Dataset Information

Heredity Indexes for Estimating Heritability Using Known and Unknown Family Data Based on the Model of Polygenic Inheritance.

ABSTRACT: Objective:To establish a model for estimating genetic risk using known and unknown family data. Methods:Four simulated datasets were generated for four paternal and maternal chromosomes. The simulated data for children were generated from the parental data according to the Mendelian law. The correlation coefficient between the children's and paternal data was calculated, and 2R was defined as the heredity index for continuous data (HIC). The simulated continuous data were transformed into binary data according to the gene accumulation threshold (incidence); the incidences of children in the parental no-disease group and the disease onset group were obtained; the correlation coefficient (R) was calculated as expected R (Re). The ratio of observed R (Ro) and Re was defined as the Heredity index for binary data (HIB). Results:Different actual pedigree data (lunula and holding a hammer in the right or left hand) were successfully used to verify the accuracy of the model. The genetic risk was estimated according to the incidence in a population using a lookup table. Conclusion:Our findings indicate the reliability of the model based on the fact that the multigene effect constitutes the normal distribution. Thus, this model can be used for comprehensive analysis of the influence of genetic and nongenetic factors on the genetic phenotype and to estimate genetic risk using known and unknown family data.

SUBMITTER: Liu H

PROVIDER: S-EPMC7142385 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Heredity Indexes for Estimating Heritability Using Known and Unknown Family Data Based on the Model of Polygenic Inheritance.

Liu Hui H

Computational and mathematical methods in medicine 20200326

<h4>Objective</h4>To establish a model for estimating genetic risk using known and unknown family data.<h4>Methods</h4>Four simulated datasets were generated for four paternal and maternal chromosomes. The simulated data for children were generated from the parental data according to the Mendelian law. The correlation coefficient between the children's and paternal data was calculated, and 2<i>R</i> was defined as the heredity index for continuous data (HIC). The simulated continuous data were t ...[more]

PMID: 32300373

Similar Datasets

Project description:Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 x more heritability than GWAS-associated SNPs on average (P=3.3 x 10??). For some diseases, this increase was individually significant: 2.07 x for Multiple Sclerosis (MS) (P=6.5 x 10??) and 1.48 x for Crohn's Disease (CD) (P = 1.3 x 10?³); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 x more MS heritability than known MS SNPs (P < 1.0 x 10?¹? and 2.20 x more CD heritability than known CD SNPs (P = 6.1 x 10??), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 x more heritability from all SNPs at GWAS loci (P = 2.3 x 10??) and 5.33 x more heritability from all autoimmune disease loci (P < 1 x 10?¹? compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.

Dataset Information

Heredity Indexes for Estimating Heritability Using Known and Unknown Family Data Based on the Model of Polygenic Inheritance.

Publications

Heredity Indexes for Estimating Heritability Using Known and Unknown Family Data Based on the Model of Polygenic Inheritance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets