Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization

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A Powerful and Adaptive Association Test for Rare Variants

This article focuses on conducting global testing for association between a binary trait and a set of rare variants (RVs), although its application can be much broader to other types of traits, common variants (CVs), and gene set or pathway analysis. We show that many of the existing tests have deteriorating performance in the presence of many nonassociated RVs: their power can dramatically drop as the proportion of nonassociated RVs in the group to be tested increases. We propose a class of so-called sum of powered score (SPU) tests, each of which is based on the score vector from a general regression model and hence can deal with different types of traits and adjust for covariates, e.g., principal components accounting for population stratification. The SPU tests generalize the sum test, a representative burden test based on pooling or collapsing genotypes of RVs, and a sum of squared score (SSU) test that is closely related to several other powerful variance component tests; a previous study (Basu and Pan 2011) has demonstrated good performance of one, but not both, of the Sum and SSU tests in many situations. The SPU tests are versatile in the sense that one of them is often powerful, although its identity varies with the unknown true association parameters. We propose an adaptive SPU (aSPU) test to approximate the most powerful SPU test for a given scenario, consequently maintaining high power and being highly adaptive across various scenarios. We conducted extensive simulations to show superior performance of the aSPU test over several state-of-the-art association tests in the presence of many nonassociated RVs. Finally we applied the SPU and aSPU tests to the GAW17 mini-exome sequence data to compare its practical performance with some existing tests, demonstrating their potential usefulness.     

普通菜豆抗菜豆象性状的全基因组关联分析

普通菜豆(Phaseolus vulgaris)具有丰富的营养价值, 菜豆象(Acanthoscelides obtectus)是危害菜豆的主要害虫, 利用抗虫种质资源防治菜豆象是最安全且经济有效的方法。该研究利用改良的室内人工接虫方法, 对625份普通菜豆种质资源进行2次菜豆象抗性重复鉴定, 筛选出2份抗性稳定且种子受害率均在10%以下的高抗种质。利用种子受害率和蛀孔总数的表型数据, 基于3 767 432个SNP标记进行全基因组关联分析, 鉴定出15个与种子受害率相关的显著关联遗传位点, 8个与蛀孔总数相关的显著关联位点, 解释了4.54%-5.56%的表型变异。在候选位点筛选出包括编码蛋白酶抑制剂、凝集素和过氧化物酶等在内的20个与抗虫防御相关的候选基因。     

A genome-wide association study identified new variants associated with mathematical abilities in Chinese children

Mathematical ability is moderately heritable, and it is a complex trait which can be evaluated in several different categories. A few genetic studies have been published on general mathematical ability. However, no genetic study focused on specific mathematical ability categories. In this study, we separately performed genome-wide association studies on 11 mathematical ability categories in 1146 students from Chinese elementary schools. We identified seven genome-wide significant single nucleotide polymorphisms (SNPs) with strong linkage disequilibrium among each other (all r² > 0.8) associated with mathematical reasoning ability (top SNP: rs34034296, p = 2.01 × 10⁻⁸, nearest gene: CUB and Sushi multiple domains 3, CSMD3). We replicated one SNP (rs133885) from 585 SNPs previously reported to be associated with general mathematical ability associated with division ability in our data (p = 1.053 × 10⁻⁵). In the gene- and gene-set enrichment analysis by MAGMA, we found three significant enrichments of associations with three mathematical ability categories for three genes (LINGO2, OAS1 and HECTD1). We also observed four significant enrichments of associations with four mathematical ability categories for three gene sets. Our results suggest new candidate genetic loci for the genetics of mathematical ability.     

奶牛重要经济性状全基因组关联分析研究进展

产奶性状是奶牛最重要的生产性状,随着平衡育种理念的提出和发展,繁殖性状、体型性状、健康性状和长寿性等功能性状也逐渐被重视并纳入育种规划中。鉴定产奶性状和功能性状主效基因或遗传标记并将之应用于奶牛标记辅助选择可望加快遗传进展。随着高密度SNP标记的高通量检测技术的发展,全基因组关联分析已成为鉴定畜禽重要经济性状基因的重要途径。文章对奶牛产奶性状和功能性状全基因组关联分析研究进展进行综述。     

奶牛重要经济性状全基因组关联分析研究进展

产奶性状是奶牛最重要的生产性状, 随着平衡育种理念的提出和发展, 繁殖性状、体型性状、健康性状和长寿性等功能性状也逐渐被重视并纳入育种规划中。鉴定产奶性状和功能性状主效基因或遗传标记并将之应用于奶牛标记辅助选择可望加快遗传进展。随着高密度SNP标记的高通量检测技术的发展, 全基因组关联分析已成为鉴定畜禽重要经济性状基因的重要途径。文章对奶牛产奶性状和功能性状全基因组关联分析研究进展进行综述。     

A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants

In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.     

ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci

Genome-wide association studies(GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common traitassociated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus(e QTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loc...     

Genetic variation, population structure, and linkage disequilibrium in European elite germplasm of perennial ryegrass

Perennial ryegrass (Lolium perenne L.) is a highly valued temperate climate grass species grown as forage crop and for amenity uses. Due to its outbreeding nature and recent domestication, a high degree of genetic diversity is expected among cultivars. The aim of this study was to assess the extent of linkage disequilibrium (LD) within European elite germplasm and to evaluate the appropriate methodology for genetic association mapping in perennial ryegrass. A high level of genetic diversity was observed in a set of 380 perennial ryegrass elite genotypes when genotyped with 40 SSRs and 2 STS markers. A Bayesian structure analysis identified two subpopulations, which were confirmed by principal coordinate analysis (PCoA). One subpopulation consisted mainly of genotypes originating from the UK, while germplasm mostly from Continental Europe was grouped into the second subpopulation. LD (r²) decay was rapid and occurred within 0.4 cM across European varieties, when population structure was taken into consideration. However, an extended LD of up to 6.6 cM was detected within the variety Aberdart. High genetic diversity and rapid LD decay provide means for high resolution association mapping in elite materials of perennial ryegrass. However, different strategies need to be applied depending on the material used. Genome-wide association study (GWAS) with several hundred markers can be applied within synthetic varieties to identify large (up to 10 cM) genomic regions affecting trait variation. A combination of available and novel DNA markers is needed to achieve resolution required for GWAS in elite breeding materials. An even higher marker density of several million SNPs might be needed for GWAS in diverse ecotype collections, potentially resulting in quantitative trait polymorphism (QTP) identification.     

Rice Pangenome Genotyping Array: an efficient genotyping solution for pangenome-based accelerated genetic improvement in rice

The advent of the pangenome era has unraveled previously unknown genetic variation existing within diverse crop plants, including rice. This untapped genetic variation is believed to account for a major portion of phenotypic variation existing in crop plants. However, the use of conventional single reference-guided genotyping often fails to capture a large portion of this genetic variation leading to a reference bias. This makes it difficult to identify and utilize novel population/cultivar-specific genes for crop improvement. Thus, we developed a Rice Pangenome Genotyping Array (RPGA) harboring probes assaying 80K single-nucleotide polymorphisms (SNPs) and presence–absence variants spanning the entire 3K rice pangenome. This array provides a simple, user-friendly and cost-effective (60–80 USD per sample) solution for rapid pangenome-based genotyping in rice. The genome-wide association study (GWAS) conducted using RPGA-SNP genotyping data of a rice diversity panel detected a total of 42 loci, including previously known as well as novel genomic loci regulating grain size/weight traits in rice. Eight of these identified trait-associated loci (dispensable loci) could not be detected with conventional single reference genome-based GWAS. A WD repeat-containing PROTEIN 12 gene underlying one of such dispensable locus on chromosome 7 (qLWR7) along with other non-dispensable loci were subsequently detected using high-resolution quantitative trait loci mapping confirming authenticity of RPGA-led GWAS. This demonstrates the potential of RPGA-based genotyping to overcome reference bias. The application of RPGA-based genotyping for population structure analysis, hybridity testing, ultra-high-density genetic map construction and chromosome-level genome assembly, and marker-assisted selection was also demonstrated. A web application ( http://www.rpgaweb.com ) was further developed to provide an easy to use platform for the imputation of RPGA-based genotyping data using 3K rice reference panel and subsequent GWAS.     

Association mapping of morphological traits in wild and captive zebra finches: reliable within,but not between populations

Identifying causal genetic variants underlying heritable phenotypic variation is a long‐standing goal in evolutionary genetics. We previously identified several quantitative trait loci (QTL) for five morphological traits in a captive population of zebra finches (Taeniopygia guttata) by whole‐genome linkage mapping. We here follow up on these studies with the aim to narrow down on the quantitative trait variants (QTN) in one wild and three captive populations. First, we performed an association study using 672 single nucleotide polymorphisms (SNPs) within candidate genes located in the previously identified QTL regions in a sample of 939 wild‐caught zebra finches. Then, we validated the most promising SNP–phenotype associations (n = 25 SNPs) in 5228 birds from four populations. Genotype–phenotype associations were generally weak in the wild population, where linkage disequilibrium (LD) spans only short genomic distances. In contrast, in captive populations, where LD blocks are large, apparent SNP effects on morphological traits (i.e. associations) were highly repeatable with independent data from the same population. Most of those SNPs also showed significant associations with the same trait in other captive populations, but the direction and magnitude of these effects varied among populations. This suggests that the tested SNPs are not the causal QTN but rather physically linked to them, and that LD between SNPs and causal variants differs between populations due to founder effects. While the identification of QTN remains challenging in nonmodel organisms, we illustrate that it is indeed possible to confirm the location and magnitude of QTL in a population with stable linkage between markers and causal variants.     

A genome‐wide association analysis for carcass traits in a commercial Duroc pig population

We performed a genome‐wide association study to map the genetic determinants of carcass traits in 350 Duroc pigs typed with the Porcine SNP60 BeadChip. Association analyses were carried out using the gemma software. The proportion of phenotypic variance explained by the SNPs ranged between negligible to moderate (= 0.01–0.30) depending on the trait under consideration. At the genome‐wide level, we detected one significant association between backfat thickness between the 3^rd and 4^th ribs and six SNPs mapping to SSC12 (37–40 Mb). We also identified several chromosome‐wide significant associations for ham weight (SSC11: 51–53 Mb, three SNPs; 67–68 Mb, two SNPs), carcass weight (SSC11: 66–68 Mb, two SNPs), backfat thickness between the 3^rd and 4^th ribs (SSC12: 21 Mb, one SNP; 33–40 Mb, 17 SNPs; 51–58 Mb, two SNPs), backfat thickness in the last rib (SSC12: 37 Mb, one SNP; 40–41 Mb, nine SNPs) and lean meat content (SSC13: 34 Mb, three SNPs and SSC16: 45.1 Mb, one SNP; 62–63 Mb, 10 SNPs; 71–75 Mb, nine SNPs). The ham weight trait‐associated region on SSC11 contains two genes (UCHL3 and LMO7) related to muscle development. In addition, the ACACA gene, which encodes an enzyme for the catalysis of fatty acid synthesis, maps to the SSC12 (37–41 Mb) region harbouring trait‐associated regions for backfat thickness traits. Sequencing of these candidate genes may help to uncover the causal mutations responsible for the associations found in the present study.     

A functional genomics approach to evaluate candidate genes located in a QTL interval for milk production traits on BTA6

The potential genetic and economic advantage of marker-assisted selection for enhanced production in dairy cattle has provided an impetus to conduct numerous genome scans in order to identify associations between DNA markers and future productive potential. One area of focus has been a quantitative trait locus on bovine chromosome 6 (BTA6) found to be associated with milk yield, milk protein and fat percentage, which has been subsequently fine-mapped to six positional candidate genes. Subsequent investigations have yet to resolve which of the potential positional candidate genes is responsible for the observed associations with productive performance. In this study, we analysed candidate gene expression and the effects of gene knockdown on expression of β- and κ-casein mRNA in a small interfering RNA transfected bovine in vitro mammosphere model. From our expression studies in vivo , we observed that four of the six candidates ( ABCG2 , SPP1 , PKD2 and LAP3 ) exhibited differential expression in bovine mammary tissue over the lactation cycle, but in vitro functional studies indicate that inhibition of only one gene, SPP1 , had a significant impact on milk protein gene expression. These data suggest that the gene product of SPP1 (also known as osteopontin) has a significant role in the modulation of milk protein gene expression. While these findings do not exclude other positional candidates from influencing lactation, they support the hypothesis that the gene product of SPP1 is a significant lactational regulatory molecule.     

A genome-wide association study for quantitative trait loci of show-jumping in Hanoverian warmblood horses

Show-jumping is an economically important breeding goal in Hanoverian warmblood horses. The aim of this study was a genome-wide association study (GWAS) for quantitative trait loci (QTL) for show-jumping in Hanoverian warmblood horses, employing the Illumina equine SNP50 Beadchip. For our analyses, we genotyped 115 stallions of the National State stud of Lower Saxony. The show-jumping talent of a horse includes style and ability in free-jumping. To control spurious associations based on population stratification, two different mixed linear animal model (MLM) approaches were employed, besides linear models with fixed effects only and adaptive permutations for correcting multiple testing. Population stratification was explained best in the MLM considering Hanoverian, Thoroughbred, Trakehner and Holsteiner genes and the marker identity-by-state relationship matrix. We identified six QTL for show-jumping on horse chromosomes (ECA) 1, 8, 9 and 26 (-log(10) P-value >5) and further putative QTL with -log(10) P-values of 3-5 on ECA1, 3, 11, 17 and 21. Within six QTL regions, we identified human performance-related genes including PAPSS2 on ECA1, MYL2 on ECA8, TRHR on ECA9 and GABPA on ECA26 and within the putative QTL regions NRAP on ECA1, and TBX4 on ECA11. The results of our GWAS suggest that genes involved in muscle structure, development and metabolism are crucial for elite show-jumping performance. Further studies are required to validate these QTL in larger data sets and further horse populations.