Beyond pathways: genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses

Although tocopherols play an important role in plants and animals, the genetic architecture of tocopherol content in maize kernels has remained largely unknown. In this study, linkage and association analyses were conducted to examine the genetic architecture of tocopherol content in maize kernels. Forty‐one unique quantitative trait loci (QTLs) were identified by linkage mapping in six populations of recombinant inbred lines (RILs). In addition, 32 significant loci were detected via genome‐wide association study (GWAS), 18 of which colocalized with the QTLs identified by linkage mapping. Fine mapping of a major QTL validated the accuracy of GWAS and QTL mapping results and suggested a role for nontocopherol pathway genes in the modulation of natural tocopherol variation. We provided genome‐wide evidence that genes involved in fatty acid metabolism, chlorophyll metabolism and chloroplast function may affect natural variation in tocopherols. These findings were confirmed through mutant analysis of a particular gene from the fatty acid pathway. In addition, the favourable alleles for many of the significant SNPs/QTLs represented rare alleles in natural populations. Together, our results revealed many novel genes that are potentially involved in the variation of tocopherol content in maize kernels. Pyramiding of the favourable alleles of the newly elucidated genes and the well‐known tocopherol pathway genes would greatly improve tocopherol content in maize.     

The genetic architecture of amino acids dissection by association and linkage analysis in maize

Amino acids are both constituents of proteins, providing the essential nutrition for humans and animals, and signalling molecules regulating the growth and development of plants. Most cultivars of maize are deficient in essential amino acids such as lysine and tryptophan. Here, we measured the levels of 17 different total amino acids, and created 48 derived traits in mature kernels from a maize diversity inbred collection and three recombinant inbred line (RIL) populations. By GWAS, 247 and 281 significant loci were identified in two different environments, 5.1 and 4.4 loci for each trait, explaining 7.44% and 7.90% phenotypic variation for each locus in average, respectively. By linkage mapping, 89, 150 and 165 QTLs were identified in B73/By804, Kui3/B77 and Zong3/Yu87‐1 RIL populations, 2.0, 2.7 and 2.8 QTLs for each trait, explaining 13.6%, 16.4% and 21.4% phenotypic variation for each QTL in average, respectively. It implies that the genetic architecture of amino acids is relative simple and controlled by limited loci. About 43.2% of the loci identified by GWAS were verified by expression QTL, and 17 loci overlapped with mapped QTLs in the three RIL populations. GRMZM2G015534, GRMZM2G143008 and one QTL were further validated using molecular approaches. The amino acid biosynthetic and catabolic pathways were reconstructed on the basis of candidate genes proposed in this study. Our results provide insights into the genetic basis of amino acid biosynthesis in maize kernels and may facilitate marker‐based breeding for quality protein maize.     

Metabolic GWAS‐based dissection of genetic bases underlying the diversity of plant metabolism

Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome‐wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass‐spectrometry‐based analytical systems and genome sequencing technologies. Metabolic genome‐wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS‐based multi‐dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS‐based multi‐dimensional analysis and emerging insights into the diversity of metabolism.     

Metabolome‐genome‐wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism

Plants produce structurally diverse secondary (specialized) metabolites to increase their fitness for survival under adverse environments. Several bioactive compounds for new drugs have been identified through screening of plant extracts. In this study, genome‐wide association studies (GWAS) were conducted to investigate the genetic architecture behind the natural variation of rice secondary metabolites. GWAS using the metabolome data of 175 rice accessions successfully identified 323 associations among 143 single nucleotide polymorphisms (SNPs) and 89 metabolites. The data analysis highlighted that levels of many metabolites are tightly associated with a small number of strong quantitative trait loci (QTLs). The tight association may be a mechanism generating strains with distinct metabolic composition through the crossing of two different strains. The results indicate that one plant species produces more diverse phytochemicals than previously expected, and plants still contain many useful compounds for human applications.     

Genome‐wide association studies on the phyllosphere microbiome: Embracing complexity in host–microbe interactions

Environmental sequencing shows that plants harbor complex communities of microbes that vary across environments. However, many approaches for mapping plant genetic variation to microbe‐related traits were developed in the relatively simple context of binary host–microbe interactions under controlled conditions. Recent advances in sequencing and statistics make genome‐wide association studies (GWAS) an increasingly promising approach for identifying the plant genetic variation associated with microbes in a community context. This review discusses early efforts on GWAS of the plant phyllosphere microbiome and the outlook for future studies based on human microbiome GWAS. A workflow for GWAS of the phyllosphere microbiome is then presented, with particular attention to how perspectives on the mechanisms, evolution and environmental dependence of plant–microbe interactions will influence the choice of traits to be mapped.     

Identification of Hepatocarcinogen-Resistance Genes in Dba/2 Mice

Male DBA/2J mice are ~20-fold more susceptible than male C57BL/6J mice to hepatocarcinogenesis induced by perinatal treatment with N,N-diethylnitrosamine (DEN). In order to elucidate the genetic control of hepatocarcinogenesis in DBA/2J mice, male BXD recombinant inbred, D2B6F(1) X B6 backcross, and D2B6F(2) intercross mice were treated at 12 days of age with DEN and liver tumors were enumerated at 32 weeks. Interestingly, the distribution of mean tumor multiplicities among BXD recombinant inbred strains indicated that hepatocarcinogen-sensitive DBA/2 mice carry multiple genes with opposing effects on the susceptibility to liver tumor induction. By analyzing D2B6F(1) X B6 backcross and D2B6F(2) intercross mice for their liver tumor multiplicity phenotypes and for their genotypes at simple sequence repeat marker loci, we mapped two resistance genes carried by DBA/2J mice, designated Hcr1 and -2, to chromosomes 4 and 10, respectively. Hcr1 and Hcr2 resolved the genetic variance in the backcross population well, indicating that these resistance loci are the major determinants of the variance in the backcross population. Although our collection of 100 simple sequence repeat markers allowed linkage analysis for ~95% of the genome, we failed to map any sensitivity alleles for DBA/2J mice. Thus, it is likely that the susceptibility of DBA/2J mice is the consequence of the combined effects of multiple sensitivity loci.     

Breeding experiments and genome‐wide association analysis elucidate two genetically different forms of non‐syndromic congenital cleft lip and jaw in Vorderwald × Montbéliarde cattle

Non‐syndromic congenital cleft lip and jaw (CLJ) is a condition reported in Vorderwald × Montbéliarde cattle. The objective of the present study was to perform a genome‐wide association study (GWAS) for 10 CLJ‐affected and 50 unaffected Vorderwald × Montbéliarde cattle using the bovine Illumina high density bead chip to identify loci for this condition. Phenotypic classification of CLJ was based on a detailed recording of orofacial structures using computed tomography. A breeding experiment among CLJ‐affected Vorderwald × Montbéliarde cattle and CLJ‐affected Vorderwald × Montbéliarde cattle with unaffected Holsteins confirmed recessive inheritance and different loci for bilateral or left‐sided versus right‐sided CLJ. The GWAS for the five cases with right‐sided CLJ gave a genome‐wide signal on bovine chromosome (BTA) 29 at 16 Mb. For the four left‐sided and one bilateral CLJ case, a genome‐wide significant association was identified on BTA4 at 32 Mb. Two different loci are very likely to be involved in CLJ in Vorderwald × Montbéliarde cattle because experimental matings among affected cows and bulls with different types of CLJ did not result in CLJ‐affected progeny, and in addition, two different loci were also found through GWAS and mapped on two different bovine chromosomes. Validation in 346 Vorderwald × Montbéliarde cattle for the highly associated SNPs on BTA4 and 29 gave ratios of 33/346 (0.095, BTA4) and 6/346 (0.017, BTA29) homozygous mutant genotypes. Further studies should elucidate the responsible mutations underlying the different types of CLJ in Vorderwald × Montbéliarde cattle.     

Searching for genetic factors of fatty liver in SMXA-5 mice by quantitative trait loci analysis under a high-fat diet

Fatty liver is strongly associated with the metabolic syndrome characterized by obesity, insulin resistance, and type 2 diabetes, but the genetic basis and functional mechanisms linking fatty liver with the metabolic syndrome are largely unknown. The SMXA-5 mouse is one of the SMXA recombinant inbred substrains established from SM/J and A/J strains and is a model for polygenic type 2 diabetes, characterized by moderately impaired glucose tolerance, hyperinsulinemia, and mild obesity. SMXA-5 mice also developed fatty liver, and a high-fat diet markedly worsened this trait, although SM/J and A/J mice are resistant to fatty liver development under a high-fat diet. To dissect loci for fatty liver in the A/J regions of the SMXA-5 genome, we attempted quantitative trait loci (QTLs) analysis in (SM/JxSMXA-5)F2 intercross mice fed a high-fat diet. We mapped a major QTL for relative liver weight and liver lipid content near D12Mit270 on chromosome 12 and designated this QTL Fl1sa. The A/J allele at this locus contributes to the increase in these traits. We confirmed the effect of Fl1sa on lipid accumulation in liver using the A/J-Chr12(SM) consomic strain, which showed significantly less accumulation than A/J mice. This suggests that the SM/J and A/J strains, neither of which develops fatty liver, possess loci causing fatty liver and that the coexistence of these loci causes fatty liver in SMXA-5 mice.     

Genomic dissection of variation in clutch size and egg mass in a wild great tit (Parus major) population

Clutch size and egg mass are life history traits that have been extensively studied in wild bird populations, as life history theory predicts a negative trade‐off between them, either at the phenotypic or at the genetic level. Here, we analyse the genomic architecture of these heritable traits in a wild great tit (Parus major) population, using three marker‐based approaches – chromosome partitioning, quantitative trait locus (QTL) mapping and a genome‐wide association study (GWAS). The variance explained by each great tit chromosome scales with predicted chromosome size, no location in the genome contains genome‐wide significant QTL, and no individual SNPs are associated with a large proportion of phenotypic variation, all of which may suggest that variation in both traits is due to many loci of small effect, located across the genome. There is no evidence that any regions of the genome contribute significantly to both traits, which combined with a small, nonsignificant negative genetic covariance between the traits, suggests the absence of genetic constraints on the independent evolution of these traits. Our findings support the hypothesis that variation in life history traits in natural populations is likely to be determined by many loci of small effect spread throughout the genome, which are subject to continued input of variation by mutation and migration, although we cannot exclude the possibility of an additional input of major effect genes influencing either trait.     

Haplotype‐based genotyping‐by‐sequencing in oat genome research

In a de novo genotyping‐by‐sequencing (GBS) analysis of short, 64‐base tag‐level haplotypes in 4657 accessions of cultivated oat, we discovered 164741 tag‐level (TL) genetic variants containing 241224 SNPs. From this, the marker density of an oat consensus map was increased by the addition of more than 70000 loci. The mapped TL genotypes of a 635‐line diversity panel were used to infer chromosome‐level (CL) haplotype maps. These maps revealed differences in the number and size of haplotype blocks, as well as differences in haplotype diversity between chromosomes and subsets of the diversity panel. We then explored potential benefits of SNP vs. TL vs. CL GBS variants for mapping, high‐resolution genome analysis and genomic selection in oats. A combined genome‐wide association study (GWAS) of heading date from multiple locations using both TL haplotypes and individual SNP markers identified 184 significant associations. A comparative GWAS using TL haplotypes, CL haplotype blocks and their combinations demonstrated the superiority of using TL haplotype markers. Using a principal component‐based genome‐wide scan, genomic regions containing signatures of selection were identified. These regions may contain genes that are responsible for the local adaptation of oats to Northern American conditions. Genomic selection for heading date using TL haplotypes or SNP markers gave comparable and promising prediction accuracies of up to r = 0.74. Genomic selection carried out in an independent calibration and test population for heading date gave promising prediction accuracies that ranged between r = 0.42 and 0.67. In conclusion, TL haplotype GBS‐derived markers facilitate genome analysis and genomic selection in oat.     

Genomic analyses reveal the genetic basis of early maturity and identification of loci and candidate genes in upland cotton (Gossypium hirsutum L.)

Although upland cotton (Gossypium hirsutism L.) originated in the tropics, this early maturity cotton can be planted as far north as 46°N in China due to the accumulation of numerous phenotypic and physiological adaptations during domestication. However, how the genome of early maturity cotton has been altered by strong human selection remains largely unknown. Herein, we report a cotton genome variation map generated by the resequencing of 436 cotton accessions. Whole‐genome scans for sweep regions identified 357 putative selection sweeps covering 4.94% (112 Mb) of the upland cotton genome, including 5184 genes. These genes were functionally related to flowering time control, hormone catabolism, ageing and defence response adaptations to environmental changes. A genome‐wide association study (GWAS) for seven early maturity traits identified 307 significant loci, 22.48% (69) of which overlapped with putative selection sweeps that occurred during the artificial selection of early maturity cotton. Several previously undescribed candidate genes associated with early maturity were identified by GWAS. This study provides insights into the genetic basis of early maturity in upland cotton as well as breeding resources for cotton improvement.     

Genetic analysis of endogenous xenotropic murine leukemia viruses: association with two common mouse mutations and the viral restriction locus Fv-1.

We have defined 40 endogenous xenotropic virus (Xmv) loci from several common inbred strains of mice by examining provirus-cell DNA junction fragments in recombinant inbred mice. Some inbred strains carried unique proviruses, but most Xmv loci were present in several strains, indicating that many Xmv integration events preexisted modern inbreeding. It was also clear that most Xmv junction fragment variation between inbred strains resulted from independent integration events and not modification or restriction site polymorphism following integration. Chromosomal assignments were determined for 32 Xmv loci by comparing their recombinant inbred strain distribution patterns to those of known genetic markers. The Xmv loci were generally dispersed throughout the genome, but several chromosomal regions contained more than one provirus. Furthermore, several close genetic associations with cellular genes were discovered. Four Xmv loci were closely linked to Fv-1b, a dominant viral resistance gene present in C57BL/6J, BALB/cJ, A/J, and several other strains. Xmv-28 was closely linked to rd (retinal degeneration), and Xmv-10 was closely linked to a (non-agouti), both of which are old mutations as inferred from their broad distribution in mice. We suggest that Xmv integration contributed to genetic diversity in the past and that much of this diversity exists today in common laboratory strains.     

QTL mapping and GWAS reveal candidate genes controlling capsaicinoid content in Capsicum

Capsaicinoids are unique compounds produced only in peppers (Capsicum spp.). Several studies using classical quantitative trait loci (QTLs) mapping and genomewide association studies (GWAS) have identified QTLs controlling capsaicinoid content in peppers; however, neither the QTLs common to each population nor the candidate genes underlying them have been identified due to the limitations of each approach used. Here, we performed QTL mapping and GWAS for capsaicinoid content in peppers using two recombinant inbred line (RIL) populations and one GWAS population. Whole‐genome resequencing and genotyping by sequencing (GBS) were used to construct high‐density single nucleotide polymorphism (SNP) maps. Five QTL regions on chromosomes 1, 2, 3, 4 and 10 were commonly identified in both RIL populations over multiple locations and years. Furthermore, a total of 109 610 SNPs derived from two GBS libraries were used to analyse the GWAS population consisting of 208 C. annuum‐clade accessions. A total of 69 QTL regions were identified from the GWAS, 10 of which were co‐located with the QTLs identified from the two biparental populations. Within these regions, we were able to identify five candidate genes known to be involved in capsaicinoid biosynthesis. Our results demonstrate that QTL mapping and GBS‐GWAS represent a powerful combined approach for the identification of loci controlling complex traits.     

Pleiotropy informed adaptive association test of multiple traits using genome‐wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large‐scale genome‐wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease‐related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans‐Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the “single trait” association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well‐characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome‐phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual‐level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P‐value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta‐analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta‐analysis. All the proposed methods are implemented in a publicly available R package.     

Nucleotide variation in wild and inbred mice

The house mouse is a well-established model organism, particularly for studying the genetics of complex traits. However, most studies of mice use classical inbred strains, whose genomes derive from multiple species. Relatively little is known about the distribution of genetic variation among these species or how variation among strains relates to variation in the wild. We sequenced intronic regions of five X-linked loci in large samples of wild Mus domesticus and M. musculus, and we found low levels of nucleotide diversity in both species. We compared these data to published data from short portions of six X-linked and 18 autosomal loci in wild mice. We estimate that M. domesticus and M. musculus diverged <500,000 years ago. Consistent with this recent divergence, some gene genealogies were reciprocally monophyletic between these species, while others were paraphyletic or polyphyletic. In general, the X chromosome was more differentiated than the autosomes. We resequenced classical inbred strains for all 29 loci and found that inbred strains contain only a small amount of the genetic variation seen in wild mice. Notably, the X chromosome contains proportionately less variation among inbred strains than do the autosomes. Moreover, variation among inbred strains derives from differences between species as well as from differences within species, and these proportions differ in different genomic regions. Wild mice thus provide a reservoir of additional genetic variation that may be useful for mapping studies. Together these results suggest that wild mice will be a valuable complement to laboratory strains for studying the genetics of complex traits.