P < 5 × 10−8, revealing associations between general neuroticism and anxiety/tension with brain-specific gene co-expression networks. These results indicate that the mitochondrial genome contributes toward neuroticism and the autosomal links between neuroticism and health.Neuroticism is a heritable trait composed of separate facets, each conferring different levels of protection or risk, to health. By examining mitochondrial DNA in 269,506 individuals, we show mitochondrial haplogroups explain 0.07-0.01% of variance in neuroticism and identify five haplogroup and 15 mitochondria-marker associations across a general factor of neuroticism, and two special factors of anxiety/tension, and worry/vulnerability with effect sizes of the same magnitude as autosomal variants. Within-haplogroup genome-wide association studies identified H-haplogroup-specific autosomal effects explaining 1.4% variance of worry/vulnerability. These H-haplogroup-specific autosomal effects show a pleiotropic relationship with cognitive, physical and mental health that differs from that found when assessing autosomal effects across haplogroups. We identify interactions between chromosome 9 regions and mitochondrial haplogroups at  Genetic variation in mitochondrial DNA can influence human traits, but it is not as well studied in association with complex traits. Here, the authors find associations between mitochondrial haplogroups and genetic variation and neuroticism. Individual differences in neuroticism are relatively stable across the adult life course4.Neuroticism is one of the five higher-order factors of personality and is measured using items that refer to irritability, anger, sadness, anxiety, worry, hostility, self-consciousness, and vulnerability6. Neuroticism has also been linked to socioeconomic position, with higher levels of neuroticism associated with lower levels of education and income7. Furthermore, individuals with higher levels of neuroticism are more likely to be diagnosed with psychiatric disorders, suffer from neurodegenerative conditions, such as Alzheimer’s disease and Parkinson’s disease10, have poorer physical health11, and die at a younger age12. In addition to the burden it can place on the individual, neuroticism has been estimated to place an economic burden on society that is greater than substance abuse, mood disorders, or anxiety disorders13 making the identification of its causes a matter of public health.Individuals with a higher level of neuroticism are more likely to experience stress, and to be less happy and satisfied with their lives than those with a lower level14 and mortality15. These paradoxical findings were explained by the observation that, using a bi-factor model, three factors can be extracted from the Short-scale Eysenck Personality Questionnaire-Revised16 neuroticism scale, and that these factors had different effects on health. The first factor, a general factor, reflected a common source of variance across all 12 items. The second and third factors reflected two common sources of variance left over following the extraction of the general factor . These factors—worry/vulnerability and anxiety/tension—are independent of the general factor of neuroticism, and each explained the common remaining variance of a subset of the 12 items. General neuroticism was associated with poorer physical health whereas worry/vulnerability and anxiety/tension are linked to better physical health19. Importantly, all three are associated with worse mental health19.Several studies have however, reported that higher neuroticism can be beneficial to healthse = 0.49%) and the special factors of worry/vulnerability (se = 0.28%), and anxiety/tension (se = 0.32%)19. Large scale genetic analyses performed on these three neuroticism phenotypes has indicated a different underlying genetic architecture. For example, whilst each of the three showed associations with genes whose proteins are expressed in the brain, the general factor of neuroticism was associated with genetic variation in genes linked to neurogenesis and the formation of dendrites, whereas the worry/vulnerability factor was associated with genetic variation in genes linked to high-voltage-gated calcium ion channels and the voltage-gated calcium channel complex. Consistent with the reported phenotypic observations18, genetic correlations between the three factors of neuroticism and health variables also showed differing directions of effect with physical health and mortality. For the general factor of neuroticism negative genetic correlations with self-rated health, and longevity were observed along with positive genetic correlations with coronary artery disease. Whereas, for the two special factors, the direction of these genetic correlations was reversed, consistent with them having a protective effect on health.As with other quantitative traits such as height and intelligence, genetic variation is associated with phenotypic variation in the general factor of neuroticism 19, but so far no investigation has examined the contributions of mitochondrial DNA. However, three lines of evidence suggest that mitochondrial variation may be associated with variation in neuroticism. First, GWAS performed on the general factor of neuroticism and the anxiety/tension and worry vulnerability special factors identified genome-wide significant loci linked with autosomal mitochondrial genes. Second, genome-wide significant loci identified in GWAS of neuroticism contain genes and loci that are involved in the expression of the tissues of the brain. As mitochondria are primarily responsible for the production of the chemical energy required for metabolism, and, due to the high energy requirements of the brain20, this link between neuroticism and the brain may extend to its source of energy. Third, neuroticism is predictive of Alzheimer’s disease21, and mtDNA variation has been shown to be associated with neurodegenerative disorders, including Alzheimer’s disease23 and Parkinson’s disease25. This link between neuroticism and both Parkinson’s disease and Alzheimer’s disease may be in part due to pleiotropic variation in the mitochondrial genome.Large scale genetic analysis have been performed for these factors of neuroticismIn the current study, we used molecular genetic data in 269,506 participants of UK Biobank to examine the contributions made to the general factor of neuroticism and the two special factors of anxiety/tension and worry/vulnerability by mitochondrial DNA. To do so, we first performed an association analysis of mitochondrial haplogroup and single variant analysis on each of the three factors of neuroticism. Second, we examine nuclear encoded mitochondrial genes as well as assessing the role that ‘mitonuclear’ combinations play in the phenotypic variance observed in the neuroticism factors using a test of enrichment. Third, we examine if the association between the neuroticism factors with nuclear DNA varies by mitochondrial haplogroup.Following quality control (QC) . This effect was consistent with the size of the mitochondrial genome when compared to the effects associated with common autosomal SNP variation  where the MT D-loop is located). Marker m.497 C > T defines the K1a haplogroup and has a frequency of 55.72% in K haplogroup samples and 4.81% in the QCed UK Biobank dataset  in R2 = 7.27 × 10−5, P = 0.038). The magnitude of these effects were as predicted by the size of the mitochondrial genome compared to the autosomes  effect was found  in MT-ND5 . This directional consistency confirmed that the effect of m.73 A > G and m.12372 G > A on worry/vulnerability and blood traits was consistent with the broader relationship between these traits. A positive phenotypic and genetic correlation was identified between the general factor of neuroticism and lymphocyte count  consistent with their relationship as predicted by the effect of the K haplogroup  yielded 54 associations from 48 unique genes at P < 0.05/2,099 = 2.38 × 10−5. Forty-four were associated with only one neuroticism trait: 25 with the general factor of neuroticism, 13 with anxiety/tension, and 6 with worry/vulnerability  and GWAScatalog: (short) sleep duration (q = 0.006). Regarding anxiety/tension, three enriched terms were found - GWAScatalog: hypertension (q = 0.04), GWAScatalog: autism spectrum disorder or schizophrenia (q = 0.04) and position: chr6p21 (q = 0.01) (HLA region). No enrichment was found for worry/vulnerability.We annotated the genes that were associated with each of the three neuroticism traits using FUMAq < 0.05  were enriched for each of the three neuroticism phenotypes. No enrichment was found in either the whole set or its annotated subsets at 34 and that specific combinations of mtDNA/nDNA alleles can modulate disease risk36. We hypothesised that nuclear DNA associations with neuroticism may differ by MT haplogroup and examined genome-wide, gene-based, and SNP-based MT-nDNA interactions acting on our three neuroticism phenotypes. The significance threshold for each analysis was set to 0.05/10, 0.05/18,337, and 5 × 10−8, respectively.Cells harbouring different MT haplogroups are known to have functional differences in mtDNA copy number, gene expression, bioenergetics, etc., and are related to immunity and diseases like type 2 diabetesh2 = 0.074, se = 0.005, P = 2.41 × 10−53) and non-H-haplogroup-stratified GWAS  was significantly lower than 1 , suggesting that nuclear DNA contributions to worry/vulnerability differ by MT-haplogroup  or gene-based tests of H-haplogroup specific association  attained statistical significance.MT-haplogroup-stratified GWAS was performed on nuclear SNPs for the ten common MT-haplogroups. Next, using LDSC, we estimated the genetic correlation between each of the ten haplogroups with the remaining nine haplogroups that were meta-analysed (Methods). The genetic correlation between worry/vulnerability in H-haplogroup-stratified GWAS  and a significantly smaller genetic correlation of around zero in the H-haplogroup . However, following adjustment using mtCOJO to control for the overlap with the non-H-haplogroup, the genetic correlation became negative and attained nominal statistical significance . This effect did not withstand correction for multiple comparisons, but the difference was statistically significant (non-H-haplogroup vs adjusted H-haplogroup Pdiff = 0.002).Performing genetic correlation analysis in LDSC, significantly different genetic correlations were found for nine traits Fig.  comparin19, which was conducted in all samples without stratification by mitochondrial haplogroup (Methods). We found one gene showing no association when all haplogroups were included, but that was significantly associated with the general factor of neuroticism when the haplogroup V was analysed separately , and three genes that were less significant in the full set compared to the haplogroup-stratified results  (Supplementary Data PCCB and ETFA are both MT-nDNA candidate genes (Methods) involved in amino acid and fatty acid metabolism. GRIK3 encodes a glutamate receptor that plays a role in synaptic transmission and has been linked to mental health including neuroticism37.Next, we performed a gene-based association analysis using MT-haplogroup-stratified GWAS using MAGMA for each trait. These results were compared with previously published workr2 > 0.4 within 250 kb of lead SNP), five independent nuclear loci  showed a significantly different effect between haplogroups at P < 1 × 10−5 . After clumping . rs181210427, was associated with anxiety/tension in T-haplogroup-stratified nGWAS . Neither of these SNPs was associated with their respective neuroticism trait in either the H haplogroup-stratified GWASs, or when all haplogroups were combined. Furthermore, there was a significantly greater effect of rs72771986 for general neuroticism, and rs181210427 for anxiety/tension compared to the H haplogroup effect  and for all haplogroups combined . Finally, the formal interaction model, for the general neuroticism factor, rs72771986 showed significant evidence of interaction with X haplogroup , and rs181210427 demonstrated a comparable effect for its interaction with the T-haplogroup  for anxiety/tension  on chromosome 9 alongside another 14 candidate markers and 11 mapped genes    from this locus reached 0.8, suggesting that these genes may be under the same co-expression network , although not reaching co-expression threshold of 0.8 , NACC family member 2 (NACC2), and TRAF2 were also negatively correlated with MT gene expression in different brain regions  Fig. , 21. To Ex) Fig. . For MT-0.8 Fig. . We exam0.8 Fig. –41 and fmus Fig. . Transmeons Fig. . Regardions Fig. .Fig. 4GeASTN2) showed the same co-expression pattern as the general neuroticism locus with MT genes in blood and brain. ASTN2 reached co-expression threshold of 0.8 with MT-ATP8, MT-CO1, MT-CO2, MT-CO3, MT-ND4L, and MT-ND5 in a positive direction in blood but did not reach the threshold in brain tissue  have, along with two super UK haplogroup-defining markers m.12372 G > A and m.11467 A > G in MT-ND454, been linked to Alzheimer’s disease,55 which is associated with higher neuroticism.9MT-ND4 shows lower level of transcription in the temporal cortex of Alzheimer’s disease patients compared with controls56. In the current study, two mtDNA SNPs in the MT-ND4 gene---m.11299 T > C (haplogroup K defining marker) and m.11467 A > G (super UK defining marker), although not the lead mtDNA SNPs---were significantly associated with general neuroticism and with worry/vulnerability, respectively, and so may provide a partial explanation for the link between neuroticism and Alzheimer’s disease.Furthermore, the haplogroups we identified as being associated with general neuroticism (K) and with worry/vulnerability . Furthermore, one association, ETFA in the V-haplogroup, was only associated with general neuroticism following stratification by haplogroup. Three of the genes identified by stratifying the GWASs based on haplogroup have evidence as to their biological relevance, both to mitochondria and brain function. Both PCCB and EFTA are related to mitochondrial activity. Where PCCB encodes a protein that is a subunit of the mitochondrial enzyme PCC, EFTA plays a role in the first stages of beta-oxidation of the mitochondrial fatty acids. GRIK3 is linked to the glutamate receptors . Specifically, the product of GRIK3 belongs to the kainite family of glutamate receptors.Gene-based analysis stratified by haplogroup identified 14 genes associated with the factors of neuroticism  and 33 (anxiety/tension) genes, respectively, there was little overlap in the identity of the genes implicated through functional annotation across these two loci, again highlighting heterogeneity between these two factors of neuroticism. Importantly, the nuclear genes identified through functional annotation of the general factor of neuroticism and anxiety/tension interaction loci were found to form brain tissue-specific co-expression networks with protein-coding mitochondrial genes, with negative correlations observed for the brain, and positive correlations for the blood. When examining the general factor of neuroticism, some evidence was found linking negative co-expression between rRNA and tRNA mitochondrial genes with the nDNA genes, the magnitude of these correlations is greater for the protein-coding mitochondrial genes.HV, as well as haplogroup B, F, P, and R. However, the majority of our samples carrying m.73 A > G belong to super haplogroup HV, and so there is a high correlation (r2 = 0.84) between m.73 A > G and super haplogroup HV. By taking into consideration this relationship specific to our data set, we were able to avoid allocating the association of m.73 A > G with worry/vulnerability.Our study highlights the importance of a careful interpretation of MT-GWAS discoveries. In the current study, simulation using genotype and haplogroup allowed us to reduce the number of independent signals, identify the single associated unit (either haplogroup or single variant) most likely responsible for driving the associations with the neuroticism traits, and provided information on the cohort-specific correlation structure in addition to knowledge of phylogenetics. As an example, the homoplasmic (not linked to any haplogroup) marker m.73 A > G is associated with worry/vulnerability but, in our data, this marker is commonly expressed in super haplogroup 57. Depression is strongly phenotypically and genetically associated with general neuroticism, indicating the mtDNA variation may provide a, partial, explanation for the link between depression and blood cell traits.The mtDNA associations with worry/vulnerability and with general neuroticism indicate that mtSNPs and haplogroups have pleiotropic effects where they also associate with blood traits such as RDW, lymphocyte count, and WBC in UK Biobank. Consistent with previous studies, the special factors of neuroticism had a different direction of phenotypic and genetic correlation with blood traits, such as with RDW and WBC, compared to those derived using general neuroticism. Higher levels of RBW and WBC have been linked to a greater severity of depression symptoms59, or specific/rare diseases61 that are underrepresented in UK Biobank, was due to pleiotropic mitochondrial DNA. This limitation arose due to the current practice of GWAS omitting the mitochondrial genome from large meta-analytic consortia. The current paper illustrates that the mitochondrial genome is associated with neuroticism, an important risk factor for psychiatric traits63, at the level of single variant, haplogroup, and through interactions with autosomal regions. The importance of including the mitochondrial genome in future GWAS meta-analyses of psychiatric and health traits is underscored by our finding that the effect sizes for associated mitochondrial variants is of a similar magnitude as autosomal SNPs for the same traits .A limitation of this study is that it was not possible to examine the degree to which the relationships between neuroticism and psychiatric traitsPs = 10−4 to 10−5, we explored whether our haplogroup and MT-GWAS association findings were confounded by these co-segregating loci. We found no evidence that these co-segregating nDNA loci were strongly associated with our neuroticism traits and thus ruled out the possibility of confounding. Guarding against co-segregation of nuclear and mitochondrial variants can be implemented as a quality control step while conducting future MT studies on other traits and diseases. Whether the association between complex traits or diseases and MT-nDNA co-segregation loci has resulted in false discoveries in previous MT studies needs further exploration. In the current study, only common variants were examined for their association with the three neuroticism traits. Future work should also examine the contributions made by rare and heterplasmic variants in the mitochondrial genome. Additionally, the inclusion of samples composed of a broader array of haplogroups---or sequence data---would allow additional sources of genetic variation to be examined for association with neuroticism.In our MT-nDNA co-segregation GWAS, we found multiple nDNA loci co-segregating with different MT haplogroups. Considering that the associations found in MT haplogroup and marker association studies are weak at the magnitude of 19. However, critics of GWAS note that the effect sizes identified for individual variants are low and the heritability captured by array data consists of a subset of the total genetic effect, and that this limits the degree to which GWAS results may benefit human health and wellbeing64. Critics also contend that, GWAS does not necessarily identify causal variants due to factors such as linkage disequilibrium64. However, GWAS have contributed insights into disease biology that have been supported by clinical translation,65 have begun to show their potential in risk stratification at the population level,66 and in identifying targets for pharmacological intervention67. Furthermore GWAS data can be used to examine potentially causal relationships between traits, for example, by Mendelian randomisation68. These contributions have been made using GWAS conducted on autosomes and in some instances the X chromosome69 with mitochondrial DNA being absent from such interrogation. In the current study we perform that initial interrogation to show the general factor of neuroticism and the special factors of anxiety/tension and worry/vulnerability are genetically distinct with different single marker, haplogroup, and nDNA by MT-DNA interactions contributing to trait variation and their links to health outcomes.Large scale GWAS have begun to identify reliably the molecular genetic aetiology of neuroticism phenotypes: traits that are phenotypically and genetically associated with health, financial, and psychiatric differencesThis UK Biobank project (10279) was approved by the National Research Ethics Service Committee North West-Haydock (REC reference: 11/NW/0382). An electronic signed consent was obtained from all participants.70. UK Biobank is composed of 502,655 individuals recruited between 2006 and 2010 and who are 39-73 years of age . These participants were asked to provide detailed information regarding their background, health and lifestyle, as well as being asked to undergo cognitive and physical testing with blood, urine, and saliva samples being stored upon consent. Participants were genotyped for more than 800,000 genome-wide genetic markers using stored blood samples and either the UK BiLEVE Axiom™ or the UK Biobank Axiom™ array. A comprehensive description of the full range of phenotype measurements, the array design, blood sample collection, DNA extraction and the initial data quality control have been published71.Individuals in this study were drawn from the UK Biobank17 and described as follows. Neuroticism was assessed in UK Biobank using a 12 item scale from the Short-scale Eysenck Personality Questionnaire-Revised16. UK Biobank participants were presented with each of the 12 items  on a touchscreen. Participants were instructed to “Work quickly and do not think about the exact meaning of the question.” and were given the option to respond in one of four ways: “Yes” (coded ‘1’), “No” (coded ‘2’), “Do not know” (coded ‘−1’), and “Prefer not to answer” (coded ‘−3’). A total of 100,981 did not answer “Yes” or “No” to all 12 questions, and so were excluded from further analyses, leaving 401,674 participants.A general factor, and two special factors, of neuroticism were studied here. They were derived in the same manner as Gale et al.72. A bi-factor model distributes variance from a set of items into a smaller number of independent factors. Specifically, the variance for items is separated into that which is related to a general factor and that which is related to one or more special factors that are independent of the general factor.To investigate the associations between factors of personality with health and other outcomes it is often best to use a bi-factor model74. A Geomin rotation of a p × k factor loading matrix Λ, where p is the number of items (rows), k refers to the number of factors (columns), and ε is a constant equal to 0.0175, is one in which correlated axes are rotated to minimise the criterion:The general and special factors were derived by applying exploratory structural equation modelling analysis to the 12 items and applying an oblique bi-factor Geomin rotationk−1 factors , so the criterion is minimised when each item loads on only one of these factors.The criterion is therefore at its minimum when every item loads on a single factor. In the case of a bi-factor Geomin rotation, this criterion is only applied to the last 16 are uncorrelated with the general factor. The anxiety/tension special factor primarily explains variance from the items of “Would you call yourself a nervous person?” (λ = 0.60), “Do you suffer from ‘nerves’?” (λ = 0.49), and “Would you call yourself tense or ‘highly strung’?” (λ = 0.35). Worry/vulnerability explains variance from the items of “Do you worry too long after an embarrassing experience?” (λ = 0.57), “Are your feelings easily hurt?” (λ = 0.40), “Are you a worrier?” (λ = 0.31), and “Are you often troubled by feelings of guilt?” (λ = 0.31) . Each participant’s score on each of the three factors was then estimated using the regression scoring method. The correlation between the estimates of the latent score of the general factor of neuroticism with anxiety/tension and worry/vulnerability were r = 0.07, and r = 0.12 (P < 2.220 × 10−16) respectively. The correlation between the estimated scores on the two special factors was r = 0.427 (P < 2.220 × 10−16). Small differences between the correlations derived using the factors within the model and those derived using the participants’ estimates are expected76.Three measures of neuroticism were derived using this method: a general factor of neuroticism that is composed of variance drawn from across each of the 12 items and two special factors of neuroticism that are composed of the variance that remained once the general factor was removed. These special factors are aspects of neuroticism (anxiety/tension and worry/vulnerability), that whilst captured by The Short-scale Eysenck Personality Questionnaire-Revised” λ = 0.3. Worry/vsee Fig. . The cor77. The residuals derived from the linear regression of each of the three neuroticism phenotypes were used as the phenotype in subsequent analyses.Following the extraction of the three factors of neuroticism, the effects of sex, age, genotyping array, batch, assessment centres, and population stratification  were removed from each phenotype at cohort level using linear regression in R v3.6.1An overview of the sample size at QC and used for association analysis can be found in the flowchart in Supplementary Fig. 78. Detailed sample-quality control information is available elsewhere78. In brief, samples who withdrew consent, harbour extreme heterozygosity or missingness on autosomes, have sex chromosome aneuploidies, have mismatched self-reported and genetically inferred sex, or have more than ten putative third degree relatives were removed. Samples with non-British ancestry based on self-report and a principal components analysis and related individuals based on genomic relationship cut-off of 0.025 were also removed.This study was restricted to 332,047 unrelated White British UK Biobank samples that were previously used in an autosomal GWAS project on neuroticism79 and was performed for each subset separately as two genotyping arrays share less than 90% mtDNA content (180 markers on UK BiLEVE Axiom™ and 243 markers on UK Biobank Axiom™ with an overlap of 158). Markers that failed QC in any of the batches (95 batches for UKBB and 11 batches for UKBL), monomorphic markers, extremely rare markers (MAF < 0.0001) and markers with a call rate <0.98 were removed. Samples with missingness > 0.05 were also removed. After QC, 295,120 samples and 205 markers and 36,237 samples and 151 markers were retained in UKBB subset and UKBL subset, respectively. No putative heteroplasmies  were removed, leaving only homoplasmic calls for mtDNA markers71.Based on the genotyping array, the UK Biobank dataset was separated into two groups, named UKBL  for UK BiLEVE Axiom™ and UKBB  for UK Biobank Axiom™. Quality control of the mtDNA genotypes was conducted in PLINKv1.90b6.2080. Second, imputation of mtDNA variants was performed using IMPUTE281 with a window size of 16.6 kb  using 2,504 samples from MT-1000G Phase 382 as a reference panel. Third, hard calls were made from genotype probabilities in PLINKv1.90b6.2079 using default certainty threshold of 0.9. Very few heterozygous calls (less than 0.001%) were produced by imputation due to putative heteroplasmies and these markers were set to missing. Fourth, quality control was applied to the imputed data. Markers with missing rate >0.05 (including those set to missing due to putative heteroplasmies), MAF < 0.01 (including monomorphic markers), MAF difference >0.02 between UKBB and UKBL subsets, MAF difference >0.1 between UKBB + UKBL and 1000 G GBR (91 British samples), call rate <0.95 and imputation info <0.3 were removed. Samples with missingness >0.05 were also removed.Imputation and quality control were performed separately by genotyping array (UKBB and UKBL). Following quality control, genotype data were first transformed from PLINK format into OXFORD format using SHAPEIT2Following QC, 295,120 samples and 162 mtDNA markers (106 genotyped + 56 imputed) passed QC in the UKBB subset. In UKBL, 36,237 samples and 134 mtDNA markers (92 genotyped + 42 imputed) passed final QC. There were 121 markers shared between UKBB and UKBL and 175 different markers in total.77. Afterwards, MT haplogroup was derived using haplogrep284 based on Phylotree build 1785. Haplogroup was set to missing for individuals with a quality score lower than 0.9, resulting in final sample size of 324,488.Post-QC MT genotype data was transformed from PLINK format to hsd format using R v3.6.185, and similar to previous work86, V is a subgroup of HV0a, H is a subgroup of HV, K is a subgroup of U8b, J and T is subgroup of JT. We further clustered phylogenetically linked haplogroups into ‘super’ haplogroups . Super haplogroup is presented in bold to distinguish it from haplogroups.To increase group size for statistical power, haplogroups were clustered into haplogroups A-Z and HV according to the first/two uppercase English letter . Using Phylotree79. Based on prior work using permutation, an alpha level of 0.001 within each of the three neuroticism traits was used to determine the statistical significance of any associations identified86. A total of 175 MT-markers were examined in 269,506 samples with phenotype information . Haplogroup association analysis was performed in R v3.6.177 at cohort level  using the phenotypic residuals and the haplogroups derived from previous steps in 263,883 samples with neuroticism phenotype and having a haplogroup quality score over 0.9. Ten common haplogroups with frequency over 0.01 were examined  using two models.MT-GWAS was performed separately in UKBL and UKBB and subsequently meta-analysed using the default fixed-effect inverse-variance weighted method in PLINK v1.90b6.20The first model examines the effect that deviation from the most common haplogroup, haplogroup H, has on the three neuroticism phenotypes. Haplogroups with frequencies lower than 0.01  were combined into one group termed ‘Other’, resulting in 11 categories. Next, linear regression was performed on the phenotypic residuals fitting the haplogroups as a categorical variable using the most common haplogroup, haplogroup H, as the reference. The significance threshold was set to 0.05/10 as there were 10 comparisons in total.The second model examined the effect of belonging to a specific haplogroup when compared to the rest of the population . Each haplogroup was tested sequentially against the sum of the remaining, where the tested haplogroup was re-coded as 1, and the non-tested haplogroups were pooled into one group and re-coded as 0. The phenotypic residuals were then regressed on the re-coded haplogroups by fitting non-tested haplogroups as the reference. The significance threshold was set to 0.05/10 as there were 10 common haplogroups tested.HV as the reference group in the first model. We considered a super haplogroup, rather than its contributing haplogroups, associated with the phenotype if the P value of super haplogroup analysis was lower than the threshold of 0.005 and lower than any of its contributing haplogroups.Super haplogroup association analysis was conducted as above, using In instances where the associated haplogroup and the associated mtDNA SNP are incompletely correlated with each other  simulations were performed to investigate the number of independent associations and the associated unit (mtDNA SNP or haplogroup) that most likely drove the signal (Supplementary Methods). To ensure that any associated MT-GWAS variant or any haplogroup associations with the three factors of neuroticism were not capturing nuclear DNA variation, co-segregation analyses were conducted (Supplementary Methods).88 using 1000 G European reference panel. A partitioned heritability model with 23 binary annotations was created to capture the total heritability and to estimate the proportion of total heritability captured by each chromosome. The per-chromosome heritability was calculated as the proportion of total heritability explained by each chromosome times the total heritability estimated in a non-partitioned model implemented in Hill et al. (2020)19.Variance in each of the three neuroticism factors explained by MT-haplogroups was estimated using ANOVA by comparing the full model  and the reduced model . This is likely to be an underestimation of the total effect of the mitochondrial genome as subgroups and homoplasmic SNPs can provide additional genetic information and coverage of the mitochondrial genome, even with imputation is sparse, however, it does provide a lower level estimate. To provide a comparison with the heritability captured by autosomal SNPs, per-chromosome heritability was estimated in SLDSC26 to examine the pleiotropic effects of neuroticism-associated mtDNA with 896 traits in UK Biobank. These traits including 594 non-cancer illness from hospital records in ICD10, 166 self-reported illness, 11 binary traits, and 125 quantitative traits, classified into nine categories, which are anthropometric, cardiovascular, endocrine/diabetes, fitness/longevity, gastrointestinal, haematology/dermatology/immune, infection, musculoskeletal/trauma, neurology/eye/psychiatry, renal/urology, reproductive, respiratory/ear-nose-throat, and others.A PheWAS scan was performed using the data generated by Yonova-Doing et al.89 that were available in Yonova-Doing, et al.26 Haplogroup-trait associations were defined as the strongest SNP-trait association for that haplogroup. Multiple testing was conducted across number of markers and traits and the significant threshold was set to FDR < 0.05.For the lead mtDNA SNPs that were associated with neuroticism, a look-up was performed where, for mtDNA haplogroup associations, look-ups were performed for all haplogroup-defining markers on the phylogenetic tree90 using pre-calculated LD scores in 1000 G EUR samples.Where mtDNA pleiotropic associations were found for the neuroticism traits, phenotypic and genetic correlation were estimated. The phenotypic correlation was estimated in R using Pearson correlation, whereas the genetic correlation was estimated in LDSC71. We extracted the imputed data for our initial 332,047 unrelated white British samples and performed chromosome-wise quality control in PLINK279 by removing markers with INFO < 0.3, MAF < 0.0005, call rate <0.95, Hardy-Weinberg equilibrium P value < 10−50 and removing samples with genotyping missingness >0.1. Multi-allelic markers in the remaining data were also removed. A total of 19,651,021 markers and 332,042 samples passed QC.Imputation of nuclear DNA markers was performed by UK Biobank79. This is to ensure the expected minimum frequency of interaction to be over 0.0001 (nDNA MAF 0.01 × MT MAF 0.01). We then tested whether the effect of the same nuclear allele varies between haplogroups as an indication of MT-nDNA interaction by comparing the beta estimates of the same nuclear marker between two stratified GWASs. We used haplogroup H as a reference for all comparisons. Z-statistics for testing the difference in SNP beta estimates was R v3.6.177 as follows,P value was lower than 1 × 10−5, and the association P value was lower than 5 × 10−8 in any one of the two GWASs under comparison.To examine if the effect of autosomal variation on the three neuroticism phenotypes differed by mitochondrial haplogroup, an interaction analysis was conducted. To reduce the statistical burden, we first ran haplogroup-stratified GWAS of nuclear DNA markers within each of the ten common MT-haplogroups . Haplogroup-stratified GWASs  were conducted on the three neuroticism traits using phenotypic residuals and 8,912,253 post-QC nDNA imputed markers with MAF > 0.01 in 270,055 samples with neuroticism phenotype in PLINK279 in the whole UK Biobank data set (Supplementary Methods). To ensure that any interactions identified were not better explained as a main effect of nuclear DNA captured by haplogroup, co-segregation analysis was performed (Supplementary Methods).For those nuclear loci showing evidence of differing association with the neuroticism traits between haplogroups, formal MT-nDNA interaction model  was conducted using PLINK291 and Mito Carta 3.092. Both datasets were mapped to GRCh37 using ENSEMBL (version 102)93 to link the two databases and take account of the difference in gene IDs and symbols due to different genome build. In instances where no match was obtained, mapping of IMPI data was based on its reported ENSEMBL gene ID or gene symbol, whereas mapping of MitoCarta data was based on its reported gene symbol or WikiGene ID. This resulted in 2,220 MT-nDNA candidate genes, 1085 of which were reported in both data base, 1,084 were reported in IMPI exclusively and the remaining 51 were reported in Mito Carta exclusively 19 where traditional GWAS method was applied without stratifying on haplogroups. MT-nDNA candidate genes associated with any neuroticism traits at P < 0.05/2,099 = 2.38 × 10−5 were reported. As for haplogroup-stratified nGWAS and co-segregation GWAS, 18,337 autosomal protein-coding genes available in the GWAS summary data were examined, resulting in a significance threshold of 0.05/18,337 = 2.73 × 10−6. Note, while comparing gene-based results from haplogroup-stratified nGWAS with that from the full GWAS, we restricted the input SNPs to those shared between two GWASs, keeping the same number of SNPs for each gene to maintain the same statistical power. Afterwards, we looked at the gene-based associations from the full GWAS for significant associations identified in the haplogroup-stratified for comparison.Regarding gene-based analysis for MT-nDNA candidate genes, 2,099 autosomal MT-nDNA candidate genes were extracted and tested using the full GWAS91. The MitoCarta gene set was divided into 14 overlapping categories according to mouse tissue-specific mitochondrial protein localisation 92. For neuroticism traits, we also tested 149 gene-sets of mitochondrial pathways from MitoCarta. In gene-set analysis, all protein-coding genes were fitted as background genes, in addition we examined the enrichment of the MitoCarta gene set following the removal of all potential mt-nDNA genes in the control gene set in order to guard against confounding by including nuclear mitochondrial genes in the control set. As translocations of mtDNA to the nuclear genome are relatively common82, we additionally blasted95 the entire MT-genome to the nuclear genome and extracted all nDNA genes which overlapped with those MT-nDNA aligned regions. This gene-set was tested for MT-nDNA co-segregation. We applied multiple testing correction within trait and reported gene-sets with an FDR < 0.05.For gene-set analysis, we first tested whether there was enrichment of neuroticism-associated genes in the whole set of 2,099 MT-nDNA candidates. The IMPI dataset was further categorised into three non-overlapping categories: genes known to encode mitochondrial proteins, protein-coding genes predicted to physically interact with mitochondria , and protein-coding genes associated with or having an ancillary effect on mitochondria90 using pre-calculated LD scores in 1000 G EUR samples. Genetic correlations were derived between each GWAS stratified by haplogroup  with the meta-analysed GWASs conducted on each of the other haplogroups . Evidence of a different nuclear polygenic signal by MT haplogroup was indicated by a genetic correlation that was significantly lower than 1. In these instances mtCOJO analysis96, performed in GCTA (version v1.92.1)97, was used to remove overlapping genetic factors, leaving the nuclear polygenic signal associated with the neuroticism phenotypes that is specific to any one mitochondrial haplogroup. mtCOJO was run using pre-calculated LD scores in 1000 G EUR samples, assuming the shared genetic components were mainly due to horizontal pleiotropy (Supplementary Method). In addition, genetic correlations were performed on the haplogroup-stratified GWAS examining traits previously found to be associated with worry/vulnerability19 28 using the SNP2GENE function. Briefly, we uploaded summary statistics and pre-defined lead SNPs  into the FUMA web interface. FUMA searched for other SNPs in 1000 G Phase3 EUR82 database within a 250 kb window of and in LD (r2 > 0.6) with our lead SNPs. Genes were mapped to the genomic risk locus by three methods: positional mapping (genes within 10 kb window of the locus based on ANNOVAR98); cis-eQTL mapping (genes within 1 mb window of the locus of which the expression level is mediated by any of the candidate SNPs at FDR < 0.05 in any tissue or cell type in the following database49); and 3D chromatin interaction mapping (genes with promoters located in regions that interact with the genomic risk locus at FDR < 1 × 10−6 in any tissue or cell type in the following databases52).For MT-haplogroup-stratified nDNA GWAS, determination of genomic risk locus, gene-mapping and annotation were performed in FUMA (version v1.3.6a)99, regulome database (RDB) scores100, and genes were annotated for the probability of being loss-of-function intolerant (pLI)101 and non-coding residual variation intolerance scores (ncRVIS)102.In addition to ANNOVAR, eQTL, and chromatin status, SNPs were annotated for combined annotation dependent depletion (CADD) scores28 using the GENE2FUNC function, by comparing our gene lists to all functional-annotated gene-sets in the following database105. All 2,220 MT-nDNA candidate genes were set as background genes to remove enrichment due to MT-related functions and pathways. Gene sets with an FDR < 0.05 were reported.For MT-nDNA candidate genes associated with our neuroticism traits, we performed functional annotation enrichment analysis in FUMA53.Protein-protein interactions between 44 mapped nDNA genes and 13 MT protein-coding genes were examined in STRING77 using GTEx v8 TPM data38. Within each tissue with sample size over 50, we calculated the Pearson correlation of gene TPM between each pair of 44 mapped nDNA genes and 37 MT-genes. The significance threshold was set to 0.05/56,200 as there are 56,200 genes in total. We additionally applied the same analysis for all brain sub-regions.Gene co-expression analysis was performed in R v3.6.1To explore if co-segregation between MT-haplogroup and nuclear DNA was driving the association between haplogroup identity and the three neuroticism phenotypes, we performed nDNA GWAS of MT haplogroups. We did not find evidence that our MT associations were driven by nuclear DNA. Details of co-segregation GWAS are in Supplementary Note, Supplementary Figs. Power of co-segregation GWAS and MT-nDNA interaction GWAS was derived and validated using simulation. Details of power calculation are in Supplementary Method, Supplementary Figs. Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary Data 19Supplementary Data 20Supplementary Data 21Supplementary Data 22Supplementary Data 23Supplementary Data 24Supplementary Data 25Supplementary Data 26Supplementary Data 27Reporting Summary


Wheat (Triticum aestivum L.) is a staple food and major source of dietary calories in Pakistan. Improving wheat varieties with higher grain yield and disease resistance is a prime objective. The knowledge of genetic behaviour of germplasm is key. To achieve this objective, elite wheat varieties were crossed in 4 by 3, line × tester design, and tested in 2019 in a triplicate yield trial to estimate genetic variance, general and specific combining ability, mid-parent heterosis and stripe rust (Puccinia striiformis L.). High grain 3358 kg·ha–1 was recorded in F1 hybrid (ZRG-79 × PAK-13). Analysis of variance (ANOVA) revealed significant genotypic variance in grain yield. Broad sense heritability (H2) was recorded in the range of 28 to 100 %. General combining ability (GCA) significant for grain yield in parents except FSD-08 and PS-05 was recorded, while specific combining ability (SCA) was recorded to be highly significant for grain yield only in two crosses (ZRG-79 × NR-09 and ZRG-79 × PAK-13). Mid-parent heterosis was estimated in the range of –28 to 62.6 %. Cross combinations ZRG-79 × PAK-13 depicted highly significant mid-parent heterosis (62.6 %). Highly significant correlation was observed among spike length, spikelets per spike, plant height and 1000-grain weight. Rust resistance index was recorded in the range of 0 to 8.5. These findings suggest exploitation of GCA for higher grain yield is important due to the presence of additive gene action and selection in the filial generations will be effective with improved rust resistance, while cross combinations ZRG-79 × PAK-13 high GCA are best suited for hybrid development. Wheat (Triticum aestivum L.) is an important cereal cropworldwide playing a crucial role in the daily dietary and nutritionalrequirement not only for human beings but also foranimals. It is the major food for one third of world populationand its chief use is the flour for making bread. It is grownaround all continents. Increasing human population, climatechange and global pandemics have an overwhelming impacton food security, especially wheat on crop with current inadequategenetic improvement of wheat to meet future demand.In Pakistan, wheat is grown in an area of 9.2 million ha withthe production of around 25.5 m tonnes and hardly meets the total requirement of the country. But thisfigure is continuously under fluctuation because of stagnantyield of cultivars, disease impact, drought, and floods. Apartfrom these factors, injudicious selection of parental selectionfor a breeding program without prior knowledge of geneticbehaviour in germplasm and lack of indigenous breedingprograms for genetic improvement of wheat is another constraintin the yield.Genetic recombination in germplasm by hybridization isa robust conventional breeding tool for obtaining transgressivesegregants and genetic variation, which provides meansof selection of ideotypes. Gene action and combining abilityanalysis are a most reliable biometric procedure for the studyof genetic behaviour of yield and yield-related components. General combining ability is the averageperformance of genotypes in a series of cross combinations,while specific combining ability is the performance of a particulargenotype in a specific cross combination. Mode ofselection depends based on genetic action in traits of interest.In self-pollinated crops, especially in wheat, plant breedersare usually interested in selection of segregants having additivegene action with high specific combining ability. Additivegene action boosts yield and yield components by cumulativeaddition of genes. Dominance genetic variance exploits heterosisin cross combinations and specific combining abilityprovides the presence of dominant or non-additive gene actionin a particular trait  and provides optimalparental identification . Equalmagnitude of both general and specific combining ability ina breeding population means preponderance of both additiveand dominant genes for the traits of interest; selection in thiscase is most effective for variety development . The term combining ability was first introduced andfurther refined as general combining ability (GCA) and specificcombining ability (SCA) by Sprague and Tatum (1942).GCA distinguishes between the mean performances of parentsin cross combinations whiles SCA is the deviation ofindividual crosses from the average performance of the parentsinvolved. GCA and SCA represent the additive and nonadditiveportions of genotypic variance respectively . The estimates from GCA and SCA provide anassessment of relative merits of the individual genotypes incross combinations to guide selection and testing schemes.Thus, line × tester analysis is among the genetic-statisticalapproaches developed to assist in selection of parents based ontheir combining ability and the potential to produce promisingsegregating populations . According toGCA and SCA impacts, positive values are desirable for mostcrop plants characteristics, such as growth and yield-relatedattributes. Negative GCA and SCA impacts, on the otherhand, are desirable for characters where minimum values areessential and appealing, such as early flowering.Heterosis is a phenomenon where F1 hybrids are superiorin traits as compared to their parental genotypes. There areseveral theories that explain the genetic basis of heterosis,including over-dominance, dominance, and genetic balance.The over-dominance theory of heterosis, first proposed byShull and East (1908), suggests that heterozygous individuals,since they carry two different alleles, have an advantage overhomozygous individuals as they carry two identical alleles fora particular gene. This advantage is thought to mean that thetwo different alleles can supplement with each other, leadingto a vigorous phenotype in F1 hybrids. The dominance theory,presented by Jones (1917), suggests that hybrid vigour iscaused by dominant alleles that are more valuable than therecessive alleles. According to this theory, F1 hybrids accedetwo copies of the dominant allele, resulting in a vigorousphenotype. The third heterosis theory is the “Lerner’s geneticbalance theory”, suggested by Lerner (1954), that describesthat heterosis is the result of a balance between the expressionof genes that promote growth and those that hamper growth.In F1 hybrids, the expression of growth-promoting genes isincreased, whereas the expression of growth-retarding genesis decreased, leading to better growth and development.Heterotic studies for increasing wheat grain yield have beenan interest of early wheat researchers. Mid-parent heterosisis the percent of the increase or decrease in the F1 value ascompared to the average value of both parents for any metrictrait. In the early green revolution era Pal and Alam (1938)reported mid-parent heterosis (MPH) in wheat. After thegreen revolution and introduction of semi-dwarf wheat varieties,various wheat researchers reported MPH heterosisin wheat . Barbosa-Neto et al. (1996) reported MPH in soft redwinter wheat in the range of –20 to 57 %. Liu et al. (1999),Dreisigacker et al. (2005), Basnet et al. (2019) reported MPHin CIMMYT wheat varieties in the range of 9.5 to 14 %.Wheat crop faces numerous challenges that cause yieldlosses, including stripe rust (Puccinia striiformis f. sp. tritici), which is a major disease in areas where cool to mild warmtemperature prevails during the months of February and Marchin the wheat-growing season. Under conducive environmentalconditions, disease causes yield losses ranging from 10 to70 % depending upon susceptibility of genotypes . Development of cultivars containing genetic resistanceis the most cost-effective and environmentally friendlystrategy to mitigate yield losses by stripe rust . Stripe rust spores continue to mutate and evolve newvirulent races causing damage to previously resistant cultivars. Wheat crop in Pakistan has faced severedamage caused by stripe rust pathogen in recent years . Due to climate change and rapid mutation instripe rust pathogen, new races overwintering on alternativehost barberry in hilly areas at high altitudes evolve . Under these circumstances, the already resistantgenotypes become susceptible .There are two types of resistance mechanism against rustpathogens in wheat, vertical resistance, and horizontal resistance.Vertical resistance is conferred by a single gene toa specific pathogenic race of rust, while horizontal resistanceinvolves the use of multiple genes that provide broad spectrumdisease resistance against multiple pathogenic races of rust.There are several resistance genes present in the Pakistanibread wheat varieties that confer resistance against yellow rust,which include Yr5, Yr10, Yr15, Yr17, and Yr18. Qamar et al.(2014) reported Lr34/Yr18 gene complex that confers broadspectrum resistance against yellow rust and leaf rust in mostof Pakistani wheat varieties. Intikhab et al. (2021) reportedthe presence of Lr46/Yr29 gene complex in Punjab-2011 andPirsabak-2005 cultivars that confer resistance against striperust. Khan S.N. et al. (2022) reported the presence of Yr17 andYr5 gene complex in Pakistani wheat varieties Punjab-2011and Pirsabak-2005. Utilization of these resistance sources inthe breeding program for development of varieties resistantagainst stripe rust is an ultimate objective to ensure high yieldon sustainable basis.Various biometrical techniques and breeding designs areused for genetic evaluation and genetic behaviour of germplasmto be utilized in crop breeding programs, but line × testeranalysis is an efficient mating design providing reliable informationabout GCA and SCA that ultimately depicts the modeof gene action in a particular trait . GCAand SCA are important to apprehend the genetic architectureof quantitative traits and create the road map for initiation ofan efficient breeding program .Several studies investigating the GCA and SCA effectshave been conducted in wheat. Zhao et al. (2013) reportedsignificant effects for both GCA and SCA for yield and itscomponents and inferred that selecting parental genotypeswith high GCA and SCA effects could lead to the developmentof high-yielding wheat hybrids. Similarly, researchers assessedthe GCA and SCA effects in spring wheat and durum wheatF1 hybrids by using line × tester model for combining abilityestimate and concluded that GCA effects were more importantthan SCA effects for grain yield and yield-related traits, andselection of parental genotypes with high GCA effects couldincrease the prospective yield of wheat hybrids . They found thatboth GCA and SCA effects were significant for grain yieldand its components and suggested that selecting parents withhigh GCA and SCA effects could lead to the development ofhigh-yielding wheat hybrids. Selecting parents with high GCAand SCA effects can improve the yield potential and diseaseresistance of wheat hybrids, and the use of line × tester designscan provide valuable information about the genetic effects ofparents and their hybridsThe objectives of this study is to elucidate the general andspecific combing ability, heterotic potential, and stripe rust(Puccinia striiformis f. sp. tritici) resistance behaviour ofindigenous elite wheat varieties and their breeding population.Experimental site and plant material. The research was carriedout at the experimental site of a wheat research program,National Agricultural Research Center, Islamabad Pakistanduring 2017–2018 wheat growing season. The soil type of thesite is clay loam from 0 to 20 cm, and at the 20–40 cm depthit is moderate clay loam. Five widely adopted approved wheatvarieties were used as lines  and three widelyadopted, registered and approved varieties for rainfed areasof Pakistan were used as a tester, namely, NARC-2009,Pakistan-2013 and Borlaug-2016 (Table 1). These testers arewidely adopted and due to their ability to withstand rainfed anddrought-prone areas of Pakistan their leaves have the abilityto stay green during high terminal heat and drought stress.Field experiment and crossing scheme. Eight parents werehybridized to produced 15 F1 cross combinations accordingto line × tester crossing fashion as described by Kempthorne(1957) during 2017–2018 wheat growing season and crossingwas conducted during March 2018. 15 cross combinationsand seven parents were planted in Randomized CompleteBlock Design (RCBD) with three replications during 2018–2019 wheat growing season. In every replication, parents andF1 hybrids were sown in 1 m length with row-to-row spacing25 cm and plant-to-plant spacing 15 cm. The experimentwas conducted in an irrigated field and a total of 6 irrigationswere applied after sowing to harvesting time. Recommendeddoses of fertilizers, i. e. 120 kg N · ha–1 and 80 kg P · ha–1, wereapplied. Half of the fertilizers were used at the time of soilpreparation, the second half was applied at the time of tillering,and weedicides were used for eradication of broad leaves and narrow leavesweeds respectively according to the doses mentioned by themanufacturer. Herbicide was applied before the jointing stageof the crop. Leaf area was measured when leaves were fullyturgid and green.Data collection. Grain yield and some yield-related parameterswere measured in parents and hybrid combinations.Grain yield per plant was measured in grams and 1000-grainweight was measured after counting 500 grains of each wheatgrain sample on a counting tray once and the second samplewas repeated for the other 500 grains.Canopy temperature was measured by using a portablethermal gun . Readingsfor canopy temperature were taken at three Feeks stages like booting, kernel water ripening and grainmilking stages . All readings were taken at the angle of 30° and above 50 cm of the crop canopy,avoiding land temperature by pointing thermal gum only atthe canopy. The observations were taken between 11:00 amand 14:00 pm under stagnant air conditions and clear sky asdescribed by . Observations for Normalizeddifference vegetative index (NDVI) were recorded 50 cmabove the canopy by using a hand-held Green Seeker with anoptical sensor unit  at three stages ofbooting and grain filling between 11:00 hours to 14:00 hourswith clear sky . Values of NDVI rangefrom –1  to +1 (the strongestgreen vegetative stage) .Statistical analysis. Data for other traits  were recorded from6 randomly selected plants. Data recorded were arranged inmean data and subjected to Analysis of Variance (ANOVA)accordingto Steel and Torrie (1980) and Line × Tester analysis,according to Kempthorne (1957), combining ability andgene action were studied  byusing R Package agricolae . Genotypic variance and phenotypicvariance were estimated as mentioned by Almutairi (2022) inMS Excel 2016, by using the following formula:Environmental variance was estimated according to Comstockand Robinson (1952). Broad sense heritability was calculatedby using the following formula as described by Burtonand Devane (1953):Heterosis was estimated in percentage increase or decreaseof the F1 hybrids value over mid-parental value by followingthe formula as described by Fonseca and Patterson (1968):Disease observations and scoring. Observations for striperust were recorded at the time of appearance of disease anddata were recorded when rust pathogen was fully developedon leaves of a susceptible check cultivar and leaves’ surfacewas fully covered with rust’s spores. Disease observation wasrecorded in three replicates of each parental line and F1 hybridsaccording to the Cobb Scale method as described byPeterson et al. (1948). The severity of disease was expressedas the percentage of leaf area covered, and 0 % score wasgiven when there was no infection on the leaf and 100 %score was considered when the leaf area was fully coveredwith rust spores and infection. Readings of percent severitywere recorded with the following descriptions for scoringand response values: , response values, coefficient of infection(CI), average coefficient of infection (ACI), country averagerelative percentage attack (CARPA) and rust resistanceindex (RRI) according to Akhtar et al. (2002). The followingformula was used for the calculation of RRI:RRI was calculated by considering the scale of 0 to 9 fromCARPA, where 0 represents a most susceptible genotypeand 9 represents a highly resistant response of the genotypeto rust pathogen.Analysis of variance (ANOVA)Analysis of variance (ANOVA) results presented in Table 1show that the lines  had statistically significant differencesfor all the trails. The testers  showed statisticaldifferences for days to heading, plant height, peduncle length,spike length, days to maturity, grains per spike, 1000-grainweight and grain yield, while non-significant results forflag leaf area, tillers per plant and spikelets per spike wereshown. Interaction of line × tester was significant in case ofplant height, flag leaf area, peduncle length, days to maturity,grains per spike and 1000-grain weight. Parents  used in this study provided a broad range of expressionfor various characters as shown in Table 2. There weresignificant differences ( p ≤ 0.05) among the means of genotypes(Table 3) for days to heading (DH), highly significant( p ≤ 0.01) for plant height (PH), flag leaf area (FLA), tillers perplant (TPP), peduncle length (PL), spikelets per spike (SPS),days to maturity (DM), grains per spike (GPS), thousand grainweight (TGW), grain yield (GY) and NDVI value.The values for days to heading (DH) were maximum in thetester  PAK-13 (119 days) and minimum in the lines PB-11, PS-05 and MRJ-08 (117 days). DH are thekey indicator of earliness in crop production. Plant breedersare keen to create new varieties of wheat genotypes with earlymaturity. So, early heading is a desirable trait. Delayed headingleads to a reduction in yield  and earlyheading increases the grain filling duration, which ultimatelyresults in high yield . Plant height (PH)was the highest in the female parent FSD-08 (103 cm) andthe lowest in the male parent NR-09 (83 cm). Minimum PHis preferred due to expected lodging losses. Similarly, thetester NR-09 (83 cm) can be assessed for developing droughttolerant variety with reduced plant height for future breedingprograms. Likewise, minimum flag leaf area (FLA) is alsodesirable for drought tolerance due to reduced transpirationlosses from a reduced area exposed to sunlight. The testers PAK-13 and BOR-16 showed the minimum values offlag leaf area: 29.57 and 29.83 cm2, respectively. Pedunclelength was longest in the female parent PS-05 (16.67 cm) andshortest in the male parent NR-09 (7.20 cm). PS-05 producedthe maximum grain yield (2531.8 kg · ha–1) while minimumgrain yield (1696.1 kg · ha–1) was recorded in ZRG-79.Mean performance of parents and their cross combinationsMean performance for line, testers and cross combinationsfor days to maturity (DH) ranged from 117 to 119 days. Theparental lines FSD-08, PB-11, PS-05, MRJ-08, ZRG-79 andBOR-16 were revealed to have 117 DH, while NR-09 andPAK-13 had 118 and 119 days to heading, respectively (seeTable 2). Among F1 hybrids Zargoon-79 × Pakistan-2013 had119 days for heading while the rest of the cross combinationsshowed 117 DH. The grand means for parents, crosses, linesand testers were 117.67, 117.56, 117.27 and 118.33, respectively.The coefficient of variance 0.67 % obtained for DHwas also in the acceptable range.Average minimum plant height was recorded in NARC-2009 (83 cm) followed by cross combination of Punjab-2011 ×Pakistan-2013 (86 cm), and maximum plant height of 104 cmwas recorded in the cross-combination Faisalabad-2008 ×Borlaug-2016 followed by one of parent viz. Faisalabad-2008(103 cm). Grand mean, coefficient of variance (CV) and leastsignificant variance (LSD) for plant height of lines, testers andtheir parental combinations was revealed to be 95.92 cm, 3.64and 5.73, respectively.The cross-combination Punjab-2011 × Pakistan-2013showed minimum value (26.8 cm2) for flag leaf area followedby the lines Pakistan-2013 (29.5 cm2) and Borlaug-2016(29.8 cm2). Maximum leaf area was recorded in the linePunjab-2011 (39.2 cm2) followed by the F1 combination,Faisalabad-2008 × Pakistan-2013 (36.9 cm2). Grand mean,CV, LSD and standard error for leaf area of lines, testers andcross combinations was recorded as 10.46, 5.7 and 2.0 cm2respectively.Maximum 13 tillers per plant (TPP) was recorded in thetester Pakistan-2013 followed by 12.3 tillers in Borlaug-2016while minimum 7.6 tillers were observed in the female parentPunjab-2011 followed by Pirsabak-2005 (8.33). In thecross combinations a maximum of 10 tillers was recorded inPunjab-2011 × NARC-2009 and Miraj-2008 × NARC-2009and grand mean for TTP was recorded as 9.49 with CV, LSDand SE 11.66, 3.61 and 1.27 respectively.Maximum peduncle length was observed in the line Pirsabak-2005 while minimum peduncle length was recordedin the tester parent NARC-2009. Grand mean for pedunclelength was observed to be 11.96 cm with CV 9.7 % and LSD(α 0.05) value 1.9.Mean performance for spike length (SL) was observed12.36 cm in parents and their cross combinations. MaximumSL was observed in cross combinations Miraj-2008 ×Pakistan-2013 followed by Miraj-2008 × Borlaug-2016, butminimum SL was observed in the parental line Faisalabad-2008.Grand mean for spikelets per spike (SPS) for parents andcross combination was recorded as 20.33 with a maximumof 21.9 spikelets observed in Pakistan-2013 in addition to theF1 hybrid combination of Miraj-2008 × Pakistan-2013 and inthe Pirsabak-2005 × Pakistan-2013. Grains per spike (GPS)was recorded maximum in parental line Punjab-2011. Averagethousand grain weight was calculated to be 34.52 withhigher TGW in Faisalabad-2008 (43.73 g) and the crosscombination of Faisalabad-2008 × Pakistan-2013 (43.53 g),and lower value for TGW was depicted by the parental lineNARC-2009 (22.47 g). Average grain yield was obtainedin all the parents and cross combinations (2344.5 kg · ha–1),while average GY was higher in crosses as compared to theparents’ grain yield, the maximum was recorded in the crosscombination ZRG- 79 × PAK-13 (3358 kg · ha–1), followed byPB-11 × NR-09 (2820 kg · ha–1), while minimum grain yieldwas recorded in the cross combination of ZRG-79 × NR-09(1372 kg · ha–1).Maximum normalized differences in vegetative index(NDVI) value was observed in the parental line PS-05 (0.73)followed by PB-11 × PAK-13 and PS-05 × NR-09 with thesame value. Average NDVI value for parents and crosses wasrevealed to be 0.67; lines, testers and crosses also containedsimilar values for NDVI.There were significant differences among the means ofcrosses combinations for almost all the traits studies exceptDH, TPP an SL. The lines  also depictedhighly significant differences in all the parameters under considerationexcept SL. The testers  also revealedhighly significant differences for all the traits except for FLA,SL, SPS, and NDVI. Interaction of lines × testers depictedhighly significant differences in their mean performance forthe traits of PH, FLA, PL, DM, GPS, TGW and NDVI value.Estimates of genetic variance componentsEstimation of genotypic variance, phenotypic variance, environmentalvariance, variance due to general combining ability,variance due to specific combining ability and variance dueto GCA over SCA is mentioned in Table 4.Phenotypic variance was depicted more as compared togenotypic variance in some traits, i. e. PH, FLA, SL, DM,and GY, while only GY showed high environmental variance.Broad sense heritability (H2) was estimated in the rangeof 28.11 % (GY) to 100 % (CT). PH, PL, GPS, TGW, NDVIvalue and CT showed broad sense heritability of more than91 %, while DH and GY depicted less heritability. The traitswith high genetic variance, low environmental variance andhigh broad sense heritability have preponderance of additivegenes and these are stable characters and selection in the filialgenerations can be made by keeping eye on these traits. Grainyield (GY) and DH attained low broad sense heritability andshowed that environmental influence is more important forthe expression of these traits. Selection in the filial generationshould be made for these traits by considering diseaseincidence and drought proxy parameters, i. e. NDVI and CTvaluesProportional contribution of lines, testers,and their interactions to total varianceProportional contribution of total variance for yield andyield-related metric traits for lines, testers and their crosscombinations was estimated . For DH, PH, TPP andCT it was recorded to be higher as compared to the testersand combinations of both lines and testers. Contribution ofL×T to total variance was recorded as high in FLA, PL, SL,SPS, DM, GPS and NDVI value, while variance contributionof testers to TGW and GY was estimated higher as comparedto the lines and L×T combinations.General combining abilityGeneral combining ability (GCA) estimates for all the traitsare given in Table 5. Both positive and negative GCA effectswere observed for lines and testers. For DH, the value of GCAeffects ranged between 0.00 and 0.56. As a good general combiner,significant positive (0.56) and negative (–0.56) GCAeffectswere observed for lines Zargoon-79 and Pirsabak-2005,respectively. Similarly, in the testers, positive and significant(0.51) GCA effect was observed for Pakistan-2013 only (seeTable 5). Patel et al. (2020) demonstrated ( p ≤ 0.01) significantnegative and desirable GCA effects in lines and non-additivegene action was primarily involved in days to heading.For plant height, negative general combining ability effectsare more important since more emphasis is placed upon selectionfor short stature in segregating the population because itultimately turns out that a short stature line is more responsiveto fertilizer and tolerant to lodging. In this study, GCA effectsranged between –5.82 and 3.24 for PH. Significant positive(3.07) and negative (–5.82) GCA effects were observedfor the lines Pirsabak-2005 and Punjab-2011, respectively.Similarly, highly significant positive (3.24) was estimated forthe tester BOR-16 and highly significant but negative (–2.56)GCA effects were observed for the testers PAK-13, respectively.These results are in accordance with the results of.For flag leaf area (FLA), negative general combining abilityeffects are more important because FLA is much influencedby transpiration losses due to disclosure to sunlight, whicheventually affects the grain yield. Hence, more emphasis isretained on the selection of genotypes with smaller FLA.From that, among the female parents, Pirsabak-2005 andPunjab-2011 showed a highly significant negative GCA effect:–2.16 and –2.10, respectively. On the other side, no significantGCA effects were observed among the testers for FLA. Theseresults confirm the findings of .In case of tillers per plant (TPP), GCA effects ranged between–0.58 and 0.98. As a good general combiner, highlysignificant positive (0.98) GCA effects were observed only forthe line MRJ-08 while there were no significant GCA effectsamong the testers for TPP. To begin with, TPP is a significantyield-boosting characteristic that contributes to increased grainyield. A higher number of tillers per plant confirms optimalplant populations and as a result higher grain yield . For this point of view, the female line MRJ-08showed better performance. These findings are in accordancewith the results of .GCA effects ranging between –1.16 and 1.39 were observedfor peduncle length (PL). Highly significant positive (1.39)and negative (–0.76) GCA effects were observed for the lines FSD-08 and ZRG-09, respectively. In the same way,highly significant positive (0.65) and negative (–1.16) GCAeffects were observed for the testers BOR-16 and PAK-13, respectively. Likewise, in PH, shorter PL is preferred becausean increase in PL ultimately increases the PH and we prefera plant with short stature. In current study, two female parents,ZRG-79 (–0.76) and MRJ-08 (–0.70), showed negative generalcombining ability. Also, one male parent, PAK-13, showedsuperior general combining ability for this trait. So, it can beconcluded that the above-mentioned parents are desirable foruse in the breeding program. The findings of  supported the results.Greater spike length (SL) and larger number of spikeletsper spike (SPS) are essential for enhanced yield. Among parents,one line , MRJ-08, showed significant positivevalues(0.77) for SPS. One tester, Pakistan-2013, exhibitedhigh GCA for SPS. These results were quite close to thefindings of . Number of grains per spike (GPS) is also animportantfactor for enhanced grain yield. Therefore, positiveGCA effects are more important due to positive contributionof grain yield. Among male parents, only NR-09 showedpositive and higher values (3.26) of GCA effects for GPS.Among female parents, MRJ-08 and PS-05 showed positiveand higher values, i. e. 2.90 and 2.02 respectively. It shouldbe noted that values of male parents were higher than thoseof female parents. These findings match with the results of. These results are different fromthe findings of Nazir et al. (2005).For grain yield per plant (GY), only one female parentMRJ-08, and among the male parents, BOR-16 and NR-09,exhibited positive general combining ability effects. Similarresults were also found by .Specific combining abilitySpecific combining ability (SCA) estimates for all the traitsare given in Table 6. Both positive and negative SCA effectswere observed among the crosses.As for SCA effects for DH, all the fifteen crosses were ofnon-significant nature with positive and negative magnitude(see Table 6). The result indicates the involvement of bothadditive and non-additive genetic effects in the inheritance ofDH, with greater proportion of additive genetic effect. Lineswith maximum SCA effects can be used in development ofhybrid cultivars. Only six among fifteen crosses depicted negativeSCA effects for plant height. If parents with tallness arethe ideal ones, then the crosses FSD-08 × NR-09, FSD-08 ×PAK- 13, PB-11 × NR-09, PB-11 × PAK-13, PS-05 × BOR-16and MRJ-08 × BOR-16 would be considered good. However,the remaining crosses exhibited higher SCA effects. Thesefindings confirmed the results of . Furthermore,non-additive type of gene action is detected for PH and supportedby . Also, our results concur withAli F.K.H. and Abdulkhaleq (2019) for plant height.GCA effects for flag leaf area range from negative –3.80 topositive 3.33. Roughly 50 % of the crosses showed smallervalues of SCA effects for flag leaf area, which is desirable.As less flag leaf area is required for drought tolerance, thecrosses with significant SCA effects, i. e. FSD-08 × BOR-16and PB-11 × PAK-13 may be used in a future breeding programbecause they have high negative SCA values contributingtowards minimum FLA. However, the remaining crossesexhibited higher positive SCA effects for FLA. Comparableresults have also been stated by .Negative SCA effects are needed to reduce the pedunclelength (PL). In this study, two crosses showed significantlynegative SCA effects. FSD-08 × NR-09 and MRJ-08 × BOR- 16are the best hybrids for reduced PL. Similar results were reportedby .In case of spike length (SL), all the fifteen crosses were ofnon-significant nature with positive and negative magnitude(see Table 6). For a number of SPS, positive specific combiningability effects were shown in 6 out of 15 crosses but onlytwo crosses, FSD-08 × NR-09 and PB-11 × BOR-16, havesignificant GCA effects. These hybrids performed best andcan be suggested for future breeding programs. These resultsare in the conformity with those of .For grain yield per plant, SCA effects found varied muchamong crosses. The poorest cross with respect to SCA forgrain yield per plant was ZRG-79 × PAK-13 whereas the crossthat appeared to be the best and the most promising specificcombination was ZRG-79 × NR-09. Positive specific combiningability effects were displayed in 8 out of 15 crosses.But only ZRG-79 × NR-09 showed such significant positiveeffects among crosses. Similar results were also reported by.Mid-parent heterosis estimation for grain yieldMid-parental heterosis (MPH) for GY was estimated for15 F1 hybrids . F1 hybrids ZRG-79 × PAK-13 showedhigher mid-parental value (62 %) followed by FSD-08 ×PAK- 13, ZRG-79 × BOR-16, and PB-11 × NR-09, whichrevealed mid-parent heterosis value above 30 % . Cross combinations PS- 05 × PAK- 13,MRJ- 08 × NR-09, MRJ-08 × PAK-13, FSD- 08 × NR- 09 depictedmid-parental heterosis value more than 15 %. Threecross combinations, ZRG-79 × NR-09, MRJ-08 × BOR- 16and PB-11 × BOR-16, depicted negative heterosis.Cross combinations with more than 30 % mid-parentalheterosis can be used in hybrid breeding in wheat. Heteroticstudies for increasing wheat grain yield has been an interestof early wheat researchers. Pal and Alam (1938) reportedmid-parent heterosis in the pre-green revolution era. After theintroduction of semi-dwarf wheat in the post-green revolutionera, various wheat researchers reported mid-parent heterosisin wheat, i. e. . Barbosa-Neto et al. (1996) reported MPH in red softwinter wheat in the range of –20 to 57 %. Liu et al. (1999),Dreisigacker et al. (2005), Basnet et al. (2019) studied MPHin CIMMYT wheat varieties and reported MPH in the rangeof 9.5 to 14 %. Parental lines and tester used in present studieshave CIMMYT background and the majority of the genotypesexhibited similar results for MPH. However, crosses combinationZRG-79 × PAK-13 has one indigenous parent ZRG-79and exhibited a high percentage of MPH. These finding candemonstrate that crosses among parents with CIMMYT backgroundhave low heterotic potential and additive gene actiongoverned the GY potential in these cross combinations andselection in the filial generation will be key for transgressive segregants, but in case of crosses among indigenous parentsand genotypes with a CIMMYT parent it will be good sourceof hybrid breeding.Correlation study of agronomic traitsCorrelation study among yield and related traits under rainfedconditions of eight parents and 15 wheat crosses is mentionedin Figure 3. High significance was observed between PH andTGW with the value of 0.9 ( p < 0.001), SL and SPS had acorrelation coefficient value of 0.72 ( p <0.001) followed byDH and DM with 0.57 ( p <0.01). PL also showed highlysignificant and positive correlation with TGW and PH . PL and TGW also revealedsignificant but negative correlation with DH –0.6 and –0.5,respectively ( p < 0.01).Positive and significant correlation among PH and PL withTGW showed that the higher the plant height the higher thethousand grain weight and peduncle length. Careful considerationshould be made while selecting the genotypes with stiffand strong stem girth to avoid lodging. Correlation betweenSL and SPS revealed that an increase in spike length leadsto an increase in spikelets per spike, genotypes with longspikes will be a good selection criterion for increasing yielddue to the increase in number of spikelets per spike. Positiveand significant correlation among DH and DM depicted thatgenotypes with early DH would mature earlier, so selectionof genotypes with early flowering is good for early maturityand short duration variety development. Significant but negativecorrelation between TGW and DH indicated that a delayin days to flowering leads to a reduced TGW and vice versa.TGW showed negative correlation with TPP and these findingsare in line with the results of Almutairi (2022). Low correlationof GY with other parameters in wheat was also reportedby Gowda et al. (2010).Stripe rust responses of parental linesand their cross combinationsThe response to stripe rust (Puccinia striiformis f. sp. tritici)on parental lines used in the study and their offspring(crosses) is recorded for disease scoring, coefficient of infection(CI), average coefficient of infection (ACI), country averagerelative percentage attack (CARPA) and rust resistanceindex (RRI) (Table 7). All the parental lines showed moderateresistant (MR) to highly resistant (R) reaction against striperust (Pst). The female parents (lines) FSD-08, PB-11, PS-05,MRJ-08 and ZRG-79 showed 20M, 20M, 5M, 30M and 40Mscores respectively, while the pollen parents (testers) viz.PAK-13, BOR-16 and NR-09 depicted 10MR, 5R and 40Mresponse against stripe rust. All these parents showed a slowrusting response against rust pathogen that is under the controlof multiple genesCross combinations of these parental lines showed a variedresponse, moderately resistant to moderately susceptible reaction against stripe rust. The F1 hybrids combinations PS- 05 ×PAK-13, PS-05 × BOR-16 and PS-05 × NR-09 showed 10MR,10MR and 5MR reaction, the crosses FSD-08 × PAK- 13,FSD-08 × BOR-16, PB-11 × PAK-13, PB-11 × BOR-16, andMRJ-08 × BOR-16 showed 20M reaction, the cross combinationMRJ-08 × PAK-13 showed 30M reaction, while the restof the crosses showed moderately susceptible to susceptiblereaction against stripe rust.Average coefficient of infection (ACI) for the parentsPS- 05, PAK-13, BOR-16 and FSD-08 was recorded as 2.0,6.7, 4.3 and 14.0 respectively and these varieties revealeda very good level of resistance against stripe rust. Rust resistanceindex (RRI) of these parents was also high (ranged 5.8to 8.5), which indicated a good resistance response of thesevarieties. Among the cross combinations, PS-05 × BOR-16,MRJ-08 × PAK-13, PB-11 × BOR-16, PB-11 × PAK-13 andPS-05 × NR-09 depicted ACI values of 4.0, 8.3, 12.0, 13.1, and14.7, respectively. These F1 hybrids had a resistant responseto stripe rust. RRI value of the F1 hybrids  was higher (ranging from 5.6 to 8.1).The higher the RRI value and the lower the ACI valuemeans of genotypes with a resistant response to the diseasepathogen and under the influence of slow rusting genes, theslower the disease progress and the lesser the yield losses.Genotypes with higher RRI values (>5.0) represent moderatelyresistant to highly resistant response against rust pathogen.The parental genotypes viz. PS-05, PAK-13 and BOR-16had higher values for RRI showing a highly resistant response against stripe rust pathogen.Cross combinations revealed an intermediate responseagainst stripe rust as compared to parents, especially testes,and resistant genes are under the control of additive geneaction. These results indicate that repeated backcross can bea better strategy for accumulation of resistant genes in thesecross combinations. Selection in these cross combinations byfollowing backcrosses with recurrent parents is efficient fordisease resistance in the filial generations. These results arevery much in line with the findings of Afzal et al. (2009) andMahmoud et al. (2015).According to these findings, it can be concluded that highergeneral combining ability and low broad sense heritabilityfor grain yield suggest the presence of additive genes, andexploitation of general combining ability for high grain yieldis important due to presence of additive gene action, and selectionin the filial generations and family rows will be effective. 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