IPED2: Inheritance Path based Pedigree Reconstruction Algorithm for Complicated Pedigrees

Our group, in an effort led by former UCLA PhD student Dan He, developed an algorithm for reconstructing pedigrees with genotype data. This novel approach is presented in a paper recently published in IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Pedigree inference plays an important role in population genetics. Pedigrees, commonly known as family trees, represent genetic relationships between individuals of a family. A pedigree diagram provides a model to compute the inheritance probability for the observed genotype and encodes all possible inheritance options for an allele in an individual. Pedigree reconstruction methods face several challenges. First, there can be an exponential number of possible pedigree graphs, and, second, the number of unknown ancestors can become very large as the height of the pedigree increases.

Examples of sequentially labeling the half-sibling graph. For more information, see our paper.

Examples of sequentially labeling the half-sibling graph. For more information, see our paper.

Our project uses genotype data to reconstruct pedigrees with computational efficiency despite these challenges. Our previous method, IPED, is the only known algorithm scalable to large pedigrees with reasonable accuracy for cases involving both outbreeding and inbreeding. IPED starts from extant individuals and reconstructs the pedigree generation by generation backwards in time. For each generation, IPED predicts the pairwise relationships between the individuals at the current generation and create parents for them according to their relationships.

Existing methods, including IPED, only consider pedigrees with simple structure; they cannot handle populations where, for example, two children share only one parent. To improve pedigree reconstruction when populations have complex structure, we proposed the novel method IPED2. Our approach uses a new statistical test to detect half-sibling relationships and a new graph-based algorithm to reconstruct the pedigree when half-siblings are allowed.

In order to test the performance of our method on complicated pedigrees, we use simulated pedigrees with different parameter settings and, instead of genotype data, we simulate haplotypes
directly. Our experiments show that IPED2 outperforms IPED and two other existing approaches for cases where there are half-siblings.

To our knowledge, this is the first method that can, using just genotype data, reconstruct pedigrees with half-siblings and inbreeding. IPED2 is also scalable to large pedigrees. In future work, we would like to consider additional genetic actions, such as insertion, deletion, and replacement, to resolve the conflicts. We also plan to refine IPED2 to consider cases where genotypes of ancestral individuals are known and where genotypes of extant individuals that are not on the lowest generations are known.

For more information, see our paper, which is available for download through Bioinformaticshttp://ieeexplore.ieee.org/abstract/document/7888513/.

In addition, the open source implementation of IPED2, which was developed by Dan He, is freely available for download at http://genetics.cs.ucla.edu/Dan/Software/IPED2.html.

The full citation to our paper is:
He, D., Wang, Z., Parida, L. and Eskin, E., 2017. IPED2: Inheritance path based pedigree reconstruction algorithm for complicated pedigrees. IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder

Variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits. Genome-wide characterization of gene expression is an important tool in genetic mapping studies of complex disorders, including many psychiatric disorders. Further, implicating eQTL to specific tissue types is key to understanding functional variation in disease development. Our group, in collaboration with Chiara Sabatti (Statistics, Stanford) and Nelson B. Freimer (David Geffen School of Medicine, UCLA), developed a novel approach for analyzing eQTL and applied the method to a dataset from a bipolar disorder study.

Current approaches to implicating eQTL specific to tissues lack sufficient power in large-scale studies of human brain related traits, such as bipolar disorder. Together with the University of California San Francisco, Universidad de Costa Rica, Universidad de Antioquia, Medellín, Colombia, and Tel Aviv University, our group adopted a novel approach to assess the heritability and genetic regulation of gene expression related to bipolar disorder in populations from Costa Rica and Colombia.

This project examines 786 genotyped subjects originally recruited in a study of bipolar disorder, all related within 26 extended families. While the subjects in this study were originally recruited as part of an investigation for severe bipolar disorder (BP1), we found no relationship between the observed gene expression data and BP1. Instead, we use this unique Latin American population to explore the architecture of genetic regulation. Specifically, we estimate heritability, evaluate the relative importance of local vs. distal genomic variation, identify variants with regulatory effects, and analyze the role of multiple associated SNPs in the same region.

Our group adopted a novel hierarchical testing procedure that leads to the analysis of eQTL data in a stage-wise manner with increasing levels of detail. This design allows us to compare estimates of the heritability of gene expression obtained using both traditional and genotype-based methods. First, we apply a multiscale testing strategy to identify SNPs that have regulatory effects (eSNPs) on BP1. Second, we investigate which specific probes are influenced by these eSNPs. This hierarchical testing procedure effectively controls error rates and leverages the heterogeneity across genetic variants to preserve computational power.

We use this approach to measure gene expression in lymphoblastoid cell lines (LCLs) in subjects from extended families, segregating for BP1. Our results suggest that variation in expression values is heritable and that, at least in samples including related individuals, relying on theoretical kinship coefficients or on realized genotype correlation for estimation of heritability leads to similar results.

Expression heritability and proportion of genetic variance due to local effects. For more information, see our paper. For more information, see our paper.

Variance decomposition approaches suggest that on average 30% of the genetic variance is due to local regulation. In the majority of probes under local regulation in our sample, more than one typed SNP is required to account for expression variation. This finding can be interpreted as the result of heterogeneity, but also could reflect un-typed causal variants that are tracked by more than one typed SNP.

The knowledge we acquired by studying the genetic regulatory network within these pedigrees, instead, can be used to inform our mapping studies: eSNPs might receive a higher prior probability of association, or be assigned a larger portion of the allowed global error rate when using a weighted approach to testing. We will report elsewhere on the results of these investigations.

For more information, see our paper, which is available for download through PLoS Genetics: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006046.

The full citation to our paper is: 

Peterson, C.B., Jasinska, A.J., Gao, F., Zelaya, I., Teshiba, T.M., Bearden, C.E., Cantor, R.M., Reus, V.I., Macaya, G., López-Jaramillo, C. and Bogomolov, M., 2016. Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder. PLoS Genet, 12(5), p.e1006046.

 

Simultaneous modeling of disease status and clinical phenotypes to increase power in GWAS

Michael Bilow and Eleazar Eskin, together with Fernando Crespo, Zhicheng Pan, and Susana Eyheramendy, recently released a novel method for accurate joint modeling of clinical phenotype and disease status. This approach incorporates a clinical phenotype into case/control studies under the assumption that the genetic variant can affect both.

Genetic case-control association studies have found thousands of associations between genetic variants and disease. Most studies collect data from individuals with and without disease, and they often search for variants with different frequencies between the groups. Jointly modelling clinical phenotype and disease status is a promising way to increase power to detect true associations between genetics and disease. In particular, this method increases potential for discovering genetic variants that are associated with both a clinical phenotype and a disease.

However, standard multivariate techniques fail to effectively solve this problem because their case-control status is discrete and not continuous. Standard approaches to estimate model parameters are biased due to the ascertainment in case/control studies. We present a novel method that resolves both of these issues for simultaneous association testing of genetic variants that have both case status and a clinical covariate.

In our paper, we show the utility of our method using data from the North Finland Birth Cohort (NFBC) dataset. NFBC enrolled almost everyone born in 1966 in Finland’s two most northern provinces. The NFBC dataset consists of 10 phenotypes and genotypes at 331,476 genetic variants measured in 5,327 individuals. We focus our study on the LDL cholesterol and triglyceride levels phenotypes.

Our evaluation strategy analyzes a subset of the NFBC data and compares what we discover here to what was discovered in the full NFBC dataset—which we treat as the gold standard. We compare the performance of our novel approach to three other methods: (1) the single univariate test applied to the disease status, (2) the multivariate approach applied to the disease status and the clinical phenotype modeled as a multivariate normal distribution, and (3) the liability threshold model treating the clinical phenotype as a covariate.

Using the univariate approach, the p-values are much weaker in comparison to those observed in the full NFBC dataset. Running the multivariate approaches, incorporating the triglyceride levels phenotypes, increased power (i.e., more significant p-values than SNPs).

Our method has the highest power in all scenarios. The advantage of our method is greater when there are substantial amounts of selection bias compared to lower amounts of selection bias. Our method is even more powerful when the correlation between the clinical covariate and the disease liability is lower, because we explicitly estimate the underlying liability using all of the data.

For more information, see our paper in Genetics: http://www.genetics.org/content/early/2017/01/27/genetics.116.198473

The software implementing the methods described in this paper was developed by Fernando Crespo and is available at: http://genetics.cs.ucla.edu/multipheno/ and
https://github.com/facrespo/BivariateProbitContinueEM

An illustration of the distribution of liability in a case-control study under selection bias. For more information, read our paper.

The full citation to our paper is:
Bilow, M., Crespo, F., Pan, Z., Eskin, E. and Eyheramendy, S., 2017. Simultaneous Modeling of Disease Status and Clinical Phenotypes to Increase Power in GWAS. Genetics, pp.genetics-116.