GENODIVE GENETIC KINSHIP UPDATE
GENODIVE GENETIC KINSHIP FULL

One major feature of genodive is that it supports both diploid and polyploid data, up to octaploidy (2n = 8x) for some analyses, but up to hexadecaploidy (2n = 16x) for other analyses. Furthermore, genodive seamlessly supports 15 different file formats for importing or exporting data from or to other programs. genodive has an intuitive graphical user interface that allows direct manipulation of the data through transformation, imputation of missing data, and exclusion and inclusion of individuals, population and/or loci.

GENODIVE GENETIC KINSHIP UPDATE

This version presents a major update from the previous version and now offers a wide spectrum of different types of analyses. doi: 10.1038/ng.Genodive version 3.0 is a user-friendly program for the analysis of population genetic data. (2010) Common SNPs explain a large proportion of the heritability for human height. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, et al. (2016) Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models. doi: 10.1038/nmeth.1681Ĭhen H, Wang C, Conomos MP, Stilp AM, Li Z, et al. (2011) FaST linear mixed models for genome-wide association studies. Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, et al.

Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. (2010) Variance component model to account for sample structure in genome-wide association studies. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, et al. Using both simulated and real phenotypes, we further illustrate how our method improves estimation of trait heritability for WES studies.

GENODIVE GENETIC KINSHIP FULL

In application to the full WES dataset without down sampling, SEEKIN also outperforms existing methods by properly analyzing shallow off-target data (~0.75X). We show that SEEKIN can accurately estimate kinship coefficient and classify genetic relatedness using off-target sequencing data down sampled to ~0.15X depth. We test SEEKIN on a whole exome sequencing dataset (WES) of Singapore Chinese and Malays, which involves substantial population structure and admixture. Our method models genotype uncertainty and leverages linkage disequilibrium through imputation. We develop a computationally efficient method called SEEKIN to estimate kinship for both homogeneous samples and heterogeneous samples with population structure and admixture. We show that these methods, without accounting for the genotype uncertainty in sparse sequencing data, can yield a strong downward bias in kinship estimation. Existing methods often assume accurate genotypes at a large number of markers across the genome.

However, estimation of kinship is challenging for sparse sequencing data, such as those from off-target regions in target sequencing studies, where genotypes are largely uncertain or missing. In large-scale human genetic association studies, the estimated kinship is used to remove cryptic relatedness, control for family structure, and estimate trait heritability. Knowledge of biological relatedness between samples is important for many genetic studies.

Genodive genetic kinship