Dataset Information

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

ABSTRACT: Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel.

SUBMITTER: Zhou H

PROVIDER: S-EPMC5340631 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

Zhou Hua H Blangero John J Dyer Thomas D TD Chan Kei-Hang K KK Lange Kenneth K Sobel Eric M EM

Genetic epidemiology 20161212 3

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped ...[more]

PMID: 27943406

Similar Datasets

Project description:Rice drought resistance is a complicated quantitative feature involving a range of biological and agronomic variables, but little is known about the underlying genetics and regulatory mechanisms that regulate drought tolerance. This study used 120 recombinant inbred lines (RILs), derived from a cross between drought tolerant Lvhan 1 and susceptible Aixian 1. The RILs were subjected to drought stress at the first ear stage, and phenotypic data of 16 agronomic and physiological traits under varying conditions were investigated. Genome-wide association study (GWAS) on the drought resistance index of traits was carried out. A total of 9 quantitative trait loci (QTLs) associated with drought-related traits were identified on chromosomes 2, 6, 7, 8, 9, and 10, which includes QTLs for plant height (PH) qPH10.1, effective panicles number (EPN) qEPN6.1, panicle length (PL) qPL9.1, thousand-grain weight (TGW) qTGW2.1, qTGW6.1, qTGW8.1, leaf length (LL) qLL7.1, leaf width (LW) qLW7.1, and leaf area (LA) qLA7.1. The fraction of phenotypic variation explained by individual QTL varied from 10.6% to 13.9%. Except for days to flowering (DTF), the mean values of all traits under normal water management conditions were considerably higher than those under drought conditions. Except for the DTF, the drought resistance index of all rice traits was less than 1, indicating that drought treatment reduced the EPN, FGPP, SSR, PH, and LA, which affected the growth and development of rice. The drought resistance index of DTF was 1.02, indicating that drought prolonged the heading time of rice and diminish the yield parameters. Along with identifying QTLs, the results also predicted ten candidate genes, which are directly or indirectly involved in various metabolic functioning related to drought stress. The identification of these genomic sites or QTLs that effectively respond to water scarcity will aid in the quest of understanding the drought tolerance mechanisms. This study will facilitate the marker-assisted rice breeding and handy in the breeding of drought-tolerant rice varieties.

Project description:The soilborne oomycete plant pathogen Pythium ultimum causes seed rot and pre-emergence damping-off of chickpea (Cicer arietinum L.). The pathogen has been controlled for several decades using the fungicide metalaxyl as seed treatment but has re-emerged as a severe problem with the detection of metalaxyl-resistant isolates of the pathogen from infested fields in the United States Pacific Northwest. The objective of this study was to identify genetic markers and candidate genes associated with resistance to P. ultimum in an interspecific recombinant inbred line population (CRIL-7) derived from a cross between C. reticulatum (PI 599072) x C. arietinum (FLIP 84-92C) and conduct genome-wide association studies (GWAS) for disease resistance using a chickpea diversity panel consisting of 184 accessions. CRIL-7 was examined using 1029 SNP markers spanning eight linkage groups. A major QTL, “qpsd4-1,” was detected on LG 4 that explained 41.8% of phenotypic variance, and a minor QTL, “qpsd8-1,” was detected on LG8 that explained 4.5% of phenotypic variance. Seven candidate genes were also detected using composite interval mapping including several genes previously associated with disease resistance in other crop species. A total of 302,902 single nucleotide polymorphic (SNP) markers were used to determine population structure and kinship of the diversity panel. Marker–trait associations were established by employing different combinations of principal components (PC) and kinships (K) in the FarmCPU model. Genome-wide association studies detected 11 significant SNPs and seven candidate genes associated with disease resistance. SNP Ca4_1765418, detected by GWAS on chromosome 4, was located within QTL qpsd4-1 that was revealed in the interspecific CRIL-7 population. The present study provides tools to enable MAS for resistance to P. ultimum and identified genomic domains and candidate genes involved in the resistance of chickpea to soilborne diseases.

Dataset Information

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

Publications

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets