Striga hermonthica (Benth.) parasitism militates against increased maize production and productivity in savannas of sub-Saharan Africa (SSA). Identification of Striga resistance genes is important in developing genotypes with durable resistance. So far, there is only one report on the existence of QTL for Striga resistance on chromosome 6 of maize. The objective of this study was to identify genomic regions significantly associated with grain yield and other agronomic traits under artificial Striga field infestation. A panel of 132 early-maturing maize inbreds were phenotyped for key agronomic traits under Striga-infested and Striga-free conditions. The inbred lines were also genotyped using 47,440 DArTseq markers from which 7224 markers were retained for population structure analysis and genome-wide association study (GWAS).Striga damage at 8 and 10 weeks after planting (WAP), ears per plant and ear aspect under Striga infestation were detected. Under Striga-free conditions, 11 SNPs significantly associated with grain yield, number of ears per plant and ear aspect were identified. Three markers physically located close to the putative genes GRMZM2G164743 (bin 10.05), GRMZM2G060216 (bin 3.06) and GRMZM2G103085 (bin 5.07) were detected, linked to grain yield, Striga damage at 8 and 10 WAP and number of ears per plant under Striga infestation, explaining 9 to 42% of the phenotypic variance. Furthermore, the S9_154,978,426 locus on chromosome 9 was found at 2.61 Mb close to the ZmCCD1 gene known to be associated with the reduction of strigolactone production in the maize roots.The inbred lines were grouped into two major clusters based on structure analysis as well as the neighbor-joining hierarchical clustering. A total of 24 SNPs significantly associated with grain yield, ZmCCD1 and amt5 genes, respectively and may be related to plant defense mechanisms against Striga parasitism. After validation, the identified loci could be targets for breeders for marker-assisted selection (MAS) to accelerate genetic enhancement of maize for Striga resistance in the tropics, particularly in SSA, where the parasitic weed is endemic.Presented in this study is the first report of the identification of significant loci on chromosomes 9 and 10 of maize that are closely linked to  Striga hermonthica is a parasitic weed causing remarkable reduction in maize yield in the SSA and threatens the livelihoods of more than 300 million people [Striga infestation is most severe in areas with low soil fertility, low rainfall and farming systems characterized by poor crop management practices and little use of inputs such as fertilizer, pesticides, and improved seeds [Striga parasitism range from 20 to 80% and the levels of infestation are often so high that maize may suffer 100% yield loss, and farmers could be forced to abandon their fields [n people . Striga ed seeds . Yield lr fields .Striga seeds germinate in response to strigolactones in the root exudates of maize plants. Germinated seeds develop haustoria which attach to the roots of the host plants through which photosynthates and nutrients are transferred from the maize to the Striga plant [Striga plants survive by siphoning-off water and nutrients from the host plant for its own growth and development. The development of Striga plants impairs the normal host-plant growth, resulting in a large reduction in plant height, biomass, and eventually grain yield [S. hermonthica such as cotton (Gossypium spp), soybean (Glycine max L.), and cowpea (Vigna unguiculata L.), herbicide treatment, hand-pulling, use of catch and trap crops, high nitrogen fertilization and use of tolerant and resistant varieties [ga plant . Once atin yield . Feasiblarieties . Among tStriga from the wild maize (Zea diploperennis L.) and African landraces. Through these efforts, several populations, inbred lines and hybrids with durable Striga resistance have been developed. For example, the early-maturing Striga resistant and drought-tolerant maize inbred line, TZdEI 352, derived from a cross between the normal endosperm white maize population TZEW Pop DT STR and the Z. diploperennis, has displayed increased grain yield and durable Striga resistance [Striga plants in TZdEI 352, implying that non-additive gene action conditioned inheritance of Striga resistance. This inbred TZdEI 352 and other inbreds including TZEI 1203, TZEI 1252, and TZEI 1348, have been reported to possess significant positive/negative general combining ability (GCA) effects for grain yield, Striga damage, number of emerged Striga plants, ears per plant and ear aspect.During the past two decades, the maize improvement program of the International Institute of Tropical Agriculture (MIP-IITA) has placed a major emphasis on the development of stable and durable resistance to sistance . Akaogu sistance  reportedStriga resistance genes into Striga susceptible but outstanding genotypes, through gene stacking. This will allow the development of inbreds, hybrids and other available maize germplasm with durable resistance and increased maize production and productivity in SSA. With the advent of rapid genome-wide high-density marker data using high-throughput and next-generation sequencing technologies, GWAS has become a common tool for identifying resistance genes and/or loci through genome-wide association mapping [Striga resistance in tropical maize.The present focus of the IITA-MIP is to transfer novel  mapping , 10. IdeStriga resistance in maize is very limited. Amusan [2 mapping population involving a cross between the Striga susceptible inbred line, 5057 and the resistant inbred line ZD05. These two QTL accounted for 55% of the phenotypic variability with dominant effects overlapping in importance. Unlike maize, the progress in the identification of QTL for marker assisted selection in sorghum is more advanced. The identification of lg gene mutant alleles at the LGS1 (Low Germination Stimulant 1) locus on chromosome 5 of sorghum has reduced greatly the Striga hermonthica germination stimulant activity [CCD1, CCD7 and CCD8 [ZmCCD1 expression and induced lower germination of Striga [Striga hermonthica, namely ShCCD7 and ShCCD8. In tobacco, the silencing of CCD7 and CCD8 genes retarded the virus parasite formation in the host, indicating that these two genes are key in parasitic life cycle [Information on the identification of QTL for . Amusan  mapped tactivity . This geactivity . In addiand CCD8 , 14. In f Striga . Liu et f Striga  providedStriga resistance breeding, the primary traits of interest in selecting for resistance and high grain yield are host plant damage, number of emerged Striga plants, ears per plant and ear aspect [Striga plants, and the two traits are negatively correlated with yield. Therefore, there is need to identify the genomic regions and genes that control the inheritance of grain yield and closely associated traits for successful use of MAS in maize improvement under Striga field infestation. Identification of genomic regions for Striga resistance would therefore facilitate rapid and efficient transfer of resistance genes to susceptible maize genotypes. The objectives of this study were to i) determine the genetic structure of a panel of 132 diverse early maturing white maize inbred lines with varying levels of resistance to Striga hermonthica parasitism and ii) identify putative genes associated with grain yield and other Striga adaptive traits under Striga-infested and Striga-free environments, using GWAS.In r aspect , 16–18. Striga infested environments revealed significant genotype (G) and environment (E) mean squares for all measured traits except environment mean squares for grain yield and Striga damage at 8 WAP (Table Striga plants at 8 and 10 WAP. Broad sense heritability (H2) estimates under artificial Striga infestation ranged from 47% for number of emerged Striga plants at 10 WAP to 71% for Striga damage at 10 WAP. Moderately high heritability estimates ≥60% were recorded for the Striga resistance indicator traits  under Striga infestation. Under Striga-free conditions, the combined ANOVA displayed significant G, E and G x E mean squares for grain yield, ears per plant and ear aspect. Broad sense heritability estimates ranged from 28% for ears per plant to 58% for ear aspect when Striga-free.Analysis of variance (ANOVA) across AP Table . SignifiStriga adaptive traits differed under artificial Striga infestation , Striga damage at 8 WAP (r = − 0.75**) and 10 WAP (r = − 0.74**), number of emerged Striga plants at 8 WAP (r = − 0.22*) and 10 WAP (r = − 0.24**), and significant positive correlation with ears per plant (r = 0.41**). Similarly, significant positive correlations were obtained between Striga damage at 8 WAP and Striga damage at 10 WAP (r = 0.84**), number of emerged Striga plants at 8 WAP and number of emerged Striga plants at 10 WAP (r = 0.90**), ear aspect and Striga damage (r = 0.71**).The phenotypic correlations among grain yield and other measured ion Fig. . Grain yA total of 47,440 SNPs were generated for the maize inbred lines using DArT sequencing technology. After quality filtering of the unmapped and multilocation markers, SNPs with missing values > 10%, heterozygosity > 20% and MAF < 5% were excluded and thus, a total of 7224 SNPs were retained for the analysis . In contrast, out of 72 inbred lines placed in group 2, there were 52 each of Striga resistant and 20 susceptible inbreds. It is interesting that the grouping of most of the inbred testers was largely based on resistance to Striga. For example, the Striga resistant inbred testers TZdEI 352, TZEI 7 and TZEI 1, the moderately resistant testers TZEI 18, and susceptible tester TZEI 31 were placed in group 1 while the Striga susceptible inbred tester TZEI 19 was placed in group 2.Population structure was inferred through the admixture model-based clustering method . The STRP < 0.001). In addition, 1231 (0.30%) of the loci pairs were in complete LD (R2 = 1). Pearson’s correlation coefficients showed negative and significant correlation (r = − 0.035) between the linkage disequilibrium (R2) and the physical distance (bp) as well as between the P-value and R2 (r = − 0.59), indicating the existence of linkage decay. The rate of LD decay differed across the chromosomes , followed by chromosome 4 (8213 kb) and chromosome 8 (7924 kb).Linkage disequilibrium analysis revealed the presence of 359,926 loci pairs within a physical distance extending up to 55,266,364 bp. About 22.05%  of the loci pairs were in significant LD  = 4  while S5_215,584,703 and S4_76,136,186 explained the least proportion (9%) of the phenotypic variation. Marker S10_133,224,759 located on chromosome 10 was found associated repeatedly with grain yield and Striga damage at 8 and 10 WAP. Similarly, marker S10_112,661,466 on chromosome 10 was found to be common for Striga damage at 8 and 10 WAP.Under artificial  4 Table . The traStriga-free conditions, 11 SNPs significantly associated with grain yield, ears per plant and ear aspect were detected (threshold of –log (p) = 4), accounting for 13 to 23% of the total phenotypic variation observed among the traits. Two markers located on chromosomes 3 and 4 were associated with grain yield and explained 14 to 15% of the phenotypic variation in grain yield. Number of ears per plant had four significant SNPs situated on chromosomes 8, 2 and 1 explaining 13 to 21% of the phenotypic variation. In addition, five different SNPs located on chromosomes 3, 6 and 8 were associated with ear aspect, describing 19 to 24% of the phenotypic variation. Significant SNPs identified for grain yield, ears per plant and ear aspect under Striga-infestation were different from those under Striga-free conditions. The results of the SNPs for grain yield, ears per plant and Striga damage under Striga infestation are illustrated in the Manhattan and quantile-quantile plots , indicating relatively low correlation with each other.Under http://www.maizegdb.org/). The list of annotated genes, including those encoding uncharacterized proteins of the most significant SNPs are presented in Table Striga damage under Striga infestation was located within the candidate gene amt5 (ammonium transporter 5) with identifier GRMZM2G164743. Similarly, SNP S10_16,561,232 having strong association with ears per plant under Striga infestation was located on another candidate gene GRMZM2G310674, which encodes a Polynucleotidyl transferase putative protein, belonging to the ribonuclease H-like superfamily protein. On chromosome 9, SNP S9_7,249,203 associated with grain yield under Striga infestation was physically located within the putative genes GRMZM2G080044 and GRMZM5G898880. These genes are responsible for cellular respiration, oxidative phosphorylation and NAD (P) H dehydrogenase activities. Additionally, SNP S9_154,978,426 significantly associated with Striga damage was located on the protein-coding putative gene GRMZM2G010017 that encodes a protein phosphatase 2C family protein and 2.62 Mb close to the Zmccd1 gene, GRMZM2G057243. On chromosome 5, S5_207,493,972 and S5_215,584,703 associated with ears per plant under Striga infestation were located on the GRMZM2G315127 and 131.43 kb close to GRMZM2G103085 putative genes, respectively. The putative gene GRMZM2G315127 encodes for an uncharacterized protein while the GRMZM2G103085/EREB139 putative gene encodes for ethylene-responsive element-binding proteins. Marker S3_179,448,461 having significant association with Striga damage was found 60.7 kb close to the candidate gene lg2, GRMZM2G060216, which encodes a basic leucine zipper protein transcription factor. On chromosome 7, S7_137,739,978 having significant association with ears per plant under Striga infestation was located on the putative gene GRMZM2G016836, which encodes the NAD (P)-binding Rossmann-fold superfamily protein.The genomic regions of the significant SNPs were examined to identify the protein-coding genes located in or close to the significant SNPs based on the data retrieved from the maize genetic database , respectively. Similarly, SNP S2_210,201,646 on chromosome 2 associated with ears per plant was located on the putative genes GRMZM2G009412 and GRMZM2G125527. These genes encode for Zinc-binding ribosomal protein. SNP S8_96,677,591 significantly linked to ear aspect was found within the gene GRMZM2G083344, encoding glucose-6-phosphate/phosphate translocator-related proteins.Under Striga-infested and Striga-free research conditions. The significant environment mean squares observed for most traits in the present study showed the distinctness of the environments in discriminating among the genotypes under each research condition [Striga adaptive traits implied increased power of SNP detection in the maize panel allowing for identification of true associations between a marker and putative gene [Striga damage, number of emerged Striga plants and ear aspect suggested simultaneous improvement of these traits would result in high yield under Striga infestation. Previous studies identified grain yield, number of emerged Striga plants, Striga damage, ears per plant and ear aspect as the most reliable traits in selecting for Striga resistant maize genotypes, thus justifying their inclusion in the selection index for yield improvement in Striga-prone environments in SSA [The significant variation observed among the inbred lines for grain yield and other agronomic traits revealed the existence of adequate genetic variability among the early maturing maize inbred lines under ondition , 21. Modive gene –24. The s in SSA , 25.Approximately 70% of the SNPs used in the study had heterozygosity < 5%. The lower values of heterozygosity observed among the inbred lines in the two groups indicated that the SNPs were efficient in forming homogenous groups. Thus, making them a valuable resource for genetic studies and association mapping where uniformity of inbred lines and genetic divergence are required . The aveStriga as each group contained both resistant and susceptible inbred lines. The results of the population structure analysis were confirmed by the neighbor joining phylogenetic tree. The average genome-wide LD decay was estimated at 7636kbp at r2 < 0.2. Previous studies reported LD less than 1000 bp for maize landraces, about 100 kb for commercial elite breeding lines and about 830Kbp for diverse breeding lines [Striga resistance, or which act as linked markers associated with Striga resistance.Estimation of population structure and within-group relatedness in maize genomic association studies is necessary in order to reduce the risk of false-positives , 32. Theng lines . The exip-values under the null hypothesis of no associations. Our results revealed that majority of points in the QQ plots were aligned on the diagonal line for all the measured traits, indicating that spurious associations due to population structure and familial relatedness were largely corrected. Similar findings were reported by Kuki et al. [The model fitness for the GWAS was confirmed by inspecting quantile-quantile (QQ) plots that compared the observed and expected i et al. , who ideStriga in the available early maturing tropical maize germplasm, 24 markers significantly associated with Striga damage, number of emerged Striga plants, number of ears per plant, ear aspect and grain yield under Striga infestation were identified, at the threshold of –log (p) = 4. These markers were located on chromosomes 10, 9, 8, 7, 5, 4, 3 and 1. In contrast to the present results, Amusan [Striga on chromosome 6 of maize, using SSR markers and composite interval mapping (CIM) in a late maturing maize F2 mapping population. The first QTL was found between markers umc2170 and bnlg1142 whereas the second QTL was found between SSR markers bnlg1867 and umc1014 [Striga parasitism and accounted for 55% of the phenotypic variation (PV) with predominance of dominance genetic effects over additive genetic effects in the expression of the two Striga resistance QTLs [Striga-free conditions. These markers were located on chromosomes 8, 6, 4, 3 and 2. Lack of SNPs overlap for the measured traits under Striga infested and Striga-free environments indicated the genetic divergence between the two contrasting test environments. In addition, results of the present study showed that the Striga resistance indicator traits under artificial Striga-infested environments were complex in nature and were controlled by multiple minor QTLs with small effects distributed across the maize genome. The markers having significant association with Striga resistance indicator traits such as reduced Striga damage symptoms, increased number of ears per plant and high grain yield under Striga infestation could be used as candidate markers for simultaneous selection for the target traits in maize [Striga resistance in tropical maize improvement programs. Striga resistance in maize is a polygenic trait making it relatively difficult to achieve good progress from selection. However, the identification of molecular markers tightly linked to functional genes is an important step towards development of genotypes with enhanced levels of resistance through gene pyramiding.Marker-trait association analyses have demonstrated that association between specific phenotypes and genotypes within a genome, could lead to the discovery of genes controlling the traits . In orde, Amusan  identifiZmCCD1 gene on chromosome 9 [ZmCCD1 expression that could limit Striga germination [Striga germination [ZmCCD1 gene is involved in the formation of the yellow pigment apocarotenoids [Striga resistance in the maize inbred lines. On chromosome 10, the SNPs S10_133,224,759 and S10_112,661,466 are particularly interesting because they are significant for Striga damage at 8 and 10 WAP whereas S10_133,224,759 was significant for grain yield under Striga infestation. The SNP S10_133,224,759 located at the physical coordinates chr 10: 133,224,759 had the largest proportion of phenotypic variance (46%) for Striga damage in the panel of the IITA early maturing maize inbred lines. This SNP marker is close to the functional gene GRMZM2G164743 (bin 10.05), which encodes an ammonium transporter protein (amt5). AMT genes have been identified in many plant species including Zea mays [Sorghum bicolor [− 3) and ammonium (NH+ 4) are the major forms of nitrogen (N) uptake in higher plants. The NH+ 4 ions accumulate in cells either by direct uptake from the rhizosphere via ammonium transporters (AMTs) or by reduction of NO− 3. Dechorgnat et al. [ZmAMT2.1 gene (a member of the AMT family) to be expressed in all organs with some specificity to roots and tassels. Interestingly, Koegel et al. [ZmAMT2.1 orthologue in sorghum. The authors observed that in sorghum, SbAMT2.1 was expressed in all organs studied with higher expression in roots and stamens. Nitrogen status of the plants is also closely associated with plant defense against Striga parasitism as several authors have reported significant reduction in number of emerged Striga plants under high nitrogen concentration [amt5) identified in the present study could be responsible for the defense mechanism against Striga parasitism in maize. The gene therefore could be a novel target for further unravelling of the regulatory mechanism of Striga resistance in the roots of maize plants and should be tested further in breeding programs for its usefulness in selecting inbred lines with resistance to Striga hermonthica parasitism.The most noticeable candidate genes identified in the present study are located at SNPs S9_154,978,426 and S10_133,224,759 on chromosomes 9 and 10, respectively. The SNP S9_154,978,426 is located at 2.62 Mb from the mination , 40). SiZea mays  and Sorgntration . The SNPlg2 gene was detected for Striga damage. The SNP S3_179,448,461, located at the long arm of chromosome 3, was found close (60.7 kb) to the candidate gene GRMZM2G060216 (3.06), which is associated with loci lg2 (liguleless2). The lg2 gene encodes a basic leucine zipper protein transcription factor and has mutants known to affect leaf angle in maize. In maize, lg1 and lg2 mutants have no ligule or auricle, leading to considerably more upright leaves than their normal counterparts, thereby increasing photosynthetic activity and eventually leading to significant grain yield increase in maize hybrids [lg2 gene may be associated with maize plant defense mechanism under Striga infestation. Previous studies have revealed that Striga infection influences the rate of photosynthesis in the host plants’ leaves by decreasing the effectiveness of the photosynthetic process. This has been demonstrated in sorghum, millet, cowpea as well as in maize [Striga infestation in the inbred lines studied.Marked association around  hybrids . Thus, tin maize . The comin maize  and showPUCHI is required for morphogenesis in the early lateral root primordium of Arabidopsis. However, this putative gene has not been well investigated in maize and needs further studies for better understanding of its importance in Striga resistance.The S5_215,584,703 is close (131.43 kb) to the gene “GRMZM2G103085-ereb13- AP2-EREBP”. EREBPs  containing a single AP2 domain are involved in regulatory networks of response to hormones, pathogen attack, and environmental signals involving DREBs (dehydration responsive element binding proteins) and ERFs (ethylene responsive factors) , 53. IntStriga infestation. The information provided in this study would serve as the starting point for functional gene studies to clarify the genetic mechanisms underlying Striga resistance in tropical maize inbred lines. After validation, the significant loci identified in this study could be targets for breeders in marker-assisted selection to accelerate genetic enhancement of maize for Striga resistance in the tropics, particularly in the West and Central Africa sub-region.Through this GWAS, we were able to detect reliable QTLs associated with maize plant defense mechanisms under Striga resistance in maize. Twenty-four SNPs were significantly associated with Striga adaptive traits in maize. The candidate putative genes GRMZM2G060216, GRMZM2G057243 and GRMZM2G164743, on chromosomes 3, 9 and 10, respectively, could be invaluable for the development of Striga resistant maize genotypes in SSA. Further studies, using different mapping populations, are urgently needed to validate the markers identified in the present study so that marker-assisted breeding for Striga resistance in tropical maize could be a reality and widely adopted in SSA where Striga is endemic.To the best of our knowledge, the present study is the first report of genome-wide association analysis for 8 inbred lines, five standard inbred testers and one inbred check) with combined resistance to Striga hermonthica, and tolerance to drought stress developed in the IITA maize improvement program were used in this study. Out of the 132 inbred lines, 126 were second generation lines extracted from six F2 populations derived from bi-parental crosses , which involved crosses among a broad based Striga resistant population (TZE COMP 5-W DT C7) and elite inbred lines from TZE-W Pop × 1368 STR , TZE-W Pop STR C0 , and TZE-W Pop x LD (TZEI 31). Of the five standard testers, inbreds TZEI 18, and TZEI 19 were derived from TZE-W Pop STR C0, inbred TZEI 31 from TZE-W Pop x LD, inbred TZEI 7 from WEC STR and inbred TZdEI 352 from TZE-W Pop STR.A total of 132 early maturing tropical white maize inbred lines  during the rainy seasons of 2017 and 2018. The experimental design was 11 × 12 alpha lattice with two replications. Each experimental unit consisted of 3 m single-row plots, with a row spacing of 0.75 m and intra-row spacing of 0.4 m. The fields were injected with ethylene gas at about 10 days before planting, to stimulate suicidal germination of residual Striga seeds in the soil. The artificial Striga infestation at Mokwa was carried out as recommended by IITA-MIP [Striga seeds collected from sorghum fields were stored for about 6 months to break seed dormancy and used for the infestation. Each hole in the Striga plot received about 5000 germinable seeds of Striga mixed with fine sand in the ratio 1:99. Fertilizer rate was reduced  and application was delayed till 3 weeks after planting to induce the production of strigolactones which stimulate good germination of the Striga seeds and the attachment of the Striga plants to the roots of host plants [Striga infestation, data were collected on number of emerged Striga plants and host plant damage syndrome rating at 8 and 10 weeks after planting (WAP). The host plant damage syndrome rating was recorded on a scale of 1–9 . Under both Striga-infested and Striga-free environments, data were collected on the inbred lines for ear aspect, number of ears per plant and grain yield.The inbred lines were phenotyped for key agronomic traits across two environments under artificial IITA-MIP . Striga Striga resistant and susceptible inbred lines under artificial Striga infestation [I = ((2 × YLD) + EPP - (SDR8 + SDR10) – 0.5(ESP8 + ESP10)), where YLD = grain yield of the Striga-infested plots, EPP is the number of ears at harvest in the Striga infested plots, SDR8 and SDR10 were Striga damage syndrome ratings at 8 and 10 WAP, and ESP8 and ESP10 were number of emerged Striga plants at 8 and 10 WAP. Broad sense heritability (H2) estimates were calculated from phenotypic variance (σ2p) and the genotypic variance (σ2g) [Analyses of variance (ANOVA) were performed across test environments for each experiment on plot mean basis for grain yield and other key agronomic traits with PROC GLM in SAS , using aestation . The meace (σ2g) . Correlace (σ2g) .www.diversityarrays.com/files/DArT_DNA_isolation.pdf). The quality and quantity of the DNA was ascertained by running the gDNA in a 1% agarose gel and measuring its concentration and purity in a NanoDrop 2000 spectrophotometer. The DNA samples were sent to the Integrated Genomic Service and Support (IGSS) genotyping platform, Nairobi, Kenya for genotyping. High-throughput genotyping was conducted in 96 plex DArTseq protocol as described previously [Zea mays L. genome reference, version AGPV3 (B73 Ref-Gen v4 assembly) [Samples of leaves were taken in the field at 2 weeks after planting. The DArT protocol was used for genomic DNA extraction (eviously . Reads assembly) , resultik = 1 to k = 10 for an initial assessment of the most likely number of subpopulations; each K was run 10 times with 10,000 iterations of burn-in followed by 10,000 Markov chain Monte Carlo iterations and the ideal number of subpopulations (K) was found by examining the optimal ∆K value [Q values) for each inbred line were estimated to have its memberships in multiple subgroups. Inbred lines with membership probabilities ≥0.70 were assigned to the corresponding subgroup and lines with membership probabilities < 0.70 were assigned to a mixed subgroup. Linkage disequilibrium was determined using the squared allele frequency correlations R2 value from which the number of significant allele pairs (P < 0.01) was determined using 1000 permutations [β + Wα + Qv + Zu + ε where Y is the observed vector of means; β is the fixed effect vector (p × 1) other than molecular markers effects and population structure; α is the fixed effect vector of the molecular markers; ν is the fixed effect vector from the population structure; u is the random effect vector from the polygenic background effect; X, W, and Z are the incidence matrixes from the associated β, α, ν, and u parameters; and ε is the residual effect vector. MLM in comparison with other models for detecting marker/trait associations such as the general linear model (GLM), could reduce the false-positive associations by controlling both types I and II errors [p-values for all the traits [Striga resistance, the physical positions of the significant SNPs were compared with the MaizeGDB database according to version 4 (RefGen_v4) from the reference genome of the maize B73 inbred line, available at the MaizeGDB database. Zoom mapping was conducted on the chromosome where a significant SNP marker was identified and associated with a trait. The extent of local LD was evaluated for each selected significant SNP to determine the interval of each locus. The heatmap of regional LD was made with the LD heatmap package [An admixture model-based clustering method was used to infer population structure of the 132 genotypes using the software package STRUCTURE, version 2.3.4 . The ass∆K value  in STRUC∆K value . In the utations .. AssociI errors , 66. Thee traits , 35, 67. package  for SNPsAdditional file 1: Figure S1. Quality filtering of 44,470 markers among 132 maize inbred lines. Description of data: Quality filtering of 44,470 markers among 132 maize inbred lines that were used for population structure analysis and the GWAS.Additional file 2: Table S1. Summary statistics of the 7224 filtered high-quality SNP markers. Summary statistics of the 7224 filtered high-quality SNP markers obtained from the total 44,470 markers.Additional file 3: Figure S2. Genome-wide and chromosome-wide linkage disequilibrium (LD) decay plot. LD decay plot estimated based on pairwise squared allele frequency correlation coefficients (R2) among 7224 SNPs distributed across the 10 maize chromosomes. The values on the y-axis represent the squared correlation coefficient R2 and the x-axis represents the genetic distance in megabases (mb).


ROC = 0.98 ) for the prediction of a homozygous EE disorder. Sensitivity and specificity values do not justify the use of Sysmex XN-1000 IP flag messages for identification of genetic hemoglobinopathies in Cambodian women. Development of an algorithm based on MCV and RBC biomarkers may optimize the screening ability of automated hematology analyzers.Genetic hemoglobinopathies are the most common single-gene disorder worldwide. Some automated hematology analyzers have the capability of flagging individuals who may have hematological disorders based on complete blood count (CBC) biomarkers. We aimed to evaluate the accuracy of a hematology analyzer in identifying genetic hemoglobinopathies in Cambodian women and to determine which hematological biomarkers are the best predictors. A CBC was completed using a Sysmex XN-1000 analyzer and hemoglobinopathies were determined with capillary hemoglobin electrophoresis for 808 nonpregnant Cambodian women. Sysmex XN-1000 Interpretive Program (IP) messages, which flag potential hematological disorders, were produced from CBC results. Then, 2 × 2 tables were used to determine sensitivity and specificity of the IP message “Hemoglobin defect” to detect a genetic hemoglobinopathy. Receiver operating characteristic (ROC) analyses assessed the diagnostic ability of six CBC biomarkers to predict a genetic hemoglobinopathy. In total, 74% of women had a hemoglobinopathy . “Hb defect” IP message sensitivity and specificity for genetic hemoglobinopathy detection were 10.4% and 98.6%, respectively. Variable selection strategies yielded a two-variable model including mean corpuscular volume (MCV) and red blood cell (RBC) count (AIC = 99.83, AUC Genetic hemoglobinopathies are autosomal recessive disorders that result from nucleotide substitutions  or deletions  in the genes encoding the α- or β-globin chains of hemoglobin . Some ofGenetic hemoglobinopathies can be detected using several methods such as capillary hemoglobin electrophoresis, isoelectric focusing, or high-performance liquid chromatography; however, DNA analysis is sometimes required for definitive diagnosis . These lAutomated hematology analyzers are blood count analyzers that are capable of measuring numerous hematological biomarkers, also referred to as a complete blood count (CBC), in a small blood sample. The biomarkers included in a CBC vary by analyzer but often include hemoglobin, hematocrit, red blood cells (RBCs), white blood cells (WBCs), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and red blood cell distribution width (RDW). These biomarkers are often used to diagnose and categorize anemia type and severity and to assess for other hematological disorders. The Sysmex XN-1000 is a hematology analyzer which generates a “flag” for individuals who may be at risk of certain hematological disorders based on the CBC results (referred to as an interpretive program (IP) message). Some examples of Sysmex XN-1000 IP messages include: RBC agglutination (clumping), iron deficiency, and hemoglobin defects . IP messThe aims of this study were to evaluate the accuracy of the IP message for hemoglobin defects (“Hb defect”) in identifying Cambodian women with a genetic hemoglobinopathy by comparing these flags to definitive testing performed through hemoglobin electrophoresis and to determine which combination of CBC biomarkers can best predict the presence of a genetic hemoglobinopathy.ClinicalTrials.gov (NCT02481375). The full trial has been reported elsewhere , α1 cd 59 [GGC>GAC] (Hb Adana), α2 init cd [ATG>ACG], α2 cd 19 [-G], α2 IVS1 [-5nt], α2 cd 59 [GGC>GAC], α2 cd 125 [CTG>CCG] (Hb Quong Sze), α2 cd 142 [TAA>CAA] (Hb Constant Spring), α2 cd 142 [TAA>AAA] (Hb Icaria), α2 cd 142 [TAA>TAT] (Hb Pakse), α2 cd 142 [TAA>TCA] (Hb Koya Dora), α2 poly A-1 [AATAAA-AATAAG], α2 poly A-2 [AATAAA-AATGAA].Capillary hemoglobin electrophoresis for the detection of hemoglobin variants  was conducted by a trained external consultant at NIPHL in Cambodia using a MINICAP analyzer . This automated technique quantifies the different types of Hb in blood for interpretive diagnosis and can detect normal Hb  and Hb variants ). The Sebia analyzer has an advantage over other methodologies as it can distinguish between Hb A2 and Hb E variants, both of which are common in Cambodia . HemogloThe primary outcome for this analysis was the presence of a genetic hemoglobinopathy, previously determined using genetic hemoglobin typing conducted as part of the original 2015 trial . The “HbROC) serving as a measure of the ability of the model to discriminate between women likely to have a hemoglobinopathy and those unlikely to have a hemoglobinopathy. AUCROC values range from 0.5 to 1, where larger values indicate greater discrimination ability and stronger predictive power; generally, an AUCROC value greater than 0.8 denotes a good classifier [CBC biomarkers  were compared across groups of women with a genetic hemoglobinopathy and those without, using Kruskal–Wallis tests with subsequent posthoc evaluation of pairwise comparisons. Sensitivity and specificity values were calculated from 2 × 2 contingency tables as an initial assessment of the accuracy of the IP flag messages in correctly identifying women with a genetic hemoglobinopathy. Receiver operating characteristic (ROC) analyses were used to assess the diagnostic ability of the six CBC biomarkers to predict whether or not a woman had a genetic hemoglobinopathy, with the area under the ROC curve  and correlation values between predictors. Univariate (unadjusted) analysis through fitting single-biomarker models was also performed to determine the directionality of the association between the CBC hematological biomarkers and the outcome of interest. Both variable significance and Akaike information criterion (AIC) were considered in the process of selection. Further assessment of model predictive ability was performed through cross-validation methods of splitting the observed data into training and validation sets in order to flag problems such as overfitting or selection bias .Statistical analyses were conducted using R version 3.6.3 (R Core Team 2020) and SAS version 9.4 .n = 808 women (99%) from the original trial were available for these diagnostic analyses; hematological values for n = 1 woman were missing due to a clotted sample and the subsequent inability to perform a CBC. Characteristics and hematological statuses of enrolled women are presented in Data from a total of p < 0.0001, adjusted for multiple comparisons). Posthoc Dunn’s multiple comparison tests were performed across each biomarker to compare distributions between two selected hemoglobinopathy classification groups.p < 0.0001) for all biomarkers as compared with both women with no disorder and compared with women with any of the other hemoglobinopathies . Biomarker distributions between α-thalassemia, heterozygous AE, and coinherited α-thalassemia/heterozygous AE groups were not significantly different for any biomarker except for MCV where there was a significant difference between α-thalassemia and heterozygous AE groups (p = 0.0009). Comparisons between the “no disorder” group and the other specific hemoglobinopathies (aside from homozygous EE) were significant for reticulocyte hemoglobin, MCV, MCH, RDW, and RBC (p < 0.0001)—these significant comparisons were not observed for hemoglobin.Notably, women with the homozygous EE disorder had significantly different distributions (p < 0.0001).We also compared the distributions of CBC biomarkers in women with no hemoglobinopathy to women with “any” hemoglobinopathy  . NotablyAssessment of the sensitivity and specificity of the Sysmex IP message (“Hb defect”) was performed through calculation from 2 × 2 tables displaying the agreement between the IP flag and the presence of a true genetic hemoglobinopathy. The performance of the “Hb defect” flag alone was considered in addition to the performance of any IP message at flagging women with genetic hemoglobinopathies. Sensitivity and specificity values of these flag messages are summarized in ROC values of the six CBC biomarkers for predicting (a) any genetic hemoglobinopathy and (b) the homozygous EE disorder are displayed in ROC curves and corresponding AUCROC values > 0.8). However, further exploration of potential improvements in predictive ability through the simultaneous evaluation of several biomarkers may justify a reliable multibiomarker algorithm. Among the six biomarkers, only MCV and RBC retained the proper directionality once included in the multivariate model, suggesting that multicollinearity is present for the other biomarkers. VIF values for the remaining biomarkers were also found to be large (>10), further justifying their exclusion.Individual CBC biomarkers appeared to perform very well in terms of predicting the homozygous EE disorder ). Performing a likelihood ratio test between this multibiomarker model and a model containing MCV alone ) revealed a significant result (p = 0.002) in favor of the multibiomarker model in terms of model fit. The regression summary of the final multivariate model is presented in Variable selection strategies yielded a two-variable model including MCV and RBC  with all AUCROC values > 0.98. This suggests good robust predictive performance of the proposed model with biomarkers of MCV and RBC.Performing cross-validation on the multibiomarker model using training set and validation set splits of 50:50, 70:30, and 80:20 revealed insignificant AUCThe multibiomarker model summarized in Predicted probabilities of having a hemoglobinopathy can be calculated from the risk scores (log odds ratios) given by the regression equation for the selected predictive model:The predicted probability of having the homozygous EE disorder can be calculated as:The predicted probability obtained from a woman’s baseline MCV and RBC measurements can then be used for screening purposes. A higher value of the risk score suggests a greater risk of the woman having the homozygous EE disorder. In screening, it may be desired to select a cut-off to identify women that are likely to have the disorder. A predicted probability of 0.5 can be applied as a conservative cut-off; however, field-specific knowledge can further guide the selection of this threshold value. Predicted probabilities higher than this cut-off value suggest an individual is at high risk of having the homozygous EE disorder, and as such, it may be beneficial to recommend the individual for further definitive testing.In our study among Cambodian women of reproductive age, we found that the Sysmex XN-1000 IP message “Hb defect” was not accurate in flagging women with a genetic hemoglobinopathy. In fact, women who were flagged for any IP message (generic) had a higher sensitivity and specificity trade-off as compared with women who were flagged for the “Hb defect” (specific). Ultimately, the sensitivity and specificity values (for any of the IP messages) are not high enough to justify using the Sysmex IP flag messages as a preliminary screen for the presence of a genetic hemoglobinopathy. With such low sensitivity, too many cases  would likely be missed.The “Hb defect” flag is purportedly obtained with use of an algorithm that is based upon MCV and RDW values. We observed that including both MCV and RDW biomarkers in the same model for predicting the presence of a homozygous EE disorder artificially altered the directionality of their association with the outcome. This change in sign of regression coefficients is evidence of multicollinearity and indicates that MCV and RDW should not be included together in the same predictive model. As such, the current algorithm may not be appropriate for flagging this severe form of genetic disorder. Across both sets of ROC curves, MCV consistently appeared to have the strongest predictive ability of all six biomarkers. Therefore, if only one biomarker should be included in an algorithm designed to predict hemoglobinopathies, our data would suggest MCV over RDW.ROC values for all CBC biomarkers, however, were higher in the homozygous EE disorder model, as compared with the “any” hemoglobinopathy model. The homozygous EE disorder is a severe disorder which has a more clinically distinct phenotype , suggesting that development of an accurate IP flag for this specific disorder may be more clinically relevant and appropriate. Individuals flagged for this severe disorder could then be referred for further definitive genetic testing, which would result in better targeting for follow-up testing.The “Hb defect” flag performed poorly when it was specifically used to detect a homozygous EE disorder, as compared with when it was used to detect “any” hemoglobinopathy, as well as when it was used to detect any of the other hemoglobinopathies . AUCIn 2015, Hoffmann et al. conducted a meta-analysis of the 12 indices most frequently used to differentiate iron deficiency anemia from thalassemia trait among those with microcytic anemia . Here, tNivaggioni et al. analyzed hematological parameters from the Sysmex XN-10  using a two-step decision by classification and regression tree (CART) to predict types of anemia, including iron deficiency anemia, heterozygous haemoglobinopathy, sickle cell disease syndrome, hereditary spherocytosis and other, in 8217 patients 15+ years) from a hospital in France [+ years fKarnpean et al. have also showed that the sensitivity of MCV and MCH biomarkers to predict a hemoglobin E disorder is substantially increased when assessed in combination with a simple dye test (dichlorophenolindophenol or DCIP test) . The autA strength of our study is our comprehensive genotyping for the most common hemoglobinopathies in Cambodian women  and 21 different α-globin deletions and point mutations); however, although comprehensive, we recognize that this list may not be exhaustive. Other rare hemoglobinopathies may have been present in women but were not captured by our comprehensive genomic testing. Further, we acknowledge that the “Hb defect” IP flag may be more accurate in identifying other hemoglobinopathies that are more common in other geographical areas of the world, such as sickle cell disease in sub-Saharan Africa . We base


Circulating lipoprotein lipids cause coronary heart disease (CHD). However, the precise way in which one or more lipoprotein lipid-related entities account for this relationship remains unclear. Using genetic instruments for lipoprotein lipid traits implemented through multivariable Mendelian randomisation (MR), we sought to compare their causal roles in the aetiology of CHD.P < 5 × 10−8 for LDL cholesterol (220), apolipoprotein B (n = 255), triglycerides (440), HDL cholesterol (534), and apolipoprotein A-I (440). Between 56%–93% of SNPs identified for each lipid trait had not been previously reported in large-scale GWASs. Almost half (46%) of these SNPs were associated at P < 5 × 10−8 with more than one lipid-related trait. Assessed individually using MR, LDL cholesterol , triglycerides  and apolipoprotein B  had effect estimates consistent with a higher risk of CHD. In multivariable MR, only apolipoprotein B  retained a robust effect, with the estimate for LDL cholesterol  reversing and that of triglycerides  becoming weaker. Individual MR analyses showed a 1-standard-deviation–higher HDL cholesterol  and apolipoprotein A-I  to lower the risk of CHD, but these effect estimates attenuated substantially to the null on accounting for apolipoprotein B. A limitation is that, owing to the nature of lipoprotein metabolism, measures related to the composition of lipoprotein particles are highly correlated, creating a challenge in making exclusive interpretations on causation of individual components.We conducted a genome-wide association study (GWAS) of circulating non-fasted lipoprotein lipid traits in the UK Biobank (UKBB) for low-density lipoprotein (LDL) cholesterol, triglycerides, and apolipoprotein B to identify lipid-associated single nucleotide polymorphisms (SNPs). Using data from CARDIoGRAMplusC4D for CHD , we performed univariable and multivariable MR analyses. Similar GWAS and MR analyses were conducted for high-density lipoprotein (HDL) cholesterol and apolipoprotein A-I. The GWAS of lipids and apolipoproteins in the UKBB included between 393,193 and 441,016 individuals in whom the mean age was 56.9 y (range 39–73 y) and of whom 54.2% were women. The mean (standard deviation) lipid concentrations were LDL cholesterol 3.57 (0.87) mmol/L and HDL cholesterol 1.45 (0.38) mmol/L, and the median triglycerides was 1.50 (IQR = 1.11) mmol/L. The mean (standard deviation) values for apolipoproteins B and A-I were 1.03 (0.24) g/L and 1.54 (0.27) g/L, respectively. The GWAS identified multiple independent SNPs associated at These findings suggest that apolipoprotein B is the predominant trait that accounts for the aetiological relationship of lipoprotein lipids with risk of CHD. There is uncertainty regarding which lipid or apolipoprotein trait is the predominant atherogenic agent involved in the aetiology of lipids and coronary heart disease (CHD).The elucidation of such is important because not only does it clarify the aetiological understanding of the pathogenesis of CHD, it also hones the focus on the lipid- or apolipoprotein-related trait that should be the focus when developing lipid-modifying interventions.P < 5 × 10−8) with LDL cholesterol (220 genetic variants), apolipoprotein B (255 genetic variants), triglycerides (440 genetic variants), HDL cholesterol (534 genetic variants), and apolipoprotein A–I (440 genetic variants). Hundreds of these variants identified were novel, to our knowledge.We used data on up to 441,016 participants from the UK Biobank to conduct genome-wide association analyses to find genetic variants reliably associated with lipoprotein lipid and apolipoprotein concentrations: this led to the identification of multiple independent genetic variants, each associated very robustly (at When we explored the potential causal role of these lipids and apolipoproteins in isolation using Mendelian randomisation, we found evidence compatible with LDL cholesterol, triglycerides, and apolipoprotein B increasing the risk of CHD and HDL cholesterol and apolipoproteins A-I lowering CHD risk.When we examined these lipids and apolipoproteins together in multivariable Mendelian randomisation, we found that only apolipoprotein B retained a robust relationship with the risk of CHD—the effect estimates for all other entities were either substantially attenuated to the null or reversed in direction.The analytical approach in this study implemented through multivariable Mendelian randomisation simultaneously accounts for genetic associations with lipids and apolipoproteins and should therefore provide more reliable insights into what is the underlying driver of CHD.These findings support that, amongst the repertoire of lipoprotein lipids and apolipoproteins that we investigated, apolipoprotein B has a fundamental role in the aetiology of CHD. There is incontrovertible evidence that lipids play a causal role in the aetiology of coronary heart disease (CHD) –3. MultiEach circulating atherogenic lipoprotein particle includes one apolipoprotein B molecule, but the amount of cholesterol  and the amount of triglycerides  can vary extensively between lipoprotein particles –12. ThusDisentangling the relationships of atherogenic lipoprotein lipids and risk of CHD is nontrivial, given the correlated nature of these traits ,18. FromIn genetic analyses, a similar ‘discordance’ analytical approach can help to elucidate the comparative causal roles of lipid-related traits on CHD through the selection of genetic variants that associate with more than one trait. This can help identify which of the traits has a predominant influence on risk of CHD. Such studies have identified that, when genetic associations with LDL cholesterol or triglycerides are discordant with their association with apolipoprotein B, it is apolipoprotein B that retains the most robust relationship. ,26 AnothIn this study, we sought to use human genetics to disentangle which one or more of the atherogenic lipid-related traits  predominantly accounts for the causal relationship with risk of CHD. We first conducted a de novo genome-wide association study (GWAS) of lipid-related traits using the UK Biobank (UKBB) to identify variants robustly associated with each trait. We then conducted MR analyses, including multivariable MR, to elucidate which of the atherogenic lipid traits is of fundamental relevance to CHD. Finally, we investigated whether the entity underlying the causal role of atherogenic lipid-related traits in CHD also accounted for the inverse association of HDL-related phenotypes with CHD.We used data from the UK Biobank (UKBB) under application #15825 and summary estimates from CARDIoGRAMplusC4D . DetailsWe explored the following traits measured in the UKBB: LDL cholesterol, apolipoprotein B, triglycerides, HDL cholesterol, and apolipoprotein A-I. Details on sample handling and assays used have been previously described ,32.Lipid-related traits in the UKBB were standardised/normalised using inverse rank-normalisation such that the mean was 0 and standard deviation was 1, allowing comparison of effect estimates between traits.P < 5 × 10−8), and SNPs were binned into loci based on pairwise linkage disequilibrium , with the SNP with the strongest association with the trait of interest  being retained in each locus. This process  was undertaken for each trait in turn using the software PLINK [P < 5 × 10−8 in our analyses in which an association had not been previously reported at P < 5 × 10−8, within 1 MB and at r2 < 0.001, by the Global Lipids Genetics Consortium [GWAS analyses were conducted in UKBB participants of European descent based on K-means clustering (K = 4) after standard exclusions including withdrawn consent, mismatch between genetic and reported sex, and putative sex chromosome aneuploidy . We idenre PLINK , based ore PLINK . We definsortium  (for LDLnsortium  (for apoP < 5 × 10−8) were taken forward to generate genetic instruments for each phenotype. A genetic instrument consists of one or more genetic variants that has characteristics that enable its use as an instrumental variable in MR [P < 5 × 10−8) and used this information to generate a Venn diagram. Further LD clumping was undertaken to define the unique number of overlapping genetic loci between all 5 traits to be depicted in the Venn diagram. Second, we characterise instrument ‘specificity’  by taking per-allele SNP estimates from our GWAS for each lipid trait and conducting inverse variance weighted regressions on these summary estimates to elucidate the association of genetic instruments across the various lipid-related traits—these estimates are presented as standardised differences per 1-standard-deviation–higher genetically predicted trait. Whilst we recognise that this approach may be prone to inflation , the primary motivation is to characterise the associations of lipid instruments with the lipid-related traits: we do not interpret these as formal instrumental variable estimates. We also estimated the correlations between the beta coefficients for the genetic instruments used in the multivariable analyses.SNPs associating with lipid-related traits at conventional GWAS thresholds . This led to a small drop in the number of SNPs being available for MR, with a median of 93% SNPs identified in GWASs available for MR ; see S1We characterised instrument strengths in both the univariable and multivariable MR settings as follows: for the univariable estimates, we generated the mean F-statistic, using the approximation described by Bowden and colleagues . For thehttp://bioinformatics.psb.ugent.be/webtools/Venn (accessed 13th August 2019).The BOLT-LMM software was used to undertake GWAS . This doP-value into being ‘significant’ or not [Whilst we desisted from dichotomising results of analyses purely on the basis of a ’ or not ,50, as aIn sensitivity analyses, we conducted univariable MR analyses robust to some forms of potential unbalanced horizontal pleiotropy  . There were no data-driven changes to analyses. Following peer review, we (i) repeated the GWAS of lipids and apolipoproteins with adjustment for fasting status and repeated MR analyses using genetic instruments from such GWAS; (ii) repeated multivariable MR including LDL cholesterol or triglycerides (rather than apolipoprotein B) to demonstrate a residual effect of HDL cholesterol, in keeping with published literature; (iii) added multivariable MR-Egger as a sensitivity analysis ; and iv.The lipid and apolipoprotein traits were measured in 393,193 to 441,016 individuals in the UKBB that have GWAS genotyping . The meaP < 5 × 10−8 with each lipid-related trait: 220 SNPs (of which 56% had not been previously reported) associated with LDL cholesterol, 440 (77% novel) for triglycerides, 255 (80% novel) for apolipoprotein B, 534 (72% novel) for HDL cholesterol, and 440 (93% novel) for apolipoprotein A-I  with other lipid traits  of SNPs used in each of the lipid-related genetic instruments showed associations at conventional GWAS significance (d traits . On expld traits . For exad traits .P < 0.001) for CHD, with the corresponding value for triglycerides being OR 1.34, 95% CI: 1.25–1.44, and P < 0.001 and those for apolipoprotein B being OR 1.73, 95%CI: 1.56–1.91, and P < 0.001. Sensitivity analyses using methodological approaches that take into account potential genetic pleiotropy led to no substantive change in these estimates  . A 1-stastimates .P < 0.001) retained a robust, potentially causal relationship with CHD (P = 0.44). The estimate for triglycerides was weakened substantially .When LDL cholesterol, triglycerides, and apolipoprotein B were assessed together in multivariable MR, only apolipoprotein B , and for apolipoprotein A-I, it was OR 0.83 . The effect estimates for both HDL cholesterol and apolipoprotein A-I were diminished when using methodologies that are more robust to potential pleiotropy of the genetic variants used in the analysis , and for apolipoprotein A-I, it was OR 0.94 . When adjusted for HDL cholesterol and apolipoprotein A-I, there was robust evidence for a potential causal role of apolipoprotein B in the development of CHD .On appraisal in a multivariable MR analysis that included apolipoprotein B , the effect estimates of both HDL cholesterol and apolipoprotein A-I diminished and were not distinguishable from the null . The adjThe F-statistics for all lipid-related genetic instruments in both the univariable and multivariable MR settings were consistent with an absence of weak instrument bias .Adjustment for fasting time in the GWAS of lipids and apolipoproteins led to negligible alterations in the effect estimates in both univariable and multivariable MR  Tables. In this study, we explored the causal relationships of lipids and apolipoproteins with risk of CHD by conducting a GWAS of lipoprotein lipid-related traits in approximately 440,000 UKBB participants and taking forward genetic instruments into multivariable MR using data from the CARDIoGRAMplusC4D consortium, including over 60,000 cases of CHD. Our principal findings are that after taking into account the effects of apolipoprotein B, the relationships of other lipid-related entities with risk of CHD attenuated markedly to the null. In contrast, the relationship of apolipoprotein B with risk of CHD was robust to adjustment for other traits. Our study therefore provides empirical evidence from human genetics that apolipoprotein B is the necessary element in order for lipoprotein lipids to exert their causal effect on risk of CHD—in other words, apolipoprotein B is a critical entity that underlies the relationship of lipid traits and risk of CHD. This adds further evidence to support the hypothesis that it is the number of atherogenic lipoprotein particles indexed by apolipoprotein B rather than the amount of circulating cholesterol or triglycerides per se that is the important driver of CHD . In otheOur GWAS identified many hundreds of variants associated with the major lipid-related traits, with most SNPs identified being novel, to our knowledge. This is likely because our GWAS was not only larger in sample size than prior GWAS of lipoprotein lipid traits  but alsoHow do these findings enhance the evidence base relating to lipid traits and vascular disease? Large-scale observational , interveHow do these findings aid us in the context of developing drugs that modify lipoprotein lipid concentrations and predicting their effects on risk of CHD? Drug-target MR studies show that, for example, modifying triglycerides through therapies such as angiopoietin-like proteins 3 and 4 (ANGPLT3/4) inhibition may represent an emerging approach to lowering the risk of CHD –65. Our We note that this interpretation is in keeping with 2 important prior investigations that examined the concordance of CHD associations between SNPs associated with apolipoprotein B, LDL cholesterol , and triThe findings that we make have been made available by 2 recent advances: first, the availability of large-scale lipoprotein lipid phenotyping and GWAS genotyping in the UKBB, providing sufficiently large numbers to permit identification of robust genetic variants (and therefore suitable genetic instruments) in order to conduct MR of each of the lipid-related traits. Use of a single study with similar numbers of individuals with measures available for each lipid-related trait enabled GWAS and the downstream synthesis of genetic instruments for each trait in which the genetic architecture of each phenotype ought to be similarly represented, allowing for a more rigorous comparative assessment of the traits in both the univariable and multivariable MR setting. Second, methodological developments in MR to include more than one trait  allows for direct effects  of multiple exposures to be assessed simultaneously and without the risk that this introduces forms of bias such as collider bias, in which, for example, conditioning on a potential mediator or a shared outcome can induce bias . It is tP < 0.001), all in a directionally consistent way to their association with apolipoprotein B. Thus, whilst it may be tempting to generate instruments based on SNPs that fall into these visually discrete silos , they are likely to be directionally pleiotropic. Indeed, given the physiological relationships of lipids and apolipoproteins, it could be argued that for a SNP to appear in the extremities of the Venn diagram , such a SNP would likely possess properties that lead to this situation through pleiotropy, rendering MR estimates derived from such a SNP more vulnerable to producing misleading findings. For these reasons, we consider the findings from our multivariable MR analyses to be more trustworthy than MR analyses based on subsetting SNPs on the basis of associations with traits at arbitrarily defined P-value thresholds.Our study has interpretational challenges that may be conceived of as limitations. For example, a naïve interpretation of our findings would be that apolipoprotein B confounds the relationship of LDL cholesterol and triglycerides with risk of CHD, but this would be to neglect evidence gleaned over a century of scientific investigations into atherosclerosis . In our In conclusion, our findings demonstrate that apolipoprotein B is of critical importance in facilitating the causal effects of lipoprotein lipid traits and risk of CHD.S1 STROBE Checklist(PDF)Click here for additional data file.S1 FigMultivariable MR: (A) conventional MR, (B) conventional MR confounded by pleiotropy, (C) multivariable MR. (A) In conventional MR, in the absence of confounding of the genetic instrument and with exclusion restriction, a suitably powered analysis can lead to causal deductions. (B) In this example, SNPs used in the LDL cholesterol instrument also associate with apolipoprotein B (apoB), leading to potential confounding of the estimate of LDL cholesterol and risk of CHD. (C) In multivariable MR, genetic instruments for each of the traits under investigation are used together with the corresponding associations of SNPs with each trait in the analysis. The analysis yields a causal estimate for each trait taking into account the relationship of that trait and the genetic variants with the other traits in the analysis. These figures are schematic representations and should not be interpreted as formal directed acyclic graphs. apoB, apolipoprotein B; CHD, coronary heart disease; CI, confidence interval; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MR, Mendelian randomisation; SNP, single nucleotide polymorphism.(PDF)Click here for additional data file.S2 FigEffect estimates are ORs of CHD per 1-standard-deviation–higher genetically instrumented trait, using a range of univariable MR approaches see . Plot ge(PDF)Click here for additional data file.S1 TableHDL, high-density lipoprotein; LDL, low-density lipoprotein; UKBB, UK Biobank.(XLSX)Click here for additional data file.S2 TableP < 5 × 10−8 not previously detected by the Global Lipids Genetics Consortium [2 < 0.001; P, P-value; SE, standard error of regression coefficient; SNP, single nucleotide polymorphism.Beta, regression coefficient; BP, base position; CHR, chromosome; EAF, effect allele frequency; Gene, nearest gene; GWAS, genome-wide association study; INFO, imputation score; LDL, low-density lipoprotein; Novel signal?, association with nsortium  based on(XLSX)Click here for additional data file.S3 TableP < 5 × 10−8 not previously detected by the Global Lipids Genetics Consortium [2 < 0.001; P, P-value; SE, standard error of regression coefficient; SNP, single nucleotide polymorphism.Beta, regression coefficient; BP, base position; CHR, chromosome; EAF, effect allele frequency; Gene, nearest gene; GWAS, genome-wide association study; INFO, imputation score; Novel signal?, association with nsortium  based on(XLSX)Click here for additional data file.S4 TableP < 5 × 10−8 not previously detected by the Global Lipids Genetics Consortium [2 < 0.001; P, P-value; SE, standard error of regression coefficient; SNP, single nucleotide polymorphism.Beta, regression coefficient; BP, base position; CHR, chromosome; EAF, effect allele frequency; Gene, nearest gene; GWAS, genome-wide association study; INFO, imputation score; Novel signal?, association with nsortium  based on(XLSX)Click here for additional data file.S5 TableP < 5 × 10−8 not previously detected by the Global Lipids Genetics Consortium [2 < 0.001; P, P-value; SE, standard error of regression coefficient; SNP, single nucleotide polymorphism.Beta, regression coefficient; BP, base position; CHR, chromosome; EAF, effect allele frequency; Gene, nearest gene; GWAS, genome-wide association study; HDL, high-density lipoprotein INFO, imputation score; Novel signal?, association with nsortium  based on(XLSX)Click here for additional data file.S6 TableP < 5 × 10−8 not previously detected by the Global Lipids Genetics Consortium [2 < 0.001; P, P-value; SE, standard error of regression coefficient; SNP, single nucleotide polymorphism.Beta, regression coefficient; BP, base position; CHR, chromosome; EAF, effect allele frequency; Gene, nearest gene; GWAS, genome-wide association study; INFO, imputation score; LDL, low-density lipoprotein; Novel signal?, association with nsortium  based on(XLSX)Click here for additional data file.S7 TableHDL, high-density lipoprotein; LDL, low-density lipoprotein; MR, Mendelian randomisation.(XLSX)Click here for additional data file.S8 TableP-value; SE, standard error of the effect estimate; TG, triglycerides.–Beta, effect estimate; LDL, low-density lipoprotein; MR, Mendelian randomisation; nSNP, number of single nucleotide polymorphisms; P, corresponding (XLSX)Click here for additional data file.S9 TableP-value; SE, standard error of the effect estimate.Beta, effect estimate; HDL, high-density lipoprotein; MR, Mendelian randomisation; nSNPs, number of single nucleotide polymorphisms; P, corresponding (XLSX)Click here for additional data file.S10 TableP-value; SE, standard error of the effect estimate; TG, triglycerides.Beta, effect estimate; LDL, low-density lipoprotein; MR, Mendelian randomisation; nSNPs, number of single nucleotide polymorphisms; P, corresponding (XLSX)Click here for additional data file.S11 TableP-value; SE, standard error of the effect estimate.Beta, effect estimate; HDL, high-density lipoprotein; MR, Mendelian randomisation; nSNPs, number of single nucleotide polymorphisms; P, corresponding (XLSX)Click here for additional data file.S12 TableP-value; SE, standard error of the effect estimate.Beta, effect estimate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MR, Mendelian randomisation; nSNPs, number of single nucleotide polymorphisms; P, corresponding (XLSX)Click here for additional data file.S13 TableP-value, SE, standard error of the effect estimate; TG, triglycerides.Beta, effect estimate; HDL, high-density lipoprotein; MR, Mendelian randomisation; nSNPs, number of single nucleotide polymorphisms; P, corresponding (XLSX)Click here for additional data file.S14 TableP-value; SE, standard error.apoB, apolipoprotein B; Beta, beta coefficient; BP, base position; CHR, chromosome; eaf, effect allele frequency; p, (XLSX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Wolbachia bacteria are the most common symbionts of insects. These bacteria are ordinarily transmitted via oocyte cytoplasm from mother to progeny, like mitochondria, and are sporadically transmitted from one species to another. The Wolbachia symbionts have evolved to be parasitic  or/and mutualistic . Here we have studied Wolbachia infection in populations of four-eyed fir bark beetle Polygraphusproximus, which is one of the most dangerous pests of Siberian fir forests. A high rate of the only wProx Wolbachia strain in P.proximus populations was found in a vastly studied territory. Surprisingly, females were more often harboring Wolbachia than males. Besides, a comparison of the Wolbachia density in individuals has revealed that females contain much more Wolbachia symbionts than males. We suppose that the difference in infection status, as well as the difference in Wolbachia load between males and females within a population, can be found in some other Wolbachia–host associations.Wolbachia symbionts are maternally inherited bacteria that are widely distributed among Arthropoda hosts. Wolbachia influence their host biology in diverse ways. They may induce reproductive abnormalities, protect hosts against pathogens and parasites, or benefit hosts through metabolic provisioning. The progeny of an infected female are ordinarily infected with Wolbachia; however, Wolbachia have no future in male host progeny because they cannot transmit the symbiont to the next generation. Here, we analyze native and invasive populations of the four-eyed fir bark beetle (Polygraphus proximus) for Wolbachia prevalence and symbiont genetic diversity. This species is a dangerous pest of Siberian fir (Abies sibirica) forests. The native range of P. proximus includes the territories of the Russian Far East, Japan, Korea, and Northeast China, whereas its invasive range includes West Siberia, with further expansion westward. Surprisingly, we revealed a difference in the patterns of Wolbachia prevalence for males and females. Infection rate and Wolbachia titers were higher in females than in males. ST-533, the only haplotype of Wolbachia supergroup B, was associated with a minimum of three out of the five described mitochondrial haplotypes. Wolbachia genus are the most successfully inherited symbionts that inhabit numerous arthropod and nematode hosts [Wolbachia are vertically transmitted through host generations via oocytes [Wolbachia increase lifespan and fecundity [Wolbachia-harboring males. Therefore, Wolbachia in males commonly have no future and, in the case of CI, only function in controlling the spread of the symbiont in a host population.Bacteria of the de hosts ,2,3. Wol oocytes , and are oocytes ,6,7,8,9.ecundity ,11,12, pecundity ,14,15,16ecundity ,18, and ecundity ,20,21. Tecundity . These pecundity , whereasWolbachia infection in native and invasive populations of the four-eyed fir bark beetle Polygraphus proximus Blandford, 1894 . This species is one of the most dangerous pests of Siberian fir (Abies sibirica) forests. Since 2014, P. proximus has been listed in the European and Mediterranean Plant Protection Organization (EPPO) Alert List (https://gd.eppo.int/taxon/POLGPR). The native range of P. proximus includes the territories of the Russian Far East, Japan, Korea, and Northeast China [We studied st China ,24,25. TWolbachia infections have been reported in Scolytinae species, particularly in the following genera: Coccotrypes, Euwallacea, Hypothenemus, Ips, Pityogenes, Taphrorychus, Xyleborus, and Xylosandrus [Wolbachia isolates belong to supergroup A, with a few belonging to supergroup B.osandrus ,28,29,30Wolbachia in the four-eyed fir bark beetle and determine infection rates of the population, including the infection prevalence according to sex. To do so, we carried out Wolbachia titer by qPCR and genotyped Wolbachia isolates using a multilocus sequence typing (MLST) protocol [Wolbachia symbionts and mitochondria are inherited together, according to the host’s maternal lineage, and can therefore form cytotypes. A cytotype results from the mitotype and the genotype of an infection, meaning cytotypic analysis can reveal the evolutionary history of Wolbachia infection [Here, we first report on protocol . In addinfection ,33,34,35P. proximus were collected in four regions of Russia: Sakhalin province (native range) (N = 60), Tomsk province (N = 271), Udmurtia (N = 38), and Krasnoyarsk Krai (N = 48) (invasive territories) (Adult specimens of itories) . In TomsThe total DNA was individually extracted from whole specimens. The insects were homogenized in 200 µL of DB extraction buffer (10 mM Tris-HCl (pH 8.0), 25 mM EDTA, 0.5% SDS, 0.1 M NaCl) with 1 µL of proteinase K (AppliChem) and incubated at 56 °C for 1 h. Total DNA was purified with NaOAc, precipitated, and dissolved in 100 µL of double-distilled water.COI) gene [Wolbachia infection status was determined by conventional PCR using the W-SpecF/R primer set [Wolbachia-negative samples and controversial samples were checked by nested PCR with ftsZunif [Wolbachia isolate, we amplified five MLST genes [COI and MLST genes) were purified with exonuclease (ExoI) enzyme (New England Biolabs), and sequenced using a BrightDye Terminator Cycle Sequencing Kit (Nimagen). Full MLST profiles of four Wolbachia isolates were deposited in the PubMLST (https://pubmlst.org/wolbachia/) database, and all MLST alleles and fragments of the COI gene were deposited in the GenBank database (All DNA samples were tested by PCR with HCO/LCO primers for the cytochrome c oxidase subunit I (OI) gene . Wolbachimer set  and coxAimer set , and addimer set . WolbachftsZunif  for the ST genes . The ampdatabase .Wolbachia genomes per beetle genome. A single copy gene per Wolbachia genome fbpA, and single copy gene per beetle genome Inx2 were used to estimate the Wolbachia load in DNA samples. For a nuclear reference, the 750 bp region of the Inx2 gene was amplified by nested PCR according to Che et al. [https://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome). The primer sets Inx2prox , and fbpAprox  amplified 143 bp and 188 bp fragments of Inx2 and fbpA loci, respectively. Each qPCR mixture of 20 µL volume included 10 µL of ×2 qPCR HS-Taq mix , 0.3 µM of each primer, and a 1 µL DNA sample that was in the range of 10–40 ng. All qPCRs were performed in duplicate on a CFX 96 Real-Time PCR Detection System . Conditions of the qPCR were optimized by gradient PCR; we used 95 °C for 5 min, followed by 50 cycles of 95 °C for 10 s, and 63 °C for 30 s. Melt curves were generated to ensure specificity of the PCR product (65–95 °C with a step of 0.5 °C). Efficiencies of amplification and R2 were determined using a 5-fold dilution of a DNA sample for both loci .We designed a qPCR assay to measure the relative number of e et al. , and theP. proximus mtDNA, the 620 bp region of the COI gene was used (38 sequences). To identify mtDNA haplogroups, we used the data from Kononov et al. [COI gene and the GTR model for MLST data. For the MLST data analysis, we also used four Wolbachia isolates of Scolytinae species (Seqence Types (STs): 138, 207, 208, 210), seven STs  as Wolbachia supergroup references, and three STs  that had similar haplotype characteristics to ST-533 (Wolbachia haplotypes [Wolbachia load was measured as a relative copy number (fbpA/Inx2) = 2 Cq(Inx2)−Cq(fbpA). Because the efficiencies of both primer sets were close to 100%, the amplification differences were not taken into account in calculations.All statistical calculations were performed using Minitab 17.1.0 software . The sequences were analyzed using FinchTV v. 1.4.0 . The alignments were generated using the MUSCLE algorithm . For phyv et al. . Phylogev et al. , using to ST-533 ; SM1. EaWolbachia prevalence in populations of P. proximus was initially estimated by conventional PCR, followed by symbiont negative DNA samples being rechecked by nested PCR .The  samples . The onlWolbachia prevalence for males and females, with 6 out of 13 samples being partially or totally subdivided based on sex. The proportion of infected females was significantly higher than males: across all collections p < 0.001; in localities, for three populations p < 0.001; for two populations p < 0.05; and for one p = 0.138.Surprisingly, we found a great difference in Wolbachia rates within populations based on the sex of individuals was observed, we tried to reexamine all Wolbachia negative DNA samples by nested PCR. In total, we analyzed 189 samples, some of which were checked for two loci. As a result, 69 new infected samples were found. Previously seemingly Wolbachia-free samples from Tomarinsky-2 appeared to be nearly totally infected (7 out of 8). Taking into account the results based on both conventional and nested PCR, the infection rates varied from 48% to 92%. The infection rates in Udmurtia and Sakhalin were higher than in Tomsk and Krasnoyarsk .Because a significant difference in p < 0.01); however, in terms of localities, significant differences were only found in Tomarinsky-1 (p < 0.05) and Bakcharsky (p < 0.01)  .Wolbachia genomes per beetle genome were measured by real-time quantitative PCR. Here, we used 27 conventional-positive and 28 nested-positive samples across all regions; among them were 13 females, 20 males, and 22 samples with undetermined sex. Variation of the Wolbachia copy number (fbpA/Inx2) was in the range of 2.9 × 10−4 to 1.73. Our results for the conventional and nested PCR were in a good agreement with the estimation of Wolbachia load by qPCR. The threshold of the two PCR approaches was about 0.018–0.020 . In particular, there were no females below the threshold, whereas there were seven males  above it. In two cases, Wolbachia infection was successfully detected by conventional PCR, although the infection titers were ~0.002 and 0.005.The values of 18–0.020 . The difWolbachia isolates (SM-1) from the Tomsk, Krasnoyarsk and Sakhalin regions; all were identical and corresponded to the ST-533 haplotype. Alleles ftsZ-263 and fbpA-461 were first characterized in the PubMLST database; then, we checked them in the GenBank database using the BLASTn tool and confirmed their uniqueness. For this reason, we characterized additional isolates from populations of Udmurtia, Tomsk, and Krasnoyarsk  with regard to the ftsZ and fbpA genes and did not detect any other alleles. The concatenated sequence of ST-533 and each MLST allele was clustered into supergroup B . The Wolbachia haplotype of Pityogenes chalcographus (Scolytinae) was also clustered into supergroup B, with a total of 72 substitutions from ST-533. Wolbachia infection of Leptopilina clavipes (parasitoid wasp) had the same hcpA-148 as ST-533 and ST-482; however, its haplotype (ST-303) was genetically closer to the isolate of P. chalcographus than that of P. proximus. The ftsZ-91 allele of Wolbachia from Hylyphantes graminicola (spider) differed from ftsZ-263 of wProx by only two substitutions, although other alleles were found to have much greater differences.Complete MLST profiles were obtained for four rgroup B . The ST-Wolbachia infection was uniform across a vast territory, we analyzed a 620 bp barcode region of the COI gene to characterize maternal inheritance in more detail. In particular, we aimed to determine the mtDNA haplotype of infected and uninfected P. proximus specimens at all sampling sites. As references, we used data from Kononov et al. [Wolbachia infection status and mtDNA haplogroup.As v et al. , where fWolbachia symbionts in the subfamily Scolytinae have previously been found in 11 species of 8 genera [Wolbachia infection in populations of P. proximus in four distant regions of its native and invasive ranges. The overall infection rate in the studied populations was 68% . Surprisingly, the infection rate of females was higher than in males . Moreover, most of the infected males were characterized by significantly lower Wolbachia titers than the females. Genetic diversity of Wolbachia in P. proximus populations indicated only one strain in the vast studied territory, which is associated with at least three out of five mitochondrial haplogroups.8 genera ,28,29,30Wolbachia implies that the symbiont has no future in males; therefore, it seemed logical that Wolbachia could be absent in males. We have not found other reports of sex-specific Wolbachia distribution in other populations, as is the case with single infected P. proximus. However, Arai et al. [wHm-b strain in males of Homona magnanima (Lepidoptera: Tortricidae) versus a rate of 100% in daughters. Dutton and Sinkins [Aedes albopictus, found wAlbA loss in males. Further, Tortosa et al. [wAlbA strain and demonstrated that wild females were double infected, whereas significant proportions of wild males were only single infected. There is a prediction of reduced expression of cytoplasmic incompatibility (CI) in males through selection of the host genome [Wolbachia loss in males could be considered [Wolbachia maintenance in a host population is based on CI, CI repression due to Wolbachia loss in males should lead to a loss of infection in a whole population. We do not have sufficient information to infer the effect(s) of Wolbachia on P. proximus biology, although the Wolbachia-inducing phenomena that lead to a sex ratio bias should be excluded, because we observed the primary sex ratio to be 1:1. There is a question as to whether Wolbachia in P. proximus is driven by CI and therefore will be lost in future, or high Wolbachia rates maintained by mutualistic effects for females, as, for instance, has been shown for Wolbachia-infected females of the coffee berry borer Hypothenemus hampei (Scolytinae), which produce more eggs than tetracycline-treated females [Maternal transmission of i et al.  reported Sinkins , in douba et al.  confirmet genome , and Wolnsidered . Therefo females .P. proximus males are likely the result of Wolbachia loss in ontogenesis. Most infected males are characterized by low Wolbachia density over all of the studied regions. Similar results were found for the wAlbA strain of Ae. albopictus [Wolbachia titers were found in populations of Pityogenes chalcographus [Wolbachia titers, whereas there are a number of low symbiont titers in other species [The uninfected bopictus ,46. In Sographus ; however species ,53,54,55