Family and individual line selection for palmitate, saturates, linolenate and seed yield of soybean

Soybean [Glycine max (L.) Merr.] genotypes with reduced palmitate, stearate, and linolenate have been developed to improve the nutritional characteristics and oxidative stability of the seed oil. The reduction of palmitate and stearate in soybean is necessary to comply with U.S. Food and Drug Administration (FDA) regulations for vegetable oils that are labeled as being low in saturated fatty acids (U.S. FDA, 1994). The reduction of linolenate should improve the oxidative stability and reduce the formation of undesirable flavor compounds in the oil (Dutton et al., 1951; Smouse, 1979; Mounts et al., 1988; White and Miller, 1988);Plant-row-yield tests (PRYT) are used by soybean breeders for the initial yield evaluation of experimental lines. The highest yielding lines in the PRYT are advanced for evaluation in replicated tests. The objectives of this study were to compare the family and line methods of selection for reduced palmitate, palmitate + stearate (saturates), linolenate, and for increased seed yield, determine the influence of the combination of reduced palmitate and linolenate on agronomic and seed traits, and determine the effectiveness of selecting lines from unreplicated plots;Four random F3-derived lines from 21 F2 families from each of four populations were evaluated in a PRYT in 1995 and in replicated tests at four locations in 1996. For the family method, the mean palmitate, palmitate + stearate (saturates), linolenate, and seed yield of the four F3-derived lines of each F2 family was used to identify families from which to select individual lines. For the line method, lines were selected without regard to the family structure. The fatty ester contents or seed yield of the selected and unselected lines based on data from the PRYT were compared with their mean performance in the 1996 environments. Selection of lines based on data from one 1996 environment was compared with their mean performance in the other three environments. The total number of lines selected by the family method was less than for the line method for all traits in the four populations. The percentage of selected lines that were correctly classified for all traits was similar for both methods. There was a greater percentage of lines incorrectly rejected by the family method than by the line method for all traits. For development of cultivars with reduced palmitate, saturates, and linolenate, and with increased seed yield, breeding methods that rely on family performance would not be more effective or efficient than methods that ignore family structure. The evaluation of lines in unreplicated plots was useful for identifying lines to advance to replicated tests

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Analysis of shared heritability in common disorders of the brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology

Analysis of Shared Heritability in Common Disorders of the Brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology