more distant parental relatedness. Due to low levels of genome-wide homozygosity commonly seen in modern human populations, very large numbers of study subjects are required to provide sufficient statistical power.  AlthoughThe lead SNP from the recessive model may be too common (MAF = 41.7%) to exist on just one haplotype. Therefore, the signal may be diluted when using the ROH method. The causal SNP is found in a large fraction of the population but is not always present on the haplotype with the strongest effect. The SNPs in the region associated with HbA1c levels are rare and are only found in individuals that share a common haplotype.UGT1A10 gene and that increased HbA1c levels were associated with increased autozygosity on chromosome eight, localized to the CHRNB3 gene.Amish have larger ROH segments and more of the Amish genome included in autozygous regions compared to outbred populations. Genome-wide summed autozygosity was not significantly associated with any of the 96 traits tested in this study. Using regional autozygosity mapping methods, we identified two traits associated with regional levels of autozygosity. We found that increased serum bilirubin levels were associated with increased autozygosity on chromosome two, localized to the Below is the link to the electronic supplementary material.Additional file 1: Supplemental Figure 1. Zoom plot showing associations between FROH and a) C-reactive protein and b) thyroid hormone. Red line indicates the significance threshold of P = 3.1 x 10-9.Additional file 2: Supplemental Table 1. "Panel of complex traits measured in Amish participants that were tested for association with regional and global autozygosity." - this title is incorrect in the current proof. Additional file 3: Supplemental Table 2. "SNPs with the highest frequency of inclusion in an ROH (frequency = 0.09 – 0.18). The SNPs that are shown were found on chromosome 2, 5, 6, 11, and 20."Additional file 4: Supplemental Table 3. "SNPs with the lowest frequency of inclusion in an ROH (frequency = 0.00 – 0.015)."Additional file 5.


Most genome-wide association studies (GWAS) for lipid traits focus on the separate analysis of lipid traits. Moreover, there are limited GWASs evaluating the genetic variants associated with multiple lipid traits in African ancestry. To further identify and localize loci with pleiotropic effects on lipid traits, we conducted a genome-wide meta-analysis, multi-trait analysis of GWAS (MTAG), and multi-trait fine-mapping (flashfm) in 125,000 individuals of African ancestry. Our meta-analysis and MTAG identified four and 14 novel loci associated with lipid traits, respectively. flashfm yielded an 18% mean reduction in the 99% credible set size compared to single-trait fine-mapping with JAM. Moreover, we identified more genetic variants with a posterior probability of causality >0.9 with flashfm than with JAM. In conclusion, we identified additional novel loci associated with lipid traits, and flashfm reduced the 99% credible set size to identify causal genetic variants associated with multiple lipid traits in African ancestry. There have been few genome-wide association studies that analyze multiple lipid traits simultaneously, especially in individuals of African ancestry. To address this, the authors performed a multi-trait analysis of GWAS along with fine-mapping to find new genetic loci associated with lipid traits in individuals of African ancestry. Evidence from epidemiological studies indicates that cardiometabolic disease rates in Africa are comparable to those in other parts of the world, but are rapidly increasing due to unhealthy dietary intake patterns and lifestyle factors4. Moreover, previous studies have suggested that individuals from continental Africa are associated with less atherogenic lipid profiles5. However, individuals of African ancestry living in the US or Europe have higher rates of cardiometabolic diseases than other ancestry groups7, suggesting that ancestral differences exist in the aetiology of cardiometabolic diseases.Lipid traits including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG) and total cholesterol (TC) are implicated in cardiometabolic diseases8. Unhealthy dietary intake, physical inactivity, cigarette smoking, alcohol consumption and several genetic factors are some of the factors implicated in atherogenic lipid levels. Although several genetic loci are associated with lipid traits11, they explain only a small fraction of the variances of atherogenic lipid levels9. Moreover, these genetic loci were mainly discovered in European and East Asian ancestry cohorts14, and the transferability of these genetic loci to individuals of African ancestry has been poor15, probably due to differences in linkage disequilibrium (LD), allele frequencies and environmental exposure among other factors, suggesting that more genetic variants remain to be discovered. To address this, The Global Lipids Genetics Consortium (GLGC) performed a meta-analysis of genome-wide association studies (GWASs) with more than 1.6 million individuals from five ancestries. Of these individuals, 99,432 were of African ancestry, in whom an additional 15 novel loci associated with lipid traits were identified16.Lipid traits are influenced by environmental and genetic factors17 approach by taking advantage of the correlation among the lipid traits. Analyzing multiple lipid traits simultaneously can provide more accurate and robust results than analyzing each trait separately. Moreover, we used a single trait (JAM)18 and multi-trait (flashfm)19 fine-mapping methods to identify causal genetic variants associated with lipid traits in individuals of African ancestry; sharing information between traits by joint fine-mapping with flashfm results in higher resolution  than fine-mapping each trait independently.Although the GLGC meta-analysis included 99,432 individuals of African ancestry, 72,859 were African Americans in the US, which may not carry ancestry-specific genetic variants across Africa. Moreover, the majority of African Americans in the US carry West African ancestry. To further identify genetic loci associated with lipid traits in individuals of African ancestry and determine their molecular mechanisms, putative causal genetic variants, and cover greater genetic diversity across the continent of Africa, we performed a meta-analysis of GWAS including up to 125,000 individuals of African ancestry. Of these individuals ~14,000 were from the African Partnership for Chronic Disease Research (APCDR) consortium in Africa, ~99,000 were from the GLGC and ~11,000 were from Africa Wits-INDEPTH Partnership for Genomic Research (AWI-Gen) in Africa. To increase the statistical power of detecting additional novel genetic loci associated with lipid traits, we used the multi-trait analysis of GWAS (MTAG)p-value < 5 × 10−8)  and rs6704760  were associated with LDL and mapped to the intergenic regions between NT51B and RDH14, MYCN and MYCNOS, and associated with cardiovascular diseases and neoplasm, respectively, Fig. p-value = 4.52 x  10−08) and rs115505361  were associated with TG, mapped within CHRM3 and MGAT2, and associated with hypertension, body fat distribution and body mass index and blood protein levels, respectively, Fig. We conducted a GWAS meta-analysis of ~125,000 individuals of African ancestry and identified 63, 68, 48, and 92 independent genetic loci (500 kb around lead SNP) associated with HDL, LDL, TG and TC, respectively, at genome-wide significance  estimated from the 1000 Genome Project phase 3 of individuals of African ancestry20 (Methods). Our MTAG approach, identified 84, 62, and 110 independent genetic loci associated with HDL, TG, and TC, respectively, at genome-wide significance  , ZNF782 , intergenic region between MSANTD3 and TMEFF1 , and LOC100507346  with lipid traits 18, a single-trait fine-mapping method and flashfm19, a multi-trait fine-mapping method, using the AFR super population of 1000 Genomes and an in-sample subset of individuals of African ancestry as a reference panel (Methods)20.We then performed fine mapping to localize putative causal variants associated with lipid traits by taking advantage of the small linkage disequilibrium block structure among individuals of African ancestry. Fine-mapping was performed using JAMThe lipid trait correlations between LDL, TC, TG, and HDL indicate a high correlation between LDL and TC, and a low to moderate correlation between all other trait pairs Fig. . In compr2 = 0.992 with rs183130, the top SNP for flashfm (MPP = 0.999) for LDL in 16:56889590-57089590)  for TG in 16:56889590-57089590)  with the variant favoured by flashfm, rs116386571 MPP = 0.989)  and TC 16. HaploReg21 indicates that rs116386571 has enhancer histone marks for adipose nuclei and fetal heart, as well as bound proteins TCF12 and POL2B and 18 altered motifs, whereas rs77974343 has three altered motifs and enhancer histone marks in the spleen, suggesting slightly more biological support for rs116386571.In 15% of the flashfm CS99, there was at least one variant with a marginal posterior probability (MPP) of being a causal variant (MPP) > 0.90, whereas all JAM variants had MPP < 0.9  of the regions that have variants prioritised (MPP > 0.90) by both flashfm and the African fine-mapping of GLGC, there was a shared prioritised variant; 21% (10/48) of the regions that have variants prioritised by both flashfm and the multi-ancestry fine-mapping of GLGC share a prioritised variant. Details of variants prioritised by flashfm or JAM, as well as all functional annotations and whether they are also prioritised by the GLGC analyses are also provided  with the respective lipid traits  using colocPSCK9, APOE, LPL, CETP, and DOCK7 in individuals of African ancestry22. The greater coverage of African genetic variation allowed us to identify an additional four novel genetic loci in our GWAS meta-analysis. Using the MTAG approach, we identified 14 additional novel genetic loci associated with lipid traits that were not found in our GWAS meta-analysis approach. Moreover, our MTAG gene expression trait colocalization found six loci with a posterior probability of shared causality ≥80%. For fine mapping, we observed an improvement in terms of credible set size reduction when multiple lipid traits were jointly fine-mapped using flashfm.By analyzing multiple lipid traits simultaneously, we have provided more accurate and robust results than by analyzing each lipid trait separately. Moreover, we replicated several genetic loci previously reported to be associated with lipid traits including, MGAT2 and associated with body mass index, blood proteins, insulin sensitivity and hypertriglyceridemia. MGTA2 is involved in various metabolic and N-glycan biosynthesis pathways, including lipid metabolism and protein synthesis. rs12118522, mapped to CHRM3, is associated with hypertension and body fat distribution. Some pathways associated with CHRM3 include calcium signalling, cholinergic synapses, taste transduction, insulin secretion, salivary secretion, gastric acid secretion, and pancreatic secretion pathways. rs2451303 and rs6704760 are novel loci associated with lipid traits and are mapped in the intergenic regions between NT51B and RDH14, MYCN and MYCNOS, respectively. These genes have been previously reported to be associated with cardiovascular diseases and neoplasm, respectively.Our meta-analysis of AWI-Gen, APCDR, and GLGC-AFR, found four novel loci associated with lipid traits in individuals of African ancestry Table . The rs1As hypothesized, by using a multivariate approach, we improved the discovery of additional novel loci Table . The MTACACNA1E encodes the alpha-1E subunit of R-type calcium channels, which belong to the ‘high-voltage activated’ group that may be involved in the modulation of firing patterns of neurons important for information processing23. This gene is involved in the following pathways; MAPK signaling, calcium, T2D, and early infantile epileptic encephalopathy pathways and has been previously associated with blood pressure, BMI, and coronary artery calcification. TMEM64 is localized in the endoplasmic reticulum and modulates the nuclear localization of β-catenin, resulting in the activation of β-catenin-mediated transcription24. Diseases and phenotypes associated with TMEM64 include cholesterol, creatinine, chronotype, and serum metabolite concentrations in chronic kidney disease. PPP2R2C has previously been associated with BMI, coronary artery disease, T2D, and metabolite levels. This gene belongs to the phosphatase 2 regulatory subunit B family25, which is related to PI3K-Akt, AMPK signaling, adrenergic signaling in cardiomyocytes, Chagas disease, hepatitis C, and human papillomavirus infection pathways. These pathways suggest an important role of viral infections in lipid regulation in areas with a high viral load. This finding is consistent with the impact of viral infection on lipid metabolism. Indeed, gene and pathway analyses confirmed that inflammatory pathways and beta-cell type gene sets are associated with these traits. Future GWAS with imputed HLA variants may reveal additional insights into the relationship between lipid regulation and immunity, which has been observed in a previous study26.Notably, our novel genetic loci have been linked to traits correlated with blood lipid traits, including body mass index, systolic and diastolic blood pressure, diabetes, chronic kidney disease, blood protein levels, blood and immunology phenotypes, and Alzheimer’s biomarkers such as amyloid or infectious diseases. For instance, 30.Our MTAG gene expression trait colocalization analysis identified six loci with an H4 PP ≥ 80%. These genes are involved in several pathways, including insulin secretion, renin secretion, cAMP signaling, inositol phosphate metabolism, metabolic pathways, and the phosphatidylinositol signaling system. Moreover, some of the pathways are directly associated with lipid metabolism; for instance, the phosphatidylinositol signaling pathway enhances lipid metabolism, and insulin secretion inhibits lipolysis. These gene have been associated with various phenotypes including, TG, glucose, ischemic stroke, malaria, BMI, waist and hip circumference Visceral adipose tissue/subcutaneous adipose tissue ratio19. In addition to refining CS99, compared to JAM single-trait fine-mapping18, we were able to find noticeably increased MPP for a variant to be causal for a trait, as well as instances where a completely different variant (r2 < 0.6) is favoured by flashfm over JAM.For fine-mapping, we observed that flashfm produced, on average a smaller CS99 and a larger number of SNPs with MPP > 0.9 than JAM Fig. . We thenThe main strength of this study is that it is the largest GWAS of lipid traits in individuals of African ancestry that covers greater genetic diversity across Africa, while previous studies of African Americans were predominantly of West African ancestry. We used MTAG and flashfm to increase the discovery of additional novel loci and to reduce the credible set size for identifying genetic variants causally associated with lipid traits. The main limitation of this study is the poor generalizability of our findings to individuals of European and Asian ancestry. The GTEx database used for gene expression trait colocalization is predominantly of European ancestry and is not matched for LD with the African ancestry GWAS, which may reduce power. Although our study increased the genetic diversity across Africa, the sample sizes from continental African cohorts were smaller than those of African Americans. Moreover, high heterogeneity was observed among individuals of African ancestry. Nevertheless, we used inverse variance-weighted fixed-effects meta-analysis to address heterogeneity.In conclusion, by meta-analyzing the summary data from APCDR, GLGC, and AWI-Gen, we identified four novel genomic loci associated with lipid traits in individuals of African ancestry. The multivariate approach identified an additional 14 novel loci, and flashfm, on average, produced a smaller CS99 and the largest number of SNPs with MMP > 0.9. Moreover, our findings highlight the importance of studying African ancestry in the context of GWAS and provide insights into the genetics of dyslipidemia in this population.32. UGP is a population-based cohort of around 6407 individuals from roughly 25 neighbouring villages of Kyamulibwa, in the countryside of southwest Uganda in East Africa22. DDS and DCC are urban population-based cohorts in Durban, South Africa. These cohorts were set up to investigate factors influencing diabetes in South African Zulus in Durban, KwaZulu Natal31. The AADM is an ongoing study investigating the genetic epidemiology of non-communicable diseases in Africa. The AADM study comprises 5231 participants recruited from University Medical Centers in Accra and Kumasi in Ghana, Enugu, Ibadan and Lagos in Nigeria and Eldoret in Kenya32. The GLGC included data from the Million Veteran Program (MVP AFR), UK Biobank  and other consortia of individuals of African ancestry in the US16. The AWI-Gen study participants are drawn from five INDEPTH member centres across the African continent, ensuring a balance of west, east and southern African populations from rural and urban settings. These centres are located in Burkina Faso, Ghana, Kenya and South Africa34.In the current analysis, we performed a meta-analysis of GWASs of up to 125,000 individuals of African ancestry. Of these individuals ~14,000 were from the African Partnership for Chronic Disease Research (APCDR) consortium in Africa, ~99,000 were from the GLGC and ~ 11,000 were from the Africa Wits-INDEPTH partnership for Genomic Studies (AWI-Gen). The APCDR comprised the Ugandan General Population Cohort (UGP), the Durban Diabetes Study (DDS), the Durban Case-Control Study (DCC), and the Africa American diabetes mellitus (AADM)20. The quality control step of the 1KAFR reference included the removal of duplicated and singleton variants as well as the removal of one individual from the first and second-degree relatives duos. Genomic inflation factors and LD regression intercepts were calculated to assess inflation in univariate summary statistics and correct for residual stratification in the input cohorts. The intercept of the LD scores regression qualifies the amount of inflation in test statistics due to stratification versus polygenicity36. LD scores within 1 centimorgan (CM) windows and regression intercepts were estimated with the LDSC package.Variants with imputation scores lower than 0.3 were removed from all cohorts. Only SNPs with minor allele frequency (MAF) greater than 0.01 in the combined cohort were retained for further analyses. This set of SNPs was then intersected with the 1000 Genomes phase 3 African super population variants (1KAFR) to ensure allelic orientation consistency, estimation of linkage disequilibrium (LD) between test variants and derive estimates of LD scores for downstream analyses37. Stratification in the participating cohorts was corrected by inflating standard errors by the square root of the LD score regression intercepts.We then performed an inverse variance weighted fixed-effect meta-analysis of the GLGC-AFR, APCDR, and AWI-Gen using genome-wide association meta-analysis (GWAMA) for each of the lipid traits36. It has been shown to increase the power of association for marginal traits while controlling for confounding of population stratification using LD score regression to estimate confounding bias and trait correlation. Given input summary statistics and LD scores, the algorithm estimates marginal association statistics for each input trait while accounting for other traits. In this step, analysis was restricted to SNPs which were not stranded ambiguous, MAF > 0.01 and with sample size N > = (2/3) of the 90th percentile of variant sample size distribution.Multi-trait Analysis of GWAS (MTAG) is a summary statistics-based method for joint analysis that generalizes inverse variance meta-analysis for studies with overlap and different traitsr2 = 0.92). Moreover, it is recommended to remove highly correlated traits from an MTAG as it can lead to inflated associations36, which can result in false positive results. In addition, highly correlated traits can increase the risk of overfitting, which can lead to poor performance and reduce the overall statistical power of the study.We did not include LDL in our MTAG analysis as it was strongly correlated with other lipid traits (p < 5 x 10–8) variants, excluding the MHC region due to its extensive LD structure. Independent loci were identified by distance clumping all significant SNPs around lead variants. For fine mapping, initial regions were constructed around each lead variant from each trait, using an interval of ±100 kb. Regions for multi-trait fine-mapping were then constructed by merging regions in which the lead variants had r2 > 0.5. This resulted in 47 regions that had genome-wide signals from at least two of the lipid traits, for which we applied both JAM single-trait fine-mapping18 and flashfm multi-trait fine-mapping19. Within these 47 regions, we also assessed whether the traits that did not have a variant with p < 5 x 10−8, had a moderately significant variant with p < 1 x 10−6; signals from such traits were also included in our fine-mapping. The distribution of the number of traits having signals fine-mapped in the same region is as follows: 26 regions with two traits, 14 regions with three traits, and seven regions with four traits , we identified all genome-wide significant (We used an in-sample LD reference panel consisting of the Uganda Genome Resource (UGR) and the Zulu cohort, together with the African super-population of 1 K Genomes; first-degree relatives were removed from these cohorts yielding a total of 8850 individuals in the reference panel. Only variants that had a call rate of at least 99% were retained. flashfm requires the trait correlation matrix for the lipid traits, and we obtained Pearson’s correlations based on the in-sample UGR lipids measurements Fig. . Varianthttp://shiny.mrc-bsu.cam.ac.uk/apps/flashfm-ivis/)38.For two of our highlighted regions, we display fine-map integrated regional association plots that indicate p-values by location height (y-axis) and MPP by the diameter of the points , two distinct causal alleles (H3 PP), a causal allele for only one target (H1 or H2) and null of no causal allele within a locus. The method was applied using default priors for meta-analysis or MTAGxTissuexGenexloci combinations and a threshold of H4 PP > 80% was set to assign significance.In addition to fine mapping, to identify potentially mediating molecular traits in the novel loci identified in the GWAS meta-analysis and MTAG analyses, we performed tissue-specific gene expression quantitative trait loci (eQTL) colocalization analysis for the 18 novel loci. GTEx Version 8 tissue-specific gene expression association summary statistics were downloaded from the eQTL catalogueFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


Motivation: Family-based study design is one of the popular designs used in genetic research, and the whole-genome sequencing data obtained from family-based studies offer many unique features for risk prediction studies. They can not only provide a more comprehensive view of many complex diseases, but also utilize information in the design to further improve the prediction accuracy. While promising, existing analytical methods often ignore the information embedded in the study design and overlook the predictive effects of rare variants, leading to a prediction model with sub-optimal performance.Results: We proposed a Bayesian linear mixed model for the prediction analysis of sequencing data obtained from family-based studies. Our method can not only capture predictive effects from both common and rare variants, but also easily accommodate various disease model assumptions. It uses information embedded in the study design to form surrogates, where the predictive effects from unmeasured/unknown genetic and environmental risk factors can be modelled. Through extensive simulation studies and the analysis of sequencing data obtained from the Michigan State University Twin Registry study, we have demonstrated that the proposed method outperforms commonly adopted techniques.Availability: R package is available at https://github.com/yhai943/FBLMM. Family-based study  is one of the most popular designs used in genetic research, and it offers many unique features for risk prediction studies. For example, the relatedness among family members helps capture the predictive effects from unmeasured/unknown polygenic and shared environmental factors, and thus contributes additional information, beyond the measured data, for risk prediction studies . DespiteIt has long been recognized that family history alone can greatly facilitate disease risk prediction. For many complex diseases , individuals with a positive family history are usually classified as the population at high risk . Family IFIH1 gene can lower the risk of type 1 diabetes. Recent developments in prediction research have also shed light on the importance of rare variants in building an accurate prediction model. For example, the risk prediction model for coronary artery disease in European and South Asian populations was built with rare variants incorporated, and it yields improved predictive accuracy  for the prediction analysis on sequencing data obtained from family-based genetic studies. The proposed FBLMM uses the correlations among family members to construct surrogates for unmeasured risk predictors, and it can account for the predictive effects from both common and rare variants. In the following sections, we first presented the details of the proposed model, and then conducted extensive simulation studies to evaluate its performance. Finally, we illustrated its application using the whole-genome exome data from Michigan State University Twin Registry study .The proposed FBLMM is built using a similar idea in BLMM presented in , where wSimilar to existing sparsity regression models , the Xβedure in , instead for β .Similar to linear mixed models that assume the infinitesimal effects , the cumterature , we defiMounting evidences suggest that there are familial aggregations for many complex traits , and theWe set the prior for  from Z.  and decoThe pseudo-code implementing our proposed model is shown in We conducted extensive simulation studies to evaluate the performance of our proposed method under various family-based designs, and further compared FBLMM with other widely used methods, including 1) adaptive MultiBLUP ; 2) DPRVTo closely mimic the real human genome, the founders’ genotypes were drawn directly from Alzheimer’s Disease Neuroimaging Initiative (ADNI) study  shared environmental factors only, 2) genetic factors only, and 3) both environmental and genetic factors.To evaluate the impact of shared environmental factors, we randomly selected 3 genes from ADNI dataset and none of them was set to be causal. For simplicity and without loss of generality, we considered mixed two-generation pedigree structures, including a) half-sibling , parentsPearson correlations and RMSEs are shown in We evaluated the performance of FBLMM when only genetic variants, including both measured and unmeasured, contributed to the familial aggregation of traits. We first randomly selected three genes and set all of them as causal regions. We simulated the outcomes as practice . TherefoPearson correlations and RMSEs are shown in In this set of simulations, we evaluated the performance of FBLMM when outcomes were affected by both shared environmental and genetic factors. Three genes were randomly selected as causal, and outcomes were simulated under the following additive model:Pearson correlations and RMSEs are shown in In this set of simulations, we assessed the effects of pedigree structures on the performance of FBLMM. We considered the twin design , the triWe considered three types of disease models  study. TBED-C is a family-based twin study, aimed at discovering genetic factors that contribute to conduct problems in children . TBED-C assessed . Teacherassessed , we assep values First, to avoid over-fitting and the chance finding problems, 20% samples were randomly select for testing and the rest 80% was used for training. In the training dataset, we assessed the marginal significance for each marker using a linear hybrid model in the GCTA software package . Common Pearson correlations and the RMSEs are shown in In this paper, we have developed a novel FBLMM method for risk prediction analysis on sequencing data obtained from family-based genetic studies. Fundamentally different from existing methods that adjust for family correlations, FBLMM utilizes this relatedness to further improve prediction accuracy. Specifically, it forms two surrogates, including a theoretical kinship coefficient matrix (i.e., posed by , and thuThe importance of genetic and environmental factors in risk prediction has long been appreciated . Many pr studies  and the  studies , we haveRare variants of large effects can play an important role in complex human diseases . It has One of the limitations of our method is that it overlooks the contributions of non-additive effects, especially interactions. As indicated in existing literature , non-linhttps://github.com/yhai943/FBLMM.In summary, we have proposed a Bayesian linear mixed model for risk prediction analysis on genomic data obtained from family-based study designs. Our proposed FBLMM extends the BLMM method, and thus it can not only capture the predictive effects from both common and rare variants, but also accommodate various disease model assumptions. In addition, using study design information, FBLMM forms two surrogates to model the predictive effects from unmeasured/unknown genetic and environmental risk factors, which substantially facilitates family-based prediction studies. The algorithm implementing our proposed method is available at