A likelihood ratio approach for identifying three-quarter siblings in genetic databases

Pairwise kinship inference and pedigree reconstruction using 91 microhaplotypes

Kinship inference has been a major issue in forensic genetics, and it remains to be solved when there is no prior hypothesis and the relationships between multiple individuals are unknown. In this study, we genotyped 91 microhaplotypes from 46 pedigree samples using massive parallel sequencing and inferred their relatedness by calculating the likelihood ratio (LR). Based on simulated and real data, different treatments were applied in the presence and absence of relatedness assumptions. The pedigree of multiple individuals was reconstructed by calculating pedigree likelihoods based on real pedigree samples. The results showed that the 91 MHs could discriminate pairs of second-degree relatives from unrelated individuals. And more highly polymorphic loci were needed to discriminate the pairs of second-degree or more distant relative from other degrees of relationship, but correct classification could be obtained by expanding the suspected relationship searched to other relationships with lower LR values. Multiple individuals with unknown relationships can be successfully reconstructed if they are closely related. Our study provides a solution for kinship inference when there are no prior assumptions, and explores the possibility of pedigree reconstruction when the relationships of multiple individuals are unknown.

Analysis of Consanguinity as Risk Factor of Nonsyndromic Cleft Lips with or without Palate

OBJECTIVES

 The etiologies of nonsyndromic cleft lips with or without palate (NS CL/P) are multifactorial, which include consanguineous marriages. The incidence of NS CL/P is relatively high in Indonesia notably in one of Indonesia's tribes whose members frequently marry close cousins. Thus, the purpose of this study is to analysis consanguinity as risk factor of NS CL/P in Sasak tribe, East Lombok, Indonesia MATERIALS AND METHODS:  An observational analysis was made of a collected database of NS CL/P patients treated in social services in regency hospital of Dr. Soejono Selong, East Lombok, Indonesia. Demographic data such as age, gender, address (urban/rural), parent's education, presence or absence of consanguinity, type of clefts, and a three-generation pedigree were collected by interview and hospital medical record. Before analysis, patient information was anonymized and deidentified. From 2016 to 2018, each of 100 cleft and normal subjects with their Sasak parent were audited. The risk factors were analyzed statistically using odds ratio (OR) and chi-squared test.

RESULTS

 Consanguineous marriages identified 54 cases (54%), and 10 cases (10%) out of a total each 100 NS CL/P and controls, respectively. The majority of consanguinity (53.7%) was discovered in marriages between first cousins. NS CL/P cases were statistically linked (p = 0.00) with consanguineous marriages (OR: 10; 95% confidence interval: 1.6-3.1); in which the most prevalent case is unilateral cleft lips.

CONCLUSION

 Consanguineous marriage increases the risk of NS CL/P in Sasak tribe, East Lombok, Indonesia. The development of strategies to educate communities on the impacts of culture-consanguineous marriage is required. The genetic inheritance from their ancestor may be responsible for the increased incidence of NS CL/P.

A machine learning approach for missing persons cases with high genotyping errors

Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship.ip estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.

Major Depressive Disorder and Lifestyle: Correlated Genetic Effects in Extended Twin Pedigrees

In recent years, evidence has accumulated with regard to the ubiquity of pleiotropy across the genome, and shared genetic etiology is thought to play a large role in the widespread comorbidity among psychiatric disorders and risk factors. Recent methods investigate pleiotropy by estimating genetic correlation from genome-wide association summary statistics. More comprehensive estimates can be derived from the known relatedness between genetic relatives. Analysis of extended twin pedigree data allows for the estimation of genetic correlation for additive and non-additive genetic effects, as well as a shared household effect. Here we conduct a series of bivariate genetic analyses in extended twin pedigree data on lifetime major depressive disorder (MDD) and three indicators of lifestyle, namely smoking behavior, physical inactivity, and obesity, decomposing phenotypic variance and covariance into genetic and environmental components. We analyze lifetime MDD and lifestyle data in a large multigenerational dataset of 19,496 individuals by variance component analysis in the ‘Mendel’ software. We find genetic correlations for MDD and smoking behavior (r_G = 0.249), physical inactivity (r_G = 0.161), body-mass index (r_G = 0.081), and obesity (r_G = 0.155), which were primarily driven by additive genetic effects. These outcomes provide evidence in favor of a shared genetic etiology between MDD and the lifestyle factors.